research on memory model

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 24 September 2019

Focus on learning and memory

Nature Neuroscience volume  22 ,  page 1535 ( 2019 ) Cite this article

17k Accesses

3 Citations

24 Altmetric

Metrics details

In this special issue of Nature Neuroscience , we feature an assortment of reviews and perspectives that explore the topic of learning and memory.

Learning new information and skills, storing this knowledge, and retrieving, modifying or forgetting these memories over time are critical for flexibly responding to a changing environment. How these processes occur has fascinated philosophers, psychologists, and neuroscientists for generations, and the question continues to inspire research encompassing diverse approaches. In this special issue, Nature Neuroscience presents a collection of reviews and perspectives that reflects the breadth and vibrancy of this field. Many of these pieces touch on topics that have animated decades of investigation, including the roles of synaptic plasticity, adult neurogenesis, neuromodulation, and sleep in learning and memory. Yet recently developed technologies continue to provide novel insights in these areas, leading to the updated views presented here.

Synaptic plasticity, such as long-term potentiation and depression, remains the prevailing cellular model for learning and memory. While many presume that these processes are engaged by learning and mediate lasting changes in behavior, this link has yet to be conclusively demonstrated in vivo. Humeau and Choquet ( https://doi.org/10.1038/s41593-019-0480-6 ) outline the latest tools that can be used to visualize and manipulate synaptic activity and signaling in behaving animals, and they discuss further advances that are needed to help bridge this gap in our understanding.

Neuroscientists have also long been intrigued by the role that the formation of new neurons could play in memory formation and maintenance of new memories. Miller and Sahay ( https://doi.org/10.1038/s41593-019-0484-2 ) integrate recent research on adult hippocampal neurogenesis to present a model of how the maturation of adult-born dentate granule cells contributes to memory indexing and interference.

While the neural mechanisms underlying memory acquisition and consolidation are relatively well-described, less is known about how memories are retrieved. Frankland, Josselyn, and Köhler ( https://doi.org/10.1038/s41593-019-0493-1 ) discuss how recent approaches that enable the manipulation of memory-encoding neural ensembles (termed ‘engrams’) have informed our current understanding of retrieval. They highlight the ways in which retrieval success is influenced by retrieval cues and the congruence between encoding and retrieval states. They also discuss important open questions in the field.

External stimuli and internal states can affect various aspects of learning and memory, which is mediated in part by neuromodulatory systems. Likhtik and Johansen ( https://doi.org/10.1038/s41593-019-0503-3 ) detail how acetylcholine, noradrenaline, and dopamine systems participate in fear encoding and extinction. They discuss emergent themes, including how neuromodulation can act throughout the brain or in specifically targeted regions, how it can boost selected neural signals, and how it can tune oscillatory relationships between neural circuits.

The efficacy of memory storage is also influenced by sleep. Klinzing, Niethard, and Born ( https://doi.org/10.1038/s41593-019-0467-3 ) review evidence from rodent and human studies that implicates reactivation of memory ensembles (or ‘replay’), synaptic scaling, and oscillations during sleep in memory consolidation. They also discuss recent findings that suggest that the thalamus coordinates these processes.

Effective learning requires us to identify critical information and ignore extraneous details, all of which varies depending on the task at hand. Yael Niv ( https://doi.org/10.1038/s41593-019-0470-8 ) discusses computational and neural processes involved in the formation of such task representations, how factors such as attention and context affect these representations, and how we use task representations to make decisions.

The ability to issue appropriate outputs in response to neural activity is a critical brain function, and is often disrupted in injury and disease. Maryam Shanechi ( https://doi.org/10.1038/s41593-019-0488-y ) discusses how ‘closed-loop’ brain–machine interfaces (BMIs) have been used to monitor motor impulses and in turn control prosthetic or paralyzed limbs in order to restore function. Furthermore, she discusses how manipulation of BMI parameters can aid the study of learning. Finally, she explores how BMIs could be used in a similar vein to monitor and correct aberrant mood processes in psychiatric disorders.

By highlighting the topic of learning and memory, we honor its importance and centrality in neuroscience, while also celebrating the ways that other disciplines, including psychology, cellular and molecular biology, computer science, and engineering fuel insights in this area. We hope to continue to publish outstanding research in this area, particularly studies that resolve long-standing questions, that develop or leverage new methodologies, and that integrate multiple approaches.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Focus on learning and memory. Nat Neurosci 22 , 1535 (2019). https://doi.org/10.1038/s41593-019-0509-x

Download citation

Published : 24 September 2019

Issue Date : October 2019

DOI : https://doi.org/10.1038/s41593-019-0509-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• A-Z Publications

Annual Review of Psychology

Volume 63, 2012, review article, working memory: theories, models, and controversies.

• Alan Baddeley 1
• View Affiliations Hide Affiliations Affiliations: Department of Psychology, University of York, York YO10 5DD, United Kingdom; email: [email protected]
• Vol. 63:1-29 (Volume publication date January 2012) https://doi.org/10.1146/annurev-psych-120710-100422
• First published as a Review in Advance on September 27, 2011
• © Annual Reviews

I present an account of the origins and development of the multicomponent approach to working memory, making a distinction between the overall theoretical framework, which has remained relatively stable, and the attempts to build more specific models within this framework. I follow this with a brief discussion of alternative models and their relationship to the framework. I conclude with speculations on further developments and a comment on the value of attempting to apply models and theories beyond the laboratory studies on which they are typically based.

Associated Article

Article metrics loading...

Full text loading...

Literature Cited

• Allen R , Baddeley AD , Hitch GJ . 2006 . Is the binding of visual features in working memory resource-demanding?. J. Exp. Psychol.: Gen. 135 : 298– 313 [Google Scholar]
• Alloway TP , Gathercole SE , Pickering SJ . 2006 . Verbal and visuospatial short-term and working memory in children: Are they separable?. Child Dev. 77 : 1698– 716 [Google Scholar]
• Anderson JR , Bothell D , Byrne MD , Douglass S , Lebiere C , Qin Y . 2004 . An integrated theory of the mind. Psychol. Rev. 111 : 1036– 60 [Google Scholar]
• Anderson JR , Reder LM , Lebiere C . 1996 . Working memory: activation limitations on retrieval. Cogn. Psychol. 30 : 221– 56 [Google Scholar]
• Atkinson RC , Shiffrin RM . 1968 . Human memory: a proposed system and its control processes. The Psychology of Learning and Motivation: Advances in Research and Theory KW Spence, JT Spence 89– 195 New York: Academic [Google Scholar]
• Attneave F . 1960 . In defense of homunculi. Sensory Communication W Rosenblith 777– 82 Cambridge, MA: MIT Press [Google Scholar]
• Baars BJ . 1988 . A Cognitive Theory of Consciousness Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Baddeley AD . 1960 . Enhanced learning of a position habit with secondary reinforcement for the wrong response. Am. J. Psychol. 73 : 454– 57 [Google Scholar]
• Baddeley AD . 1966a . Short-term memory for word sequences as a function of acoustic, semantic and formal similarity. Q. J. Exp. Psychol. 18 : 362– 65 [Google Scholar]
• Baddeley AD . 1966b . The influence of acoustic and semantic similarity on long-term memory for word sequences. Q. J. Exp. Psychol. 18 : 302– 9 [Google Scholar]
• Baddeley AD . 1968 . How does acoustic similarity influence short-term memory?. Q. J. Exp. Psychol. 20 : 249– 64 [Google Scholar]
• Baddeley AD . 1976 . The Psychology of Memory New York: Basic Books [Google Scholar]
• Baddeley AD . 1986 . Working Memory Oxford, UK: Oxford Univ. Press [Google Scholar]
• Baddeley AD . 1996 . Exploring the central executive. Q. J. Exp. Psychol. A 49 : 5– 28 [Google Scholar]
• Baddeley AD . 2000 . The episodic buffer: a new component of working memory?. Trends. Cogn. Sci. 4 : 417– 23 [Google Scholar]
• Baddeley AD . 2007 . Working Memory, Thought and Action Oxford, UK: Oxford Univ. Press [Google Scholar]
• Baddeley AD , Allen RJ , Hitch GJ . 2011 . Binding in visual working memory: the role of the episodic buffer. Neuropsychologia 49 : 1393– 400 [Google Scholar]
• Baddeley AD , Allen RJ , Vargha-Khadem F . 2010 . Is the hippocampus necessary for visual and verbal binding in working memory?. Neuropsychologia 48 : 1089– 95 [Google Scholar]
• Baddeley AD , Andrade J . 2000 . Working memory and the vividness of imagery. J. Exp. Psychol.: Gen. 129 : 126– 45 [Google Scholar]
• Baddeley AD , Ecob JR . 1970 . Simultaneous acoustic and semantic coding in short-term memory. Nature 277 : 288– 89 [Google Scholar]
• Baddeley AD , Gathercole SE , Papagno C . 1998 . The phonological loop as a language learning device. Psychol. Rev. 105 : 158– 73 [Google Scholar]
• Baddeley AD , Grant W , Wight E , Thomson N . 1975a . Imagery and visual working memory. Attention and Performance V PMA Rabbitt, S Dornic 205– 17 London: Academic [Google Scholar]
• Baddeley AD , Hitch GJ . 1974 . Working memory. The Psychology of Learning and Motivation: Advances in Research and Theory GA Bower 47– 89 New York: Academic [Google Scholar]
• Baddeley AD , Hitch GJ . 1977 . Recency re-examined. Attention and Performance VI S Dornic 647– 67 Hillsdale, NJ: Erlbaum [Google Scholar]
• Baddeley AD , Hitch GJ , Allen RJ . 2009 . Working memory and binding in sentence recall. J. Mem. Lang. 61 : 438– 56 [Google Scholar]
• Baddeley AD , Larsen JD . 2007 . The phonological loop unmasked? A comment on the evidence for a “perceptual-gestural” alternative. Q. J. Exp. Psychol. 60 : 497– 504 [Google Scholar]
• Baddeley AD , Levy BA . 1971 . Semantic coding and short-term memory. J. Exp. Psychol. 89 : 132– 36 [Google Scholar]
• Baddeley AD , Lewis V , Vallar G . 1984 . Exploring the articulatory loop. Q. J. Exp. Psychol. A 36 : 233– 52 [Google Scholar]
• Baddeley AD , Lieberman K . 1980 . Spatial working memory. Attention and Performance VIII R Nickerson 521– 39 Hillsdale, NJ: Erlbaum [Google Scholar]
• Baddeley AD , Logie RH . 1999 . Working memory: the multiple component model. Models of Working Memory: Mechanisms of Active Maintenance and Executive Control A Miyake, P Shah 28– 61 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Baddeley AD , Papagno C , Vallar G . 1988 . When long-term learning depends on short-term storage. J. Mem. Lang. 27 : 586– 95 [Google Scholar]
• Baddeley AD , Thomson N , Buchanan M . 1975b . Word length and the structure of short-term memory. J. Verbal Learn. Verbal Behav. 14 : 575– 89 [Google Scholar]
• Baddeley AD , Vallar G , Wilson BA . 1987 . Sentence comprehension and phonological memory: some neuropsychological evidence. Attention and Performance XII: The Psychology of Reading M Coltheart 509– 29 London: Erlbaum [Google Scholar]
• Baddeley AD , Vargha-Khadem F , Mishkin M . 2001 . Preserved recognition in a case of developmental amnesia: implications for the acquisition of semantic memory. J. Cogn. Neurosci. 13 : 357– 69 [Google Scholar]
• Baddeley AD , Warrington EK . 1970 . Amnesia and the distinction between long- and short-term memory. J. Verbal Learn. Verbal Behav. 9 : 176– 89 [Google Scholar]
• Barnard PJ . 1985 . Interactive cognitive subsystems: a psycholinguistic approach to short-term memory. Progress in the Psychology of Language A Ellis 197– 258 London: Erlbaum [Google Scholar]
• Barnard PJ . 1987 . Cognitive resources and the learning of human-computer dialogs. Interfacing Thought: Cognitive Aspects of Human-Computer Interaction JM Carroll 112– 58 Cambridge, MA: MIT Press [Google Scholar]
• Barrouillet P , Bernardin S , Camos V . 2004 . Time constraints and resource sharing in adults' working memory spans. J. Exp. Psychol.: Gen. 133 : 83– 100 [Google Scholar]
• Bowman H . 2011 . Is there a future in architectural modeling of mind? Paper presented at Philip Barnard retirement symp., MRC Appl. Psychol. Unit, Cambridge, UK [Google Scholar]
• Braithwaite RB . 1953 . Scientific Explanation Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Brooks LR . 1968 . Spatial and verbal components in the act of recall. Can. J. Psychol. 22 : 349– 68 [Google Scholar]
• Brown GDA , Preece T , Hulme C . 2000 . Oscillator-based memory for serial order. Psychol. Rev. 107 : 127– 81 [Google Scholar]
• Burgess N , Hitch GJ . 1999 . Memory for serial order: a network model of the phonological loop and its timing. Psychol. Rev. 106 : 551– 81 [Google Scholar]
• Burgess N , Hitch GJ . 2006 . A revised model of short-term memory and long-term learning of verbal sequences. J. Mem. Lang. 55 : 627– 52 [Google Scholar]
• Cermak LS , Butters N , Moreines J . 1974 . Some analyses of the verbal encoding deficit of alcoholic Korsakoff patients. Brain Lang. 1 : 141– 50 [Google Scholar]
• Cermak LS , Reale L . 1978 . Depth of processing and retention of words by alcoholic Korsakoff patients. J. Exp. Psychol. Hum. Learn. 4 : 165– 74 [Google Scholar]
• Colle HA , Welsh A . 1976 . Acoustic masking in primary memory. J. Verbal Learn. Verbal Behav. 15 : 17– 32 [Google Scholar]
• Conrad R , Hull AJ . 1964 . Information, acoustic confusion and memory span. Br. J. Psychol. 55 : 429– 32 [Google Scholar]
• Cowan N . 1999 . An embedded-processes model of working memory. Models of Working Memory A Miyake, P Shah 62– 101 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Cowan N . 2005 . Working Memory Capacity. Hove, UK: Psychol. Press [Google Scholar]
• Cowan N , Day L , Saults JS , Keller TA , Johnson T , Flores L . 1992 . The role of verbal output time and the effects of word-length on immediate memory. J. Mem. Lang. 31 : 1– 17 [Google Scholar]
• Craik FIM , Lockhart RS . 1972 . Levels of processing: a framework for memory research. J. Verbal Learn. Verbal Behav. 11 : 671– 84 [Google Scholar]
• Dale HCA . 1973 . Short-term memory for visual information. Br. J. Psychol. 64 : 1– 8 [Google Scholar]
• Daneman M , Carpenter PA . 1980 . Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19 : 450– 66 [Google Scholar]
• Daneman M , Merikle PM . 1996 . Working memory and language comprehension: a meta-analysis. Psychonom. Bull. Rev. 3 : 422– 33 [Google Scholar]
• Della Sala S , Gray C , Baddeley A , Allamano N , Wilson L . 1999 . Pattern span: a tool for unwelding visuo-spatial memory. Neuropsychologia 37 : 1189– 99 [Google Scholar]
• Düzel E , Vargha-Khadem F , Heinze HJ , Mishkin M . 2001 . Brain activity evidence for recognition without recollection after early hippocampal damage. Proc. Natl. Acad. Sci. USA 98 : 8101– 6 [Google Scholar]
• Emerson MJ , Miyake A . 2003 . The role of inner speech in task switching: a dual-task investigation. J. Mem. Lang. 48 : 148– 68 [Google Scholar]
• Engle RW , Kane MJ . 2004 . Executive attention, working memory capacity and two-factor theory of cognitive control. The Psychology of Learning and Motivation B Ross 145– 99 New York: Elsevier [Google Scholar]
• Engle RW , Kane MJ , Tuholski SW . 1999 . Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. Models of Working Memory: Mechanisms of Active Maintenance and Executive Control A Miyake, P Shah 102– 34 New York: Cambridge Univ. Press [Google Scholar]
• Ericsson KA , Kintsch W . 1995 . Long-term working memory. Psychol. Rev. 102 : 211– 45 [Google Scholar]
• Fodor JA . 1983 . The Modularity of Mind Cambridge, MA: MIT Press [Google Scholar]
• Gathercole SE . 1995 . Is nonword repetition a test of phonological memory or long-term knowledge? It all depends on the nonwords. Mem. Cognit. 23 : 83– 94 [Google Scholar]
• Gathercole SE , Alloway TP . 2008 . Working Memory and Learning: A Practical Guide London: Sage [Google Scholar]
• Gathercole SE , Pickering SJ , Ambridge B , Wearing H . 2004a . The structure of working memory from 4 to 15 years of age. Dev. Psychol. 40 : 177– 90 [Google Scholar]
• Gathercole SE , Pickering SJ , Knight C , Stegmann Z . 2004b . Working memory skills and educational attainment: evidence from National Curriculum assessments at 7 and 14 years of age. Appl. Cogn. Psychol. 40 : 1– 16 [Google Scholar]
• Glanzer M . 1972 . Storage mechanisms in recall. The Psychology of Learning and Motivation: Advances in Research and Theory GH Bower 129– 93 New York: Academic [Google Scholar]
• Goldman-Rakic PW . 1988 . Topography of cognition: parallel distributed networks in primate association cortex. Annu. Rev. Neurosci. 11 : 137– 56 [Google Scholar]
• Hannula DE , Tranel D , Cohen NJ . 2006 . The long and the short of it: relational memory impairments in amnesia, even at short lags. J. Neurosci. 26 : 8352– 59 [Google Scholar]
• Henson R . 2001 . Neural working memory. Working Memory in Perspective J Andrade 151– 74 Hove, UK: Psychol. Press [Google Scholar]
• Hitch GJ , Flude B , Burgess N . 2009 . Slave to the rhythm: experimental tests of a model for verbal short-term memory and long-term sequence learning. J. Mem. Lang. 61 : 97– 111 [Google Scholar]
• Hurlstone MJ . 2010 . The problem of serial order in visuospatial short-term memory Unpubl. PhD dissert., Univ. York, UK [Google Scholar]
• Johnson MK , Reeder JA , Raye CL , Mitchell KJ . 2002 . Second thoughts versus second looks: an age-related deficit in reflectively refreshing just-activated information. Psychol. Sci. 13 : 64– 67 [Google Scholar]
• Jones D , Hughes RW , Macken WJ . 2006 . Perceptual organization masquerading as phonological storage: further support for a perceptual-gestural view of short-term memory. J. Mem. Lang. 54 : 265– 81 [Google Scholar]
• Jones DM . 1993 . Objects, streams and threads of auditory attention. Attention: Selection, Awareness and Control AD Baddeley, L Weiskrantz 87– 104 Oxford, UK: Clarendon [Google Scholar]
• Jones DM , Macken WJ . 1993 . Irrelevant tones produce an irrelevant speech effect: implications for phonological coding in working memory. J. Exp. Psychol.: Learn. Mem. Cogn. 19 : 369– 81 [Google Scholar]
• Jones DM , Macken WJ . 1995 . Phonological similarity in the irrelevant speech effect: within- or between-stream similarity?. J. Exp. Psychol.: Learn. Mem. Cogn. 21 : 103– 15 [Google Scholar]
• Jonides J , Lewis RL , Nee DE , Lustig CA , Berman MG , Moore KS . 2008 . The mind and brain of short-term memory. Annu. Rev. Psychol. 59 : 193– 224 [Google Scholar]
• Kaschel R , Logie RH , Kazén M , Della Sala S . 2009 . Alzheimer's disease, but not ageing or depression, affects dual-tasking. J. Neurol. 256 : 1860– 68 [Google Scholar]
• Klauer KC , Zhao Z . 2004 . Double dissociations in visual and spatial short-term memory. J. Exp. Psychol.: Gen. 133 : 355– 81 [Google Scholar]
• Kroll NE , Parks T , Parkinson SR , Bieber SL , Johnson AL . 1970 . Short-term memory while shadowing: recall of visually and aurally presented letters. J. Exp. Psychol. 85 : 220– 24 [Google Scholar]
• Lakatos I . 1976 . Proofs and Reputations Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Larsen J , Baddeley AD . 2003 . Disruption of verbal STM by irrelevant speech, articulatory suppression and manual tapping: Do they have a common source?. Q. J. Exp. Psychol. A 56 : 1249– 68 [Google Scholar]
• Larsen JD , Baddeley AD , Andrade J . 2000 . Phonological similarity and the irrelevant speech effect: implications for models of short-term verbal memory. Memory 8 : 145– 57 [Google Scholar]
• Lashley KS . 1951 . The problem of serial order in behavior. Cerebral Mechanisms in Behavior: The Hixon Symposium LA Jeffress 112– 36 New York: John Wiley [Google Scholar]
• Logie RH . 1986 . Visuo-spatial processing in working memory. Q. J. Exp. Psychol. 38A : 229– 47 [Google Scholar]
• Logie RH . 1995 . Visuo-Spatial Working Memory Hove, UK: Erlbaum [Google Scholar]
• Logie RH . 2011 . The functional organisation and the capacity limits of working memory. Curr. Dir. Psychol. Sci. 20 : 240– 45 [Google Scholar]
• Logie RH , Cocchini G , Della Sala S , Baddeley A . 2004 . Is there a specific capacity for dual task co-ordination? Evidence from Alzheimer's disease. Neuropsychology 18 : 504– 13 [Google Scholar]
• Logie RH , Della Sala S , Wynn V , Baddeley AD . 2000 . Visual similarity effects in immediate serial recall. Q. J. Exp. Psychol. 53A : 626– 46 [Google Scholar]
• Luck SJ , Vogel EK . 1997 . The capacity of visual working memory for features and conjunctions. Nature 390 : 279– 81 [Google Scholar]
• Luria AR . 1959 . The directive function of speech in development and dissolution, part I. Word 15 : 341– 52 [Google Scholar]
• Milner B . 1971 . Interhemispheric differences in the localization of psychological processes in man. Br. Med. Bull. 27 : 272– 77 [Google Scholar]
• Miyake A , Friedman NP , Emerson MJ , Witzki AH , Howenter A , Wager TD . 2000 . The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn. Psychol. 41 : 49– 100 [Google Scholar]
• Monsell S . 2005 . The chronometrics of task-set control. Measuring the Mind: Speed, Control, and Age J Duncan, L Phillips, P McLeod 161– 90 Oxford, UK: Oxford Univ. Press [Google Scholar]
• Nairne JS . 1990 . A feature model of immediate memory. Mem. Cognit. 18 : 251– 69 [Google Scholar]
• Nairne JS . 2002 . Remembering over the short-term: the case against the standard model. Annu. Rev. Psychol. 53 : 53– 81 [Google Scholar]
• Norman DA , Shallice T . 1986 . Attention to action: willed and automatic control of behaviour. Consciousness and Self-Regulation. Advances in Research and Theory RJ Davidson, GE Schwartz, D Shapiro 1– 18 New York: Plenum [Google Scholar]
• Oberauer K . 2010 . Design for a working memory. Psychol. Learn. Motiv. 51 : 45– 100 [Google Scholar]
• Olson IR , Moore KS , Stark M , Chatterjee A . 2006 . Visual working memory is impaired when the medial temporal lobe is damaged. J. Cogn. Neurosci. 18 : 1087– 97 [Google Scholar]
• Osgood CE . 1949 . The similarity paradox in human learning: a resolution. Psychol. Rev. 56 : 132– 43 [Google Scholar]
• Page MPA , Norris D . 1998 . The primacy model: a new model of immediate serial recall. Psychol. Rev. 105 : 761– 81 [Google Scholar]
• Page MPA , Norris DG . 2003 . The irrelevant sound effect: what needs modeling, and a tentative model. Q. J. Exp. Psychol. 56A : 1289– 300 [Google Scholar]
• Phillips WA . 1974 . On the distinction between sensory storage and short-term visual memory. Percept. Psychophys. 16 : 283– 90 [Google Scholar]
• Phillips WA , Baddeley AD . 1971 . Reaction time and short-term visual memory. Psychon. Sci. 22 : 73– 4 [Google Scholar]
• Popper K . 1959 . The Logic of Scientific Discovery London: Hutchison [Google Scholar]
• Posner MI , Keele SW . 1967 . Decay of visual information from a single letter. Science 158 : 137– 39 [Google Scholar]
• Posner MI , Konick AF . 1966 . Short-term retention of visual and kinesthetic information. Organ. Behav. Hum. Perform. 1 : 71– 86 [Google Scholar]
• Riby LM , Perfect TJ , Stollery B . 2004 . The effects of age and task domain on dual task performance: a meta-analysis. Eur. J. Cogn. Psychol. 16 : 863– 91 [Google Scholar]
• Robbins T , Henderson E , Barker D , Bradley A , Fearneyhough C . et al. 1996 . Working memory in chess. Mem. Cognit. 24 : 83– 93 [Google Scholar]
• Rönnberg J , Rudner M , Ingvar M . 2004 . Neural correlates of working memory for sign language. Cogn. Brain Res. 20 : 165– 82 [Google Scholar]
• Ruchkin DS , Grafman J , Cameron K , Berndt RS . 2003 . Working memory retention systems: a state of activated long-term memory. Behav. Brain Sci. 26 : 709– 77 [Google Scholar]
• Saito S , Miyake A . 2004 . On the nature of forgetting and the processing-storage relationship in reading span performance. J. Mem. Lang. 50 : 425– 43 [Google Scholar]
• Salame P , Baddeley AD . 1986 . Phonological factors in STM: similarity and the unattended speech effect. Bull. Psychonom. Soc. 24 : 263– 65 [Google Scholar]
• Shallice T . 1988 . From Neuropsychology to Mental Structure Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Shallice T . 2002 . Fractionation of the supervisory system. Principles of Frontal Lobe Function DT Stuss, RT Knight 261– 77 New York: Oxford Univ. Press [Google Scholar]
• Shallice T , Warrington EK . 1970 . Independent functioning of verbal memory stores: a neuropsychological study. Q. J. Exp. Psychol. 22 : 261– 73 [Google Scholar]
• Shrager Y , Levy DA , Hopkins RO , Squire LR . 2008 . Working memory and the organization of brain systems. J. Neurosci. 28 : 4818– 22 [Google Scholar]
• Smith EE , Jonides J . 1997 . Working memory: a view from neuroimaging. Cognit. Psychol. 33 : 5– 42 [Google Scholar]
• Smyth MM , Pendleton LR . 1990 . Space and movement in working memory. Q. J. Exp. Psychol. 42A : 291– 304 [Google Scholar]
• Squire LR . 2004 . Memory systems of the brain: a brief history and current perspective. Neurobiol. Learn. Mem. 82 : 171– 77 [Google Scholar]
• Teasdale JD , Barnard PJ . 1993 . Affect, Cognition and Change: Remodeling Depressive Thought Hove, UK: Erlbaum [Google Scholar]
• Toulmin S . 1953 . The Philosophy of Science London: Hutchison [Google Scholar]
• Towse JN , Hitch GJ , Hutton U . 2000 . On the interpretation of working memory span in adults. Mem. Cognit. 28 : 341– 48 [Google Scholar]
• Unsworth N , Engle RW . 2007 . The nature of individual differences in working memory capacity: active maintenance in primary memory and controlled search from secondary memory. Psychol. Rev. 114 : 104– 32 [Google Scholar]
• Vogel EK , Woodman GF , Luck SJ . 2001 . Storage of features, conjunctions, and objects in visual working memory. J. Exp. Psychol.: Hum. Percept. Perform. 27 : 92– 114 [Google Scholar]
• Waugh NC , Norman DA . 1965 . Primary memory. Psychol. Rev. 72 : 89– 104 [Google Scholar]
• Wheeler ME , Treisman AM . 2002 . Binding in short-term visual memory. J. Exp. Psychol.: Gen. 131 : 48– 64 [Google Scholar]
• Williamson V , Baddeley A , Hitch G . 2010 . Musicians' and nonmusicians' short-term memory for verbal and musical sequences: comparing phonological similarity and pitch proximity. Mem. Cognit. 38 : 163– 75 [Google Scholar]

Data & Media loading...

• Article Type: Review Article

Most Read This Month

Most cited most cited rss feed, job burnout, executive functions, social cognitive theory: an agentic perspective, on happiness and human potentials: a review of research on hedonic and eudaimonic well-being, sources of method bias in social science research and recommendations on how to control it, mediation analysis, missing data analysis: making it work in the real world, grounded cognition, personality structure: emergence of the five-factor model, motivational beliefs, values, and goals.

REVIEW article

Working memory from the psychological and neurosciences perspectives: a review.

• 1 Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
• 2 Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, Malaysia

Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory’s capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.

Introduction

Working memory has fascinated scholars since its inception in the 1960’s ( Baddeley, 2010 ; D’Esposito and Postle, 2015 ). Indeed, more than a century of scientific studies revolving around memory in the fields of psychology, biology, or neuroscience have not completely agreed upon a unified categorization of memory, especially in terms of its functions and mechanisms ( Cowan, 2005 , 2008 ; Baddeley, 2010 ). From the coining of the term “memory” in the 1880’s by Hermann Ebbinghaus, to the distinction made between primary and secondary memory by William James in 1890, and to the now widely accepted and used categorizations of memory that include: short-term, long-term, and working memories, studies that have tried to decode and understand this abstract concept called memory have been extensive ( Cowan, 2005 , 2008 ). Short and long-term memory suggest that the difference between the two lies in the period that the encoded information is retained. Other than that, long-term memory has been unanimously understood as a huge reserve of knowledge about past events, and its existence in a functioning human being is without dispute ( Cowan, 2008 ). Further categorizations of long-term memory include several categories: (1) episodic; (2) semantic; (3) Pavlovian; and (4) procedural memory ( Humphreys et al., 1989 ). For example, understanding and using language in reading and writing demonstrates long-term storage of semantics. Meanwhile, short-term memory was defined as temporarily accessible information that has a limited storage time ( Cowan, 2008 ). Holding a string of meaningless numbers in the mind for brief delays reflects this short-term component of memory. Thus, the concept of working memory that shares similarities with short-term memory but attempts to address the oversimplification of short-term memory by introducing the role of information manipulation has emerged ( Baddeley, 2012 ). This article seeks to present an up-to-date introductory overview of the realm of working memory by outlining several working memory studies from the psychological and neurosciences perspectives in an effort to refine and unite the scientific knowledge concerning working memory.

The Multicomponent Working Memory Model

When one describes working memory, the multicomponent working memory model is undeniably one of the most prominent working memory models that is widely cited in literatures ( Baars and Franklin, 2003 ; Cowan, 2005 ; Chein et al., 2011 ; Ashkenazi et al., 2013 ; D’Esposito and Postle, 2015 ; Kim et al., 2015 ). Baddeley and Hitch (1974) proposed a working memory model that revolutionized the rigid and dichotomous view of memory as being short or long-term, although the term “working memory” was first introduced by Miller et al. (1960) . The working memory model posited that as opposed to the simplistic functions of short-term memory in providing short-term storage of information, working memory is a multicomponent system that manipulates information storage for greater and more complex cognitive utility ( Baddeley and Hitch, 1974 ; Baddeley, 1996 , 2000b ). The three subcomponents involved are phonological loop (or the verbal working memory), visuospatial sketchpad (the visual-spatial working memory), and the central executive which involves the attentional control system ( Baddeley and Hitch, 1974 ; Baddeley, 2000b ). It was not until 2000 that another component termed “episodic buffer” was introduced into this working memory model ( Baddeley, 2000a ). Episodic buffer was regarded as a temporary storage system that modulates and integrates different sensory information ( Baddeley, 2000a ). In short, the central executive functions as the “control center” that oversees manipulation, recall, and processing of information (non-verbal or verbal) for meaningful functions such as decision-making, problem-solving or even manuscript writing. In Baddeley and Hitch (1974) ’s well-cited paper, information received during the engagement of working memory can also be transferred to long-term storage. Instead of seeing working memory as merely an extension and a useful version of short-term memory, it appears to be more closely related to activated long-term memory, as suggested by Cowan (2005 , 2008 ), who emphasized the role of attention in working memory; his conjectures were later supported by Baddeley (2010) . Following this, the current development of the multicomponent working memory model could be retrieved from Baddeley’s article titled “Working Memory” published in Current Biology , in Figure 2 ( Baddeley, 2010 ).

An Embedded-Processes Model of Working Memory

Notwithstanding the widespread use of the multicomponent working memory model, Cowan (1999 , 2005 ) proposed the embedded-processes model that highlights the roles of long-term memory and attention in facilitating working memory functioning. Arguing that the Baddeley and Hitch (1974) model simplified perceptual processing of information presentation to the working memory store without considering the focus of attention to the stimuli presented, Cowan (2005 , 2010 ) stressed the pivotal and central roles of working memory capacity for understanding the working memory concept. According to Cowan (2008) , working memory can be conceptualized as a short-term storage component with a capacity limit that is heavily dependent on attention and other central executive processes that make use of stored information or that interact with long-term memory. The relationships between short-term, long-term, and working memory could be presented in a hierarchical manner whereby in the domain of long-term memory, there exists an intermediate subset of activated long-term memory (also the short-term storage component) and working memory belongs to the subset of activated long-term memory that is being attended to ( Cowan, 1999 , 2008 ). An illustration of Cowan’s theoretical framework on working memory can be traced back to Figure 1 in his paper titled “What are the differences between long-term, short-term, and working memory?” published in Progress in Brain Research ( Cowan, 2008 ).

Alternative Models

Cowan’s theoretical framework toward working memory is consistent with Engle (2002) ’s view, in which it was posited that working memory capacity is comparable to directed or held attention information inhibition. Indeed, in their classic study on reading span and reading comprehension, Daneman and Carpenter (1980) demonstrated that working memory capacity, which was believed to be reflected by the reading span task, strongly correlated with various comprehension tests. Surely, recent and continual growth in the memory field has also demonstrated the development of other models such as the time-based resource-sharing model proposed by several researchers ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This model similarly demonstrated that cognitive load and working memory capacity that were so often discussed by working memory researchers were mainly a product of attention that one receives to allocate to tasks at hand ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). In fact, the allocated cognitive resources for a task (such as provided attention) and the duration of such allocation dictated the likelihood of success in performing the tasks ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This further highlighted the significance of working memory in comparison with short-term memory in that, although information retained during working memory is not as long-lasting as long-term memory, it is not the same and deviates from short-term memory for it involves higher-order processing and executive cognitive controls that are not observed in short-term memory. A more detailed presentation of other relevant working memory models that shared similar foundations with Cowan’s and emphasized the roles of long-term memory can be found in the review article by ( D’Esposito and Postle, 2015 ).

In addition, in order to understand and compare similarities and disparities in different proposed models, about 20 years ago, Miyake and Shah (1999) suggested theoretical questions to authors of different models in their book on working memory models. The answers to these questions and presentations of models by these authors gave rise to a comprehensive definition of working memory proposed by Miyake and Shah (1999 , p. 450), “working memory is those mechanisms or processes that are involved in the control, regulation, and active maintenance of task-relevant information in the service of complex cognition, including novel as well as familiar, skilled tasks. It consists of a set of processes and mechanisms and is not a fixed ‘place’ or ‘box’ in the cognitive architecture. It is not a completely unitary system in the sense that it involves multiple representational codes and/or different subsystems. Its capacity limits reflect multiple factors and may even be an emergent property of the multiple processes and mechanisms involved. Working memory is closely linked to LTM, and its contents consist primarily of currently activated LTM representations, but can also extend to LTM representations that are closely linked to activated retrieval cues and, hence, can be quickly activated.” That said, in spite of the variability and differences that have been observed following the rapid expansion of working memory understanding and its range of models since the inception of the multicomponent working memory model, it is worth highlighting that the roles of executive processes involved in working memory are indisputable, irrespective of whether different components exist. Such notion is well-supported as Miyake and Shah, at the time of documenting the volume back in the 1990’s, similarly noted that the mechanisms of executive control were being heavily investigated and emphasized ( Miyake and Shah, 1999 ). In particular, several domains of working memory such as the focus of attention ( Cowan, 1999 , 2008 ), inhibitory controls ( Engle and Kane, 2004 ), maintenance, manipulation, and updating of information ( Baddeley, 2000a , 2010 ), capacity limits ( Cowan, 2005 ), and episodic buffer ( Baddeley, 2000a ) were executive processes that relied on executive control efficacy (see also Miyake and Shah, 1999 ; Barrouillet et al., 2004 ; D’Esposito and Postle, 2015 ).

The Neuroscience Perspective

Following such cognitive conceptualization of working memory developed more than four decades ago, numerous studies have intended to tackle this fascinating working memory using various means such as decoding its existence at the neuronal level and/or proposing different theoretical models in terms of neuronal activity or brain activation patterns. Table 1 offers the summarized findings of these literatures. From the cognitive neuroscientific standpoint, for example, the verbal and visual-spatial working memories were examined separately, and the distinction between the two forms was documented through studies of patients with overt impairment in short-term storage for different verbal or visual tasks ( Baddeley, 2000b ). Based on these findings, associations or dissociations with the different systems of working memory (such as phonological loops and visuospatial sketchpad) were then made ( Baddeley, 2000b ). It has been established that verbal and acoustic information activates Broca’s and Wernicke’s areas while visuospatial information is represented in the right hemisphere ( Baddeley, 2000b ). Not surprisingly, many supporting research studies have pointed to the fronto-parietal network involving the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (ACC), and the parietal cortex (PAR) as the working memory neural network ( Osaka et al., 2003 ; Owen et al., 2005 ; Chein et al., 2011 ; Kim et al., 2015 ). More precisely, the DLPFC has been largely implicated in tasks demanding executive control such as those requiring integration of information for decision-making ( Kim et al., 2015 ; Jimura et al., 2017 ), maintenance and manipulation/retrieval of stored information or relating to taxing loads (such as capacity limit) ( Osaka et al., 2003 ; Moore et al., 2013 ; Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ), and information updating ( Murty et al., 2011 ). Meanwhile, the ACC has been shown to act as an “attention controller” that evaluates the needs for adjustment and adaptation of received information based on task demands ( Osaka et al., 2003 ), and the PAR has been regarded as the “workspace” for sensory or perceptual processing ( Owen et al., 2005 ; Andersen and Cui, 2009 ). Figure 1 attempted to translate the theoretical formulation of the multicomponent working memory model ( Baddeley, 2010 ) to specific regions in the human brain. It is, however, to be acknowledged that the current neuroscientific understanding on working memory adopted that working memory, like other cognitive systems, involves the functional integration of the brain as a whole; and to clearly delineate its roles into multiple components with only a few regions serving as specific buffers was deemed impractical ( D’Esposito and Postle, 2015 ). Nonetheless, depicting the multicomponent working memory model in the brain offers a glimpse into the functional segregation of working memory.

TABLE 1. Working memory (WM) studies in the healthy brain.

FIGURE 1. A simplified depiction (adapted from the multicomponent working memory model by Baddeley, 2010 ) as implicated in the brain, in which the central executive assumes the role to exert control and oversee the manipulation of incoming information for intended execution. ACC, Anterior cingulate cortex.

Further investigation has recently revealed that other than the generally informed cortical structures involved in verbal working memory, basal ganglia, which lies in the subcortical layer, plays a role too ( Moore et al., 2013 ). Particularly, the caudate and thalamus were activated during task encoding, and the medial thalamus during the maintenance phase, while recorded activity in the fronto-parietal network, which includes the DLPFC and the parietal lobules, was observed only during retrieval ( Moore et al., 2013 ). These findings support the notion that the basal ganglia functions to enhance focusing on a target while at the same time suppressing irrelevant distractors during verbal working memory tasks, which is especially crucial at the encoding phase ( Moore et al., 2013 ). Besides, a study conducted on mice yielded a similar conclusion in which the mediodorsal thalamus aided the medial prefrontal cortex in the maintenance of working memory ( Bolkan et al., 2017 ). In another study by Murty et al. (2011) in which information updating, which is one of the important aspects of working memory, was investigated, the midbrain including the substantia nigra/ventral tegmental area and caudate was activated together with DLPFC and other parietal regions. Taken together, these studies indicated that brain activation of working memory are not only limited to the cortical layer ( Murty et al., 2011 ; Moore et al., 2013 ). In fact, studies on cerebellar lesions subsequently discovered that patients suffered from impairments in attention-related working memory or executive functions, suggesting that in spite of the motor functions widely attributed to the cerebellum, the cerebellum is also involved in higher-order cognitive functions including working memory ( Gottwald et al., 2004 ; Ziemus et al., 2007 ).

Shifting the attention to the neuronal network involved in working memory, effective connectivity analysis during engagement of a working memory task reinforced the idea that the DLPFC, PAR and ACC belong to the working memory circuitry, and bidirectional endogenous connections between all these regions were observed in which the left and right PAR were the modeled input regions ( Dima et al., 2014 ) (refer to Supplementary Figure 1 in Dima et al., 2014 ). Effective connectivity describes the attempt to model causal influence of neuronal connections in order to better understand the hidden neuronal states underlying detected neuronal responses ( Friston et al., 2013 ). Another similar study of working memory using an effective connectivity analysis that involved more brain regions, including the bilateral middle frontal gyrus (MFG), ACC, inferior frontal cortex (IFC), and posterior parietal cortex (PPC) established the modulatory effect of working memory load in this fronto-parietal network with memory delay as the driving input to the bilateral PPC ( Ma et al., 2012 ) (refer to Figure 1 in Ma et al., 2012 ).

Moving away from brain regions activated but toward the in-depth neurobiological side of working memory, it has long been understood that the limited capacity of working memory and its transient nature, which are considered two of the defining characteristics of working memory, indicate the role of persistent neuronal firing (see Review Article by D’Esposito and Postle, 2015 ; Zylberberg and Strowbridge, 2017 ; see also Silvanto, 2017 ), that is, continuous action potentials are generated in neurons along the neural network. However, this view was challenged when activity-silent synaptic mechanisms were found to also be involved ( Mongillo et al., 2008 ; Rose et al., 2016 ; see also Silvanto, 2017 ). Instead of holding relevant information through heightened and persistent neuronal firing, residual calcium at the presynaptic terminals was suggested to have mediated the working memory process ( Mongillo et al., 2008 ). This synaptic theory was further supported when TMS application produced a reactivation effect of past information that was not needed or attended at the conscious level, hence the TMS application facilitated working memory efficacy ( Rose et al., 2016 ). As it happens, this provided evidence from the neurobiological viewpoint to support Cowan’s theorized idea of “activated long-term memory” being a feature of working memory as non-cued past items in working memory that were assumed to be no longer accessible were actually stored in a latent state and could be brought back into consciousness. However, the researchers cautioned the use of the term “activated long-term memory” and opted for “prioritized long-term memory” because these unattended items maintained in working memory seemed to employ a different mechanism than items that were dropped from working memory ( Rose et al., 2016 ). Other than the synaptic theory, the spiking working memory model proposed by Fiebig and Lansner (2017) that borrowed the concept from fast Hebbian plasticity similarly disagreed with persistent neuronal activity and demonstrated that working memory processes were instead manifested in discrete oscillatory bursts.

Age and Working Memory

Nevertheless, having established a clear working memory circuitry in the brain, differences in brain activations, neural patterns or working memory performances are still apparent in different study groups, especially in those with diseased or aging brains. For a start, it is well understood that working memory declines with age ( Hedden and Gabrieli, 2004 ; Ziaei et al., 2017 ). Hence, older participants are expected to perform poorer on a working memory task when making comparison with relatively younger task takers. In fact, it was reported that decreases in cortical surface area in the frontal lobe of the right hemisphere was associated with poorer performers ( Nissim et al., 2017 ). In their study, healthy (those without mild cognitive impairments [MCI] or neurodegenerative diseases such as dementia or Alzheimer’s) elderly people with an average age of 70 took the n-back working memory task while magnetic resonance imaging (MRI) scans were obtained from them ( Nissim et al., 2017 ). The outcomes exhibited that a decrease in cortical surface areas in the superior frontal gyrus, pars opercularis of the inferior frontal gyrus, and medial orbital frontal gyrus that was lateralized to the right hemisphere, was significantly detected among low performers, implying an association between loss of brain structural integrity and working memory performance ( Nissim et al., 2017 ). There was no observed significant decline in cortical thickness of the studied brains, which is assumed to implicate neurodegenerative tissue loss ( Nissim et al., 2017 ).

Moreover, another extensive study that examined cognitive functions of participants across the lifespan using functional magnetic resonance imaging (fMRI) reported that the right lateralized fronto-parietal regions in addition to the ventromedial prefrontal cortex (VMPFC), posterior cingulate cortex, and left angular and middle frontal gyri (the default mode regions) in older adults showed reduced modulation of task difficulty, which was reflective of poorer task performance ( Rieck et al., 2017 ). In particular, older-age adults (55–69 years) exhibited diminished brain activations (positive modulation) as compared to middle-age adults (35–54 years) with increasing task difficulty, whereas lesser deactivation (negative modulation) was observed between the transition from younger adults (20–34 years) to middle-age adults ( Rieck et al., 2017 ). This provided insights on cognitive function differences during an individual’s lifespan at the neurobiological level, which hinted at the reduced ability or efficacy of the brain to modulate functional regions to increased difficulty as one grows old ( Rieck et al., 2017 ). As a matter of fact, such an opinion was in line with the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposed by Reuter-Lorenz and Cappell (2008) . The CRUNCH likewise agreed upon reduced neural efficiency in older adults and contended that age-associated cognitive decline brought over-activation as a compensatory mechanism; yet, a shift would occur as task loads increase and under-activation would then be expected because older adults with relatively lesser cognitive resources would max out their ‘cognitive reserve’ sooner than younger adults ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ).

In addition to those findings, emotional distractors presented during a working memory task were shown to alter or affect task performance in older adults ( Oren et al., 2017 ; Ziaei et al., 2017 ). Based on the study by Oren et al. (2017) who utilized the n-back task paired with emotional distractors with neutral or negative valence in the background, negative distractors with low load (such as 1-back) resulted in shorter response time (RT) in the older participants ( M age = 71.8), although their responses were not significantly more accurate when neutral distractors were shown. Also, lesser activations in the bilateral MFG, VMPFC, and left PAR were reported in the old-age group during negative low load condition. This finding subsequently demonstrated the results of emotional effects on working memory performance in older adults ( Oren et al., 2017 ). Further functional connectivity analyses revealed that the amygdala, the region well-known to be involved in emotional processing, was deactivated and displayed similar strength in functional connectivity regardless of emotional or load conditions in the old-age group ( Oren et al., 2017 ). This finding went in the opposite direction of that observed in the younger group in which the amygdala was strongly activated with less functional connections to the bilateral MFG and left PAR ( Oren et al., 2017 ). This might explain the shorter reported RT, which was an indication of improved working memory performance, during the emotional working memory task in the older adults as their amygdala activation was suppressed as compared to the younger adults ( Oren et al., 2017 ).

Interestingly, a contrasting neural connection outcome was reported in the study by Ziaei et al. (2017) in which differential functional networks relating to emotional working memory task were employed by the two studied groups: (1) younger ( M age = 22.6) and (2) older ( M age = 68.2) adults. In the study, emotional distractors with positive, neutral, and negative valence were presented during a visual working memory task and older adults were reported to adopt two distinct networks involving the VMPFC to encode and process positive and negative distractors while younger adults engaged only one neural pathway ( Ziaei et al., 2017 ). The role of amygdala engagement in processing only negative items in the younger adults, but both negative and positive distractors in the older adults, could be reflective of the older adults’ better ability at regulating negative emotions which might subsequently provide a better platform for monitoring working memory performance and efficacy as compared to their younger counterparts ( Ziaei et al., 2017 ). This study’s findings contradict those by Oren et al. (2017) in which the amygdala was found to play a bigger role in emotional working memory tasks among older participants as opposed to being suppressed as reported by Oren et al. (2017) . Nonetheless, after overlooking the underlying neural mechanism relating to emotional distractors, it was still agreed that effective emotional processing sustained working memory performance among older/elderly people ( Oren et al., 2017 ; Ziaei et al., 2017 ).

Aside from the interaction effect between emotion and aging on working memory, the impact of caffeine was also investigated among elders susceptible to age-related cognitive decline; and those reporting subtle cognitive deterioration 18-months after baseline measurement showed less marked effects of caffeine in the right hemisphere, unlike those with either intact cognitive ability or MCI ( Haller et al., 2017 ). It was concluded that while caffeine’s effects were more pronounced in MCI participants, elders in the early stages of cognitive decline displayed diminished sensitivity to caffeine after being tested with the n-back task during fMRI acquisition ( Haller et al., 2017 ). It is, however, to be noted that the working memory performance of those displaying minimal cognitive deterioration was maintained even though their brain imaging uncovered weaker brain activation in a more restricted area ( Haller et al., 2017 ). Of great interest, such results might present a useful brain-based marker that can be used to identify possible age-related cognitive decline.

Similar findings that demonstrated more pronounced effects of caffeine on elderly participants were reported in an older study, whereas older participants in the age range of 50–65 years old exhibited better working memory performance that offset the cognitive decline observed in those with no caffeine consumption, in addition to displaying shorter reaction times and better motor speeds than observed in those without caffeine ( Rees et al., 1999 ). Animal studies using mice showed replication of these results in mutated mice models of Alzheimer’s disease or older albino mice, both possibly due to the reported results of reduced amyloid production or brain-derived neurotrophic factor and tyrosine-kinase receptor. These mice performed significantly better after caffeine treatment in tasks that supposedly tapped into working memory or cognitive functions ( Arendash et al., 2006 ). Such direct effects of caffeine on working memory in relation to age was further supported by neuroimaging studies ( Haller et al., 2013 ; Klaassen et al., 2013 ). fMRI uncovered increased brain activation in regions or networks of working memory, including the fronto-parietal network or the prefrontal cortex in old-aged ( Haller et al., 2013 ) or middle-aged adults ( Klaassen et al., 2013 ), even though the behavioral measures of working memory did not differ. Taken together, these outcomes offered insight at the neurobiological level in which caffeine acts as a psychoactive agent that introduces changes and alters the aging brain’s biological environment that explicit behavioral testing might fail to capture due to performance maintenance ( Haller et al., 2013 , 2017 ; Klaassen et al., 2013 ).

With respect to physiological effects on cognitive functions (such as effects of caffeine on brain physiology), estradiol, the primary female sex hormone that regulates menstrual cycles, was found to also modulate working memory by engaging different brain activity patterns during different phases of the menstrual cycle ( Joseph et al., 2012 ). The late follicular (LF) phase of the menstrual cycle, characterized by high estradiol levels, was shown to recruit more of the right hemisphere that was associated with improved working memory performance than did the early follicular (EF) phase, which has lower estradiol levels although overall, the direct association between estradiol levels and working memory was inconclusive ( Joseph et al., 2012 ). The finding that estradiol levels modified brain recruitment patterns at the neurobiological level, which could indirectly affect working memory performance, presents implications that working memory impairment reported in post-menopausal women (older aged women) could indicate a link with estradiol loss ( Joseph et al., 2012 ). In 2000, post-menopausal women undergoing hormone replacement therapy, specifically estrogen, were found to have better working memory performance in comparison with women who took estrogen and progestin or women who did not receive the therapy ( Duff and Hampson, 2000 ). Yet, interestingly, a study by Janowsky et al. (2000) showed that testosterone supplementation counteracted age-related working memory decline in older males, but a similar effect was not detected in older females who were supplemented with estrogen. A relatively recent paper might have provided the explanation to such contradicting outcomes ( Schöning et al., 2007 ). As demonstrated in the study using fMRI, the nature of the task (such as verbal or visual-spatial) might have played a role as a higher level of testosterone (in males) correlated with activations of the left inferior parietal cortex, which was deemed a key region in spatial processing that subsequently brought on better performance in a mental-rotation task. In contrast, significant correlation between estradiol and other cortical activations in females in the midluteal phase, who had higher estradiol levels, did not result in better performance of the task compared to women in the EF phase or men ( Schöning et al., 2007 ). Nonetheless, it remains premature to conclude that age-related cognitive decline was a result of hormonal (estradiol or testosterone) fluctuations although hormones might have modulated the effect of aging on working memory.

Other than the presented interaction effects of age and emotions, caffeine, and hormones, other studies looked at working memory training in the older population in order to investigate working memory malleability in the aging brain. Findings of improved performance for the same working memory task after training were consistent across studies ( Dahlin et al., 2008 ; Borella et al., 2017 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ). Such positive results demonstrated effective training gains regardless of age difference that could even be maintained until 18 months later ( Dahlin et al., 2008 ) even though the transfer effects of such training to other working memory tasks need to be further elucidated as strong evidence of transfer with medium to large effect size is lacking ( Dahlin et al., 2008 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ; see also Karbach and Verhaeghen, 2014 ). The studies showcasing the effectiveness of working memory training presented a useful cognitive intervention that could partially stall or delay cognitive decline. Table 2 presents an overview of the age-related working memory studies.

TABLE 2. Working memory (WM) studies in relation to age.

The Diseased Brain and Working Memory

Age is not the only factor influencing working memory. In recent studies, working memory deficits in populations with mental or neurological disorders were also being investigated (see Table 3 ). Having identified that the working memory circuitry involves the fronto-parietal region, especially the prefrontal and parietal cortices, in a healthy functioning brain, targeting these areas in order to understand how working memory is affected in a diseased brain might provide an explanation for the underlying deficits observed at the behavioral level. For example, it was found that individuals with generalized or social anxiety disorder exhibited reduced DLPFC activation that translated to poorer n-back task performance in terms of accuracy and RT when compared with the controls ( Balderston et al., 2017 ). Also, VMPFC and ACC, representing the default mode network (DMN), were less inhibited in these individuals, indicating that cognitive resources might have been divided and resulted in working memory deficits due to the failure to disengage attention from persistent anxiety-related thoughts ( Balderston et al., 2017 ). Similar speculation can be made about individuals with schizophrenia. Observed working memory deficits might be traced back to impairments in the neural networks that govern attentional-control and information manipulation and maintenance ( Grot et al., 2017 ). The participants performed a working memory binding task, whereby they had to make sure that the word-ellipse pairs presented during the retrieval phase were identical to those in the encoding phase in terms of location and verbal information; results concluded that participants with schizophrenia had an overall poorer performance compared to healthy controls when they were asked to actively bind verbal and spatial information ( Grot et al., 2017 ). This was reflected in the diminished activation in the schizophrenia group’s ventrolateral prefrontal cortex and the PPC that were said to play a role in manipulation and reorganization of information during encoding and maintenance of information after encoding ( Grot et al., 2017 ).

TABLE 3. Working memory (WM) studies in the diseased brain.

In addition, patients with major depressive disorder (MDD) displayed weaker performance in the working memory updating domain in which information manipulation was needed when completing a visual working memory task ( Le et al., 2017 ). The working memory task employed in the study was a delayed recognition task that required participants to remember and recognize the faces or scenes as informed after stimuli presentation while undergoing fMRI scan ( Le et al., 2017 ). Subsequent functional connectivity analyses revealed that the fusiform face area (FFA), parahippocampal place area (PPA), and left MFG showed aberrant activity in the MDD group as compared to the control group ( Le et al., 2017 ). These brain regions are known to be the visual association area and the control center of working memory and have been implicated in visual working memory updating in healthy adults ( Le et al., 2017 ). Therefore, altered visual cortical functions and load-related activation in the prefrontal cortex in the MDD group implied that the cognitive control for visual information processing and updating might be impaired at the input or control level, which could have ultimately played a part in the depressive symptoms ( Le et al., 2017 ).

Similarly, during a verbal delayed match to sample task that asked participants to sub-articulatorly rehearse presented target letters for subsequent letter-matching, individuals with bipolar affective disorder displayed aberrant neural interactions between the right amygdala, which is part of the limbic system implicated in emotional processing as previously described, and ipsilateral cortical regions often concerned with verbal working memory, pointing out that the cortico-amygdalar connectivity was disrupted, which led to verbal working memory deficits ( Stegmayer et al., 2015 ). As an attempt to gather insights into previously reported hyperactivation in the amygdala in bipolar affective disorder during an articulatory working memory task, functional connectivity analyses revealed that negative functional interactions seen in healthy controls were not replicated in patients with bipolar affective disorder ( Stegmayer et al., 2015 ). Consistent with the previously described study about emotional processing effects on working memory in older adults, this reported outcome was suggestive of the brain’s failed attempts to suppress pathological amygdalar activation during a verbal working memory task ( Stegmayer et al., 2015 ).

Another affected group with working memory deficits that has been the subject of research interest was children with developmental disorders such as attention deficit/hyperactivity disorder (ADHD), developmental dyscalculia, and reading difficulties ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ; Wang and Gathercole, 2013 ; Maehler and Schuchardt, 2016 ). For instance, looking into the different working memory subsystems based on Baddeley’s multicomponent working memory model in children with dyslexia and/or ADHD and children with dyscalculia and/or ADHD through a series of tests, it was reported that distinctive working memory deficits by groups could be detected such that phonological loop (e.g., digit span) impairment was observed in the dyslexia group, visuospatial sketchpad (e.g., Corsi block tasks) deficits in the dyscalculia group, while central executive (e.g., complex counting span) deficits in children with ADHD ( Maehler and Schuchardt, 2016 ). Meanwhile, examination of working memory impairment in a delayed match-to-sample visual task that put emphasis on the maintenance phase of working memory by examining the brainwaves of adults with ADHD using electroencephalography (EEG) also revealed a marginally significantly lower alpha band power in the posterior regions as compared to healthy individuals, and such an observation was not significantly improved after working memory training (Cogmed working memory training, CWMT Program) ( Liu et al., 2016 ). The alpha power was considered important in the maintenance of working memory items; and lower working memory accuracy paired with lower alpha band power was indeed observed in the ADHD group ( Liu et al., 2016 ).

Not dismissing the above compiled results, children encountering disabilities in mathematical operations likewise indicated deficits in the working memory domain that were traceable to unusual brain activities at the neurobiological level ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). It was speculated that visuospatial working memory plays a vital role when arithmetic problem-solving is involved in order to ensure intact mental representations of the numerical information ( Rotzer et al., 2009 ). Indeed, Ashkenazi et al. (2013) revealed that Block Recall, a variant of the Corsi Block Tapping test and a subtest of the Working Memory Test Battery for Children (WMTB-C) that explored visuospatial sketchpad ability, was significantly predictive of math abilities. In relation to this, studies investigating brain activation patterns and performance of visuospatial working memory task in children with mathematical disabilities identified the intraparietal sulcus (IPS), in conjunction with other regions in the prefrontal and parietal cortices, to have less activation when visuospatial working memory was deemed involved (during an adapted form of Corsi Block Tapping test made suitable for fMRI [ Rotzer et al., 2009 ]); in contrast the control group demonstrated correlations of the IPS in addition to the fronto-parietal cortical activation with the task ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). These brain activity variations that translated to differences in overt performances between healthily developing individuals and those with atypical development highlighted the need for intervention and attention for the disadvantaged groups.

Traumatic Brain Injury and Working Memory

Physical injuries impacting the frontal or parietal lobes would reasonably be damaging to one’s working memory. This is supported in studies employing neuropsychological testing to assess cognitive impairments in patients with traumatic brain injury; and poorer cognitive performances especially involving the working memory domains were reported (see Review Articles by Dikmen et al., 2009 ; Dunning et al., 2016 ; Phillips et al., 2017 ). Research on cognitive deficits in traumatic brain injury has been extensive due to the debilitating conditions brought upon an individual daily life after the injury. Traumatic brain injuries (TBI) refer to accidental damage to the brain after being hit by an object or following rapid acceleration or deceleration ( Farrer, 2017 ). These accidents include falls, assaults, or automobile accidents and patients with TBI can be then categorized into three groups; (1) mild TBI with GCS – Glasgow Coma Scale – score of 13–15; (2) moderate TBI with GCS score of 9–12; and (3) severe TBI with GCS score of 3–8 ( Farrer, 2017 ). In a recently published meta-analysis that specifically looked at working memory impairments in patients with moderate to severe TBI, patients displayed reduced cognitive functions in verbal short-term memory in addition to verbal and visuospatial working memory in comparison to control groups ( Dunning et al., 2016 ). It was also understood from the analysis that the time lapse since injury and age of injury were deciding factors that influenced these cognitive deficits in which longer time post-injury or older age during injury were associated with greater cognitive decline ( Dunning et al., 2016 ).

Nonetheless, it is to be noted that such findings relating to age of injury could not be generalized to the child population since results from the pediatric TBI cases showed that damage could negatively impact developmental skills that could indicate a greater lag in cognitive competency as the child’s frontal lobe had yet to mature ( Anderson and Catroppa, 2007 ; Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). These studies all reported working memory impairment of different domains such as attentional control, executive functions, or verbal and visuospatial working memory in the TBI group, especially for children with severe TBI ( Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). Investigation of whether working memory deficits are domain-specific or -general or involve one or more mechanisms, has yielded inconsistent results. For example, Perlstein et al. (2004) found that working memory was impaired in the TBI group only when complex manipulation such as sequential coding of information is required and not accounted for by processing speed or maintenance of information, but two teams of researchers ( Perbal et al., 2003 ; Gorman et al., 2012 ) suggested otherwise. From their study on timing judgments, Perbal et al. (2003) concluded that deficits were not related to time estimation but more on generalized attentional control, working memory and processing speed problems; while Gorman et al. (2012) also attributed the lack of attentional focus to impairments observed during the working memory task. In fact, in a later study by Gorman et al. (2016) , it was shown that processing speed mediated TBI effects on working memory even though the mediation was partial. On the other hand, Vallat-Azouvi et al. (2007) reported impairments in the working memory updating domain that came with high executive demands for TBI patients. Also, Mandalis et al. (2007) similarly highlighted potential problems with attention and taxing cognitive demands in the TBI group.

From the neuroscientific perspective, hyper-activation or -connectivity in the working memory circuitry was reported in TBI patients in comparison with healthy controls when both groups engaged in working memory tasks, suggesting that the brain attempted to compensate for or re-establish lost connections upon the injury ( Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ). For a start, it was observed that participants with mild TBI displayed increased activation in the right prefrontal cortex during a working memory task when comparing to controls ( Wylie et al., 2015 ). Interestingly, this activation pattern only occurred in patients who did not experience a complete recovery 1 week after the injury ( Wylie et al., 2015 ). Besides, low activation in the DMN was observed in mild TBI patients without cognitive recovery, and such results seemed to be useful in predicting recovery in patients in which the patients did not recover when hypoactivation (low activation) was reported, and vice versa ( Wylie et al., 2015 ). This might be suggestive of the potential of cognitive recovery simply by looking at the intensity of brain activation of the DMN, for an increase in activation of the DMN seemed to be superseded before cognitive recovery was present ( Wylie et al., 2015 ).

In fact, several studies lent support to the speculation mentioned above as hyperactivation or hypoactivation in comparison with healthy participants was similarly identified. When sex differences were being examined in working memory functional activity in mild TBI patients, hyperactivation was reported in male patients when comparing to the male control group, suggesting that the hyperactivation pattern might be the brain’s attempt at recovering impaired functions; even though hypoactivation was shown in female patients as compared to the female control group ( Hsu et al., 2015 ). The researchers from the study further explained that such hyperactivation after the trauma acted as a neural compensatory mechanism so that task performance could be maintained while hypoactivation with a poorer performance could have been the result of a more severe injury ( Hsu et al., 2015 ). Therefore, the decrease in activation in female patients, in addition to the observed worse performance, was speculated to be due to a more serious injury sustained by the female patients group ( Hsu et al., 2015 ).

In addition, investigation of the effective connectivity of moderate and severe TBI participants during a working memory task revealed that the VMPFC influenced the ACC in these TBI participants when the opposite was observed in healthy subjects ( Dobryakova et al., 2015 ). Moreover, increased inter-hemispheric transfer due to an increased number of connections between the left and right hemispheres (hyper-connectivity) without clear directionality of information flow (redundant connectivity) was also reported in the TBI participants ( Dobryakova et al., 2015 ). This study was suggestive of location-specific changes in the neural network connectivity following TBI depending on the cognitive functions at work, other than providing another support to the neural compensatory hypothesis due to the observed hyper-connectivity ( Dobryakova et al., 2015 ).

Nevertheless, inconsistent findings should not be neglected. In a study that also focused on brain connectivity analysis among patients with mild TBI by Hillary et al. (2011) , elevated task-related connectivity in the right hemisphere, in particular the prefrontal cortex, was consistently demonstrated during a working memory task while the control group showed greater left hemispheric activation. This further supported the right lateralization of the brain to reallocate cognitive resources of TBI patients post-injury. Meanwhile, the study did not manage to obtain the expected outcome in terms of greater clustering of whole-brain connections in TBI participants as hypothesized ( Hillary et al., 2011 ). That said, no significant loss or gain of connections due to the injury could be concluded from the study, as opposed to the hyper- or hypoactivation or hyper-connectivity frequently highlighted in other similar researches ( Hillary et al., 2011 ). Furthermore, a study by Chen et al. (2012) also failed to establish the same results of increased brain activation. Instead, with every increase of the working memory load, increase in brain activation, as expected to occur and as demonstrated in the control group, was unable to be detected in the TBI group ( Chen et al., 2012 ).

Taken all the insightful studies together, another aspect not to be neglected is the neuroimaging techniques employed in contributing to the literature on TBI. Modalities other than fMRI, which focuses on localization of brain activities, show other sides of the story of working memory impairments in TBI to offer a more holistic understanding. Studies adopting electroencephalography (EEG) or diffusor tensor imaging (DTI) reported atypical brainwaves coherence or white matter integrity in patients with TBI ( Treble et al., 2013 ; Ellis et al., 2016 ; Bailey et al., 2017 ; Owens et al., 2017 ). Investigating the supero-lateral medial forebrain bundle (MFB) that innervates and consequently terminates at the prefrontal cortex, microstructural white matter damage at the said area was indicated in participants with moderate to severe TBI by comparing its integrity with the control group ( Owens et al., 2017 ). Such observation was backed up by evidence showing that the patients performed more poorly on attention-loaded cognitive tasks of factors relating to slow processing speed than the healthy participants, although a direct association between MFB and impaired attentional system was not found ( Owens et al., 2017 ).

Correspondingly, DTI study of the corpus callosum (CC), which described to hold a vital role in connecting and coordinating both hemispheres to ensure competent cognitive functions, also found compromised microstructure of the CC with low fractional anisotropy and high mean diffusivity, both of which are indications of reduced white matter integrity ( Treble et al., 2013 ). This reported observation was also found to be predictive of poorer verbal or visuospatial working memory performance in callosal subregions connecting the parietal and temporal cortices ( Treble et al., 2013 ). Adding on to these results, using EEG to examine the functional consequences of CC damage revealed that interhemispheric transfer time (IHTT) of the CC was slower in the TBI group than the control group, suggesting an inefficient communication between the two hemispheres ( Ellis et al., 2016 ). In addition, the TBI group with slow IHTT as well exhibited poorer neurocognitive functioning including working memory than the healthy controls ( Ellis et al., 2016 ).

Furthermore, comparing the working memory between TBI, MDD, TBI-MDD, and healthy participants discovered that groups with MDD and TBI-MDD performed poorer on the Sternberg working memory task but functional connectivity on the other hand, showed that increased inter-hemispheric working memory gamma connectivity was observed in the TBI and TBI-MDD groups ( Bailey et al., 2017 ). Speculation provided for the findings of such neuronal state that was not reflected in the explicit working memory performance was that the deficits might not be detected or tested by the utilized Sternberg task ( Bailey et al., 2017 ). Another explanation attempting to answer the increase in gamma connectivity in these groups was the involvement of the neural compensatory mechanism after TBI to improve performance ( Bailey et al., 2017 ). Nevertheless, such outcome implies that behavioral performances or neuropsychological outcomes might not always be reflective of the functional changes happening in the brain.

Yet, bearing in mind that TBI consequences can be vast and crippling, cognitive improvement or recovery, though complicated due to the injury severity-dependent nature, is not impossible (see Review Article by Anderson and Catroppa, 2007 ; Nadebaum et al., 2007 ; Dikmen et al., 2009 ; Chen et al., 2012 ). As reported by Wylie et al. (2015) , cognitive improvement together with functional changes in the brain could be detected in individuals with mild TBI. Increased activation in the brain during 6-week follow-up was also observed in the mild TBI participants, implicating the regaining of connections in the brain ( Chen et al., 2012 ). Administration of certain cognitively enhancing drugs such as methylphenidate was reported to be helpful in improving working memory performance too ( Manktelow et al., 2017 ). Methylphenidate as a dopamine reuptake inhibitor was found to have modulated the neural activity in the left cerebellum which subsequently correlated with improved working memory performance ( Manktelow et al., 2017 ). A simplified summary of recent studies on working memory and TBI is tabulated in Table 4 .

TABLE 4. Working memory (WM) studies in the TBI group.

General Discussion and Future Direction

In practice, all of the aforementioned studies contribute to the working memory puzzle by addressing the topic from different perspectives and employing various methodologies to study it. Several theoretical models of working memory that conceptualized different working memory mechanisms or domains (such as focus of attention, inhibitory controls, maintenance and manipulation of information, updating and integration of information, capacity limits, evaluative and executive controls, and episodic buffer) have been proposed. Coupled with the working memory tasks of various means that cover a broad range (such as Sternberg task, n-back task, Corsi block-tapping test, Wechsler’s Memory Scale [WMS], and working memory subtests in the Wechsler Adult Intelligence Scale [WAIS] – Digit Span, Letter Number Sequencing), it has been difficult, if not highly improbable, for working memory studies to reach an agreement upon a consistent study protocol that is acceptable for generalization of results due to the constraints bound by the nature of the study. Various data acquisition and neuroimaging techniques that come with inconsistent validity such as paper-and-pen neuropsychological measures, fMRI, EEG, DTI, and functional near-infrared spectroscopy (fNIRS), or even animal studies can also be added to the list. This poses further challenges to quantitatively measure working memory as only a single entity. For example, when studying the neural patterns of working memory based on Cowan’s processes-embedded model using fMRI, one has to ensure that the working memory task selected is fMRI-compatible, and demands executive control of attention directed at activated long-term memory (domain-specific). That said, on the one hand, there are tasks that rely heavily on the information maintenance such as the Sternberg task; on the other hand, there are also tasks that look into the information manipulation updating such as the n-back or arithmetic task. Meanwhile, the digit span task in WAIS investigates working memory capacity, although it can be argued that it also encompasses the domain on information maintenance and updating-. Another consideration involves the different natures (verbal/phonological and visuospatial) of the working memory tasks as verbal or visuospatial information is believed to engage differing sensory mechanisms that might influence comparison of working memory performance between tasks of different nature ( Baddeley and Hitch, 1974 ; Cowan, 1999 ). For instance, though both are n-back tasks that includes the same working memory domains, the auditory n-back differs than the visual n-back as the information is presented in different forms. This feature is especially crucial with regards to the study populations as it differentiates between verbal and visuospatial working memory competence within individuals, which are assumed to be domain-specific as demonstrated by vast studies (such as Nadler and Archibald, 2014 ; Pham and Hasson, 2014 ; Nakagawa et al., 2016 ). These test variations undeniably present further difficulties in selecting an appropriate task. Nevertheless, the adoption of different modalities yielded diverging outcomes and knowledge such as behavioral performances, functional segregation and integration in the brain, white matter integrity, brainwave coherence, and oxy- and deoxyhaemoglobin concentrations that are undeniably useful in application to different fields of study.

In theory, the neural efficiency hypothesis explains that increased efficiency of the neural processes recruit fewer cerebral resources in addition to displaying lower activation in the involved neural network ( Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ). This is in contrast with the neural compensatory hypothesis in which it attempted to understand diminished activation that is generally reported in participants with TBI ( Hillary et al., 2011 ; Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ; Bailey et al., 2017 ). In the diseased brain, low activation has often been associated with impaired cognitive function ( Chen et al., 2012 ; Dobryakova et al., 2015 ; Wylie et al., 2015 ). Opportunely, the CRUNCH model proposed within the field of aging might be translated and integrated the two hypotheses here as it suitably resolved the disparity of cerebral hypo- and hyper-activation observed in weaker, less efficient brains as compared to healthy, adept brains ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ). Moreover, other factors such as the relationship between fluid intelligence and working memory might complicate the current understanding of working memory as a single, isolated construct since working memory is often implied in measurements of the intelligence quotient ( Cowan, 2008 ; Vartanian et al., 2013 ). Indeed, the process overlap theory of intelligence proposed by Kovacs and Conway (2016) in which the constructs of intelligence were heavily scrutinized (such as general intelligence factors, g and its smaller counterparts, fluid intelligence or reasoning, crystallized intelligence, perceptual speed, and visual-spatial ability), and fittingly connected working memory capacity with fluid reasoning. Cognitive tests such as Raven’s Progressive Matrices or other similar intelligence tests that demand complex cognition and were reported in the paper had been found to correlate strongly with tests of working memory ( Kovacs and Conway, 2016 ). Furthermore, in accordance with such views, in the same paper, neuroimaging studies found intelligence tests also activated the same fronto-parietal network observed in working memory ( Kovacs and Conway, 2016 ).

On the other hand, even though the roles of the prefrontal cortex in working memory have been widely established, region specificity and localization in the prefrontal cortex in relation to the different working memory domains such as manipulation or delayed retention of information remain at the premature stage (see Review Article by D’Esposito and Postle, 2015 ). It has been postulated that the neural mechanisms involved in working memory are of high-dimensionality and could not always be directly captured and investigated using neurophysiological techniques such as fMRI, EEG, or patch clamp recordings even when comparing with lesion data ( D’Esposito and Postle, 2015 ). According to D’Esposito and Postle (2015) , human fMRI studies have demonstrated that a rostral-caudal functional gradient related to level of abstraction required of working memory along the frontal cortex (in which different regions in the prefrontal cortex [from rostral to caudal] might be associated with different abstraction levels) might exist. Other functional gradients relating to different aspects of working memory were similarly unraveled ( D’Esposito and Postle, 2015 ). These proposed mechanisms with different empirical evidence point to the fact that conclusive understanding regarding working memory could not yet be achieved before the inconsistent views are reconciled.

Not surprisingly, with so many aspects of working memory yet to be understood and its growing complexity, the cognitive neuroscience basis of working memory requires constant research before an exhaustive account can be gathered. From the psychological conceptualization of working memory as attempted in the multicomponent working memory model ( Baddeley and Hitch, 1974 ), to the neural representations of working memory in the brain, especially in the frontal regions ( D’Esposito and Postle, 2015 ), one important implication derives from the present review of the literatures is that working memory as a psychological construct or a neuroscientific mechanism cannot be investigated as an isolated event. The need for psychology and neuroscience to interact with each other in an active feedback cycle exists in which this cognitive system called working memory can be dissected at the biological level and refined both empirically, and theoretically.

In summary, the present article offers an account of working memory from the psychological and neuroscientific perspectives, in which theoretical models of working memory are presented, and neural patterns and brain regions engaging in working memory are discussed among healthy and diseased brains. It is believed that working memory lays the foundation for many other cognitive controls in humans, and decoding the working memory mechanisms would be the first step in facilitating understanding toward other aspects of human cognition such as perceptual or emotional processing. Subsequently, the interactions between working memory and other cognitive systems could reasonably be examined.

Author Contributions

WC wrote the manuscript with critical feedback and consultation from AAH. WC and AAH contributed to the final version of the manuscript. JA supervised the process and proofread the manuscript.

This work was supported by the Transdisciplinary Research Grant Scheme (TRGS) 203/CNEURO/6768003 and the USAINS Research Grant 2016.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer EB and handling Editor declared their shared affiliation.

Andersen, R. A., and Cui, H. (2009). Review intention, action planning, and decision making in parietal-frontal circuits. Neuron 63, 568–583. doi: 10.1016/j.neuron.2009.08.028

PubMed Abstract | CrossRef Full Text | Google Scholar

Anderson, V., and Catroppa, C. (2007). Memory outcome at 5 years post-childhood traumatic brain injury. Brain Inj. 21, 1399–1409. doi: 10.1080/02699050701785070

Arendash, G. W., Schleif, W., Rezai-Zadeh, K., Jackson, E. K., Zacharia, L. C., Cracchiolo, J. R., et al. (2006). Caffeine protects Alzheimer’s mice against cognitive impairment and reduces brain β-amyloid production. Neuroscience 142, 941–952. doi: 10.1016/j.neuroscience.2006.07.021

Ashkenazi, S., Rosenberg-lee, M., Metcalfe, A. W. S., Swigart, A. G., and Menon, V. (2013). Neuropsychologia visuo – spatial working memory is an important source of domain-general vulnerability in the development of arithmetic cognition. Neuropsychologia 51, 2305–2317. doi: 10.1016/j.neuropsychologia.2013.06.031

Baars, B. J., and Franklin, S. (2003). How conscious experience and working memory interact. Trends Cogn. Sci. 7, 166–172. doi: 10.1016/S1364-6613(03)00056-1

CrossRef Full Text | Google Scholar

Baddeley, A. (1996). Exploring the central executive. Q. J. Exp. Psychol. A 49, 5–28. doi: 10.1080/713755608

Baddeley, A. (2010). Working memory. Curr. Biol. 20, R136–R140. doi: 10.1016/j.cub.2009.12.014

Baddeley, A. (2012). Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29. doi: 10.1146/annurev-psych-120710-100422

Baddeley, A., and Hitch, G. (1974). Working memory. Psychol. Learn. Motiv. 8, 47–89. doi: 10.1016/j.cub.2009.12.014

Baddeley, A. D. (2000a). The episodic buffer : a new component of working memory? Trends Cogn. Sci. 4, 417–423. doi: 10.1016/S1364-6613(00)01538-2

Baddeley, A. D. (2000b). Short-Term and Working Memory. The Oxford Handbook of Memory. Oxford: Oxford University Press.

Google Scholar

Bailey, N. W., Rogasch, N. C., Hoy, K. E., Maller, J. J., Segrave, R. A., Sullivan, C. M., et al. (2017). Increased gamma connectivity during working memory retention following traumatic brain injury. Brain Inj. 31, 379–389. doi: 10.1080/02699052.2016.1239273

Balderston, N. L., Vytal, K. E., O’Connell, K., Torrisi, S., Letkiewicz, A., Ernst, M., et al. (2017). Anxiety patients show reduced working memory related dlPFC activation during safety and threat. Depress. Anxiety 34, 25–36. doi: 10.1002/da.22518

Barrouillet, P., Bernardin, S., and Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. J. Exp. Psychol. Gen. 133, 83–100. doi: 10.1037/0096-3445.133.1.83

Barrouillet, P., and Camos, V. (2007). “The time-based resource-sharing model of working memory,” in The Cognitive Neuroscience of Working Memory , ed. N. Osaka (Oxford: Oxford University Press), 59–80. doi: 10.1093/acprof:oso/9780198570394.003.0004

Barrouillet, P., Gavens, N., Vergauwe, E., Gaillard, V., and Camos, V. (2009). Working memory span development: a time-based resource-sharing model account. Dev. Psychol. 45, 477–490. doi: 10.1037/a0014615

Bolkan, S. S., Stujenske, J. M., Parnaudeau, S., Spellman, T. J., Rauffenbart, C., Abbas, A. I., et al. (2017). Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996. doi: 10.1038/nn.4568

Borella, E., Carretti, B., Sciore, R., Capotosto, E., Taconnat, L., Cornoldi, C., et al. (2017). Training working memory in older adults: is there an advantage of using strategies? Psychol. Aging 32, 178–191. doi: 10.1037/pag0000155

Chein, J. M., Moore, A. B., and Conway, A. R. A. (2011). NeuroImage domain-general mechanisms of complex working memory span. Neuroimage 54, 550–559. doi: 10.1016/j.neuroimage.2010.07.067

Chen, C. J., Wu, C. H., Liao, Y. P., Hsu, H. L., Tseng, Y. C., Liu, H. L., et al. (2012). Working memory in patients with mild traumatic brain injury: functional MR imaging analysis. Radiology 264, 844–851. doi: 10.1148/radiol.12112154

Cowan, N. (1999). “An embedded-processes model of working memory,” in Models of Working Memory: Mechanisms of Active Maintenance and Executive Control , eds A. Miyake and P. Shah (Cambridge: Cambridge University Press). doi: 10.1017/S0140525X01003922

Cowan, N. (2005). Working memory capacity. Exp. Psychol. 54, 245–246. doi: 10.1027/1618-3169.54.3.245

Cowan, N. (2008). What are the differences between long-term, short-term, and working memory? Prog. Brain Res. 169, 323–338. doi: 10.1016/S0079-6123(07)00020-9

Cowan, N. (2010). The magical mystery four. Curr. Dir. Psychol. Sci. 19, 51–57. doi: 10.1177/0963721409359277

Dahlin, E., Nyberg, L., Bäckman, L., and Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: immediate training gains, transfer, and long-term maintenance. Psychol. Aging 23, 720–730. doi: 10.1037/a0014296

Daneman, M., and Carpenter, P. A. (1980). Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19, 450–466. doi: 10.1016/S0022-5371(80)90312-6

D’Esposito, M., and Postle, B. R. (2015). The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66, 115–142. doi: 10.1146/annurev-psych-010814-015031

Dikmen, S. S., Corrigan, J. D., Levin, H. S., Machamer, J., Stiers, W., and Weisskopf, M. G. (2009). Cognitive outcome following traumatic brain injury. J. Head Trauma Rehabil. 24, 430–438. doi: 10.1097/HTR.0b013e3181c133e9

Dima, D., Jogia, J., and Frangou, S. (2014). Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network. Hum. Brain Mapp. 35, 3025–3035. doi: 10.1002/hbm.22382

Dobryakova, E., Boukrina, O., and Wylie, G. R. (2015). Investigation of information flow during a novel working memory task in individuals with traumatic brain injury. Brain Connect. 5, 433–441. doi: 10.1089/brain.2014.0283

Duff, S. J., and Hampson, E. (2000). A beneficial effect of estrogen on working memory in postmenopausal women taking hormone replacement therapy. Horm. Behav. 38, 262–276. doi: 10.1006/hbeh.2000.1625

Dunning, D. L., Westgate, B., and Adlam, A.-L. R. (2016). A meta-analysis of working memory impairments in survivors of moderate-to-severe traumatic brain injury. Neuropsychology 30, 811–819. doi: 10.1037/neu0000285

Ellis, M. U., DeBoard Marion, S., McArthur, D. L., Babikian, T., Giza, C., Kernan, C. L., et al. (2016). The UCLA study of children with moderate-to-severe traumatic brain injury: event-related potential measure of interhemispheric transfer time. J. Neurotrauma 33, 990–996. doi: 10.1089/neu.2015.4023

Engle, R. W. (2002). Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23. doi: 10.1111/1467-8721.00160

Engle, R. W., and Kane, M. J. (2004). “Executive attention, working memory capacity, and a two-factor theory of cognitive control,” in The Psychology of Learning and Motivation: Advances in Research and Theory , ed. B. H. Ross (New York, NY: Elsevier), 145–199. doi: 10.1016/S0079-7421(03)44005-X

Farrer, T. J. (2017). Encyclopedia of Geropsychology , ed. N. A. Pachana. Singapore: Springer. doi: 10.1007/978-981-287-080-3

CrossRef Full Text

Fiebig, F., and Lansner, A. (2017). A spiking working memory model based on hebbian short-term potentiation. J. Neurosci. 37, 83–96. doi: 10.1523/JNEUROSCI.1989-16.2016

Friston, K., Moran, R., and Seth, A. K. (2013). Analysing connectivity with granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172–178. doi: 10.1016/j.conb.2012.11.010

Gorman, S., Barnes, M. A., Swank, P. R., Prasad, M., Cox, C. S., and Ewing-Cobbs, L. (2016). Does processing speed mediate the effect of pediatric traumatic brain injury on working memory? Neuropsychology 30, 263–273. doi: 10.1037/neu0000214

Gorman, S., Barnes, M. A., Swank, P. R., Prasad, M., and Ewing-Cobbs, L. (2012). The effects of pediatric traumatic brain injury on verbal and visual-spatial working memory. J. Int. Neuropsychol. Soc. 18, 29–38. doi: 10.1017/S1355617711001251

Gottwald, B., Wilde, B., Mihajlovic, Z., and Mehdorn, H. M. (2004). Evidence for distinct cognitive deficits after focal cerebellar lesions. J. Neurol. Neurosurg. Psychiatry 75, 1524–1531. doi: 10.1136/jnnp.2003.018093

Grot, S., Légaré, V. P., Lipp, O., Soulières, I., Dolcos, F., and Luck, D. (2017). Abnormal prefrontal and parietal activity linked to deficient active binding in working memory in schizophrenia. Schizophr. Res. 188, 68–74. doi: 10.1016/j.schres.2017.01.021

Guye, S., and von Bastian, C. C. (2017). Working memory training in older adults: bayesian evidence supporting the absence of transfer. Psychol. Aging 32, 732–746. doi: 10.1037/pag0000206

Haller, S., Montandon, M.-L., Rodriguez, C., Moser, D., Toma, S., Hofmeister, J., et al. (2017). Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline. Neuroradiology 59, 387–395. doi: 10.1007/s00234-017-1803-5

Haller, S., Rodriguez, C., Moser, D., Toma, S., Hofmeister, J., Sinanaj, I., et al. (2013). Acute caffeine administration impact on working memory-related brain activation and functional connectivity in the elderly: a BOLD and perfusion MRI study. Neuroscience 250, 364–371. doi: 10.1016/j.neuroscience.2013.07.021

Hedden, T., and Gabrieli, J. D. E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5, 87–96. doi: 10.1038/nrn1323

Heinzel, S., Rimpel, J., Stelzel, C., and Rapp, M. A. (2017). Transfer effects to a multimodal dual-task after working memory training and associated neural correlates in older adults – a pilot study. Front. Hum. Neurosci. 11:85. doi: 10.3389/fnhum.2017.00085

Hillary, F. G., Medaglia, J. D., Gates, K., Molenaar, P. C., Slocomb, J., Peechatka, A., et al. (2011). Examining working memory task acquisition in a disrupted neural network. Brain 134, 1555–1570. doi: 10.1093/brain/awr043

Hsu, H.-L., Chen, D. Y.-T., Tseng, Y.-C., Kuo, Y.-S., Huang, Y.-L., Chiu, W.-T., et al. (2015). Sex differences in working memory after mild traumatic brain injury: a functional MR imaging study. Radiology 276, 828–835. doi: 10.1148/radiol.2015142549

Humphreys, M. S., Bain, J. D., and Pike, R. (1989). Different ways to cue a coherent memory system: a theory for episodic, semantic, and procedural tasks. Psychol. Rev. 96, 208–233. doi: 10.1037/0033-295X.96.2.208

Janowsky, J. S., Chavez, B., and Orwoll, E. (2000). Sex steroids modify working memory. J. Cogn. Neurosci. 12, 407–414. doi: 10.1162/089892900562228

Jimura, K., Chushak, M. S., Westbrook, A., and Braver, T. S. (2017). Intertemporal decision-making involves prefrontal control mechanisms associated with working memory. Cereb. Cortex doi: 10.1093/cercor/bhx015 [Epub ahead of print].

Joseph, J. E., Swearingen, J. E., Corbly, C. R., Curry, T. E., and Kelly, T. H. (2012). Influence of estradiol on functional brain organization for working memory. Neuroimage 59, 2923–2931. doi: 10.1016/j.neuroimage.2011.09.067

Karbach, J., and Verhaeghen, P. (2014). Making working memory work: a meta-analysis of executive control and working memory training in younger and older adults. Psychol. Sci. 25, 2027–2037. doi: 10.1177/0956797614548725

Kim, C., Kroger, J. K., Calhoun, V. D., and Clark, V. P. (2015). The role of the frontopolar cortex in manipulation of integrated information in working memory. Neurosci. Lett. 595, 25–29. doi: 10.1016/j.neulet.2015.03.044

Klaassen, E. B., De Groot, R. H. M., Evers, E. A. T., Snel, J., Veerman, E. C. I., Ligtenberg, A. J. M., et al. (2013). The effect of caffeine on working memory load-related brain activation in middle-aged males. Neuropharmacology 64, 160–167. doi: 10.1016/j.neuropharm.2012.06.026

Kovacs, K., and Conway, A. R. A. (2016). Process overlap theory: a unified account of the general factor of intelligence. Psychol. Inq. 27, 151–177. doi: 10.1080/1047840X.2016.1153946

Le, T. M., Borghi, J. A., Kujawa, A. J., Klein, D. N., and Leung, H.-C. (2017). Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder. Neuroimage 14, 43–53. doi: 10.1016/j.nicl.2017.01.004

Liu, Z.-X., Glizer, D., Tannock, R., and Woltering, S. (2016). EEG alpha power during maintenance of information in working memory in adults with ADHD and its plasticity due to working memory training: a randomized controlled trial. Clin. Neurophysiol. 127, 1307–1320. doi: 10.1016/j.clinph.2015.10.032

Ma, L., Steinberg, J. L., Hasan, K. M., Narayana, P. A., Kramer, L. A., and Moeller, F. G. (2012). Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling. Hum. Brain Mapp. 33, 1850–1867. doi: 10.1002/hbm.21329

Maehler, C., and Schuchardt, K. (2016). Working memory in children with specific learning disorders and/or attention deficits. Learn. Individ. Differ. 49, 341–347. doi: 10.1016/j.lindif.2016.05.007

Mandalis, A., Kinsella, G., Ong, B., and Anderson, V. (2007). Working memory and new learning following pediatric traumatic brain injury. Dev. Neuropsychol. 32, 683–701. doi: 10.1080/87565640701376045

Manktelow, A. E., Menon, D. K., Sahakian, B. J., and Stamatakis, E. A. (2017). Working memory after traumatic brain injury: the neural basis of improved performance with methylphenidate. Front. Behav. Neurosci. 11:58. doi: 10.3389/fnbeh.2017.00058

Miller, G. A., Galanter, E., and Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Henry Holt and Company. doi: 10.1037/10039-000

Miyake, A., and Shah, P. (eds). (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York, NY: Cambridge University Press. doi: 10.1017/CBO9781139174909

Mongillo, G., Barak, O., and Tsodyks, M. (2008). Synaptic theory of working memory. Science 319, 1543–1546. doi: 10.1126/science.1150769

Moore, A. B., Li, Z., Tyner, C. E., Hu, X., and Crosson, B. (2013). Bilateral basal ganglia activity in verbal working memory. Brain Lang. 125, 316–323. doi: 10.1016/j.bandl.2012.05.003

Murty, V. P., Sambataro, F., Radulescu, E., Altamura, M., Iudicello, J., Zoltick, B., et al. (2011). Selective updating of working memory content modulates meso-cortico-striatal activity. Neuroimage 57, 1264–1272. doi: 10.1016/j.neuroimage.2011.05.006

Nadebaum, C., Anderson, V., and Catroppa, C. (2007). Executive function outcomes following traumatic brain injury in young children: a five year follow-up. Dev. Neuropsychol. 32, 703–728. doi: 10.1080/87565640701376086

Nadler, R. T., and Archibald, L. M. D. (2014). The assessment of verbal and visuospatial working memory with school age canadian children. Can. J. Speech Lang. Pathol. Audiol. 38, 262–279.

Nakagawa, S., Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Kotozaki, Y., et al. (2016). Sex-related differences in the effects of sleep habits on verbal and visuospatial working memory. Front. Psychol. 7:1128. doi: 10.3389/fpsyg.2016.01128

Nissim, N. R., O’Shea, A. M., Bryant, V., Porges, E. C., Cohen, R., and Woods, A. J. (2017). Frontal structural neural correlates of working memory performance in older adults. Front. Aging Neurosci. 8:328. doi: 10.3389/fnagi.2016.00328

Oren, N., Ash, E. L., Tarrasch, R., Hendler, T., Giladi, N., and Shapira-Lichter, I. (2017). Neural patterns underlying the effect of negative distractors on working memory in older adults. Neurobiol. Aging 53, 93–102. doi: 10.1016/j.neurobiolaging.2017.01.020

Osaka, M., Osaka, N., Kondo, H., Morishita, M., Fukuyama, H., Aso, T., et al. (2003). The neural basis of individual differences in working memory capacity: an fMRI study. Neuroimage 18, 789–797. doi: 10.1016/S1053-8119(02)00032-0

Owen, A. M., McMillan, K. M., Laird, A. R., and Bullmore, E. (2005). N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 25, 46–59. doi: 10.1002/hbm.20131

Owens, J. A., Spitz, G., Ponsford, J. L., Dymowski, A. R., Ferris, N., and Willmott, C. (2017). White matter integrity of the medial forebrain bundle and attention and working memory deficits following traumatic brain injury. Brain Behav. 7:e00608. doi: 10.1002/brb3.608

Perbal, S., Couillet, J., Azouvi, P., and Pouthas, V. (2003). Relationships between time estimation, memory, attention, and processing speed in patients with severe traumatic brain injury. Neuropsychologia 41, 1599–1610. doi: 10.1016/S0028-3932(03)00110-6

Perlstein, W. M., Cole, M. A., Demery, J. A., Seignourel, P. J., Dixit, N. K., Larson, M. J., et al. (2004). Parametric manipulation of working memory load in traumatic brain injury: behavioral and neural correlates. J. Int. Neuropsychol. Soc. 10, 724–741. doi: 10.1017/S1355617704105110

Pham, A. V., and Hasson, R. M. (2014). Verbal and visuospatial working memory as predictors of children’s reading ability. Arch. Clin. Neuropsychol. 29, 467–477. doi: 10.1093/arclin/acu024

Phillips, N. L., Parry, L., Mandalis, A., and Lah, S. (2017). Working memory outcomes following traumatic brain injury in children: a systematic review with meta-analysis. Child Neuropsychol. 23, 26–66. doi: 10.1080/09297049.2015.1085500

Rees, K., Allen, D., and Lader, M. (1999). The influences of age and caffeine on psychomotor and cognitive function. Psychopharmacology 145, 181–188. doi: 10.1007/s002130051047

Reuter-Lorenz, P. A., and Cappell, K. A. (2008). Neurocognitive ageing and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182. doi: 10.1111/j.1467-8721.2008.00570.x

Reuter-Lorenz, P. A., and Park, D. C. (2010). Human neuroscience and the aging mind : a new look at old problems. J. Gerontol. Psychol. Sci. 65, 405–415. doi: 10.1093/geronb/gbq035

Rieck, J. R., Rodrigue, K. M., Boylan, M. A., and Kennedy, K. M. (2017). Age-related reduction of BOLD modulation to cognitive difficulty predicts poorer task accuracy and poorer fluid reasoning ability. Neuroimage 147, 262–271. doi: 10.1016/j.neuroimage.2016.12.022

Rodriguez Merzagora, A. C., Izzetoglu, M., Onaral, B., and Schultheis, M. T. (2014). Verbal working memory impairments following traumatic brain injury: an fNIRS investigation. Brain Imaging Behav. 8, 446–459. doi: 10.1007/s11682-013-9258-8

Rose, N. S., LaRocque, J. J., Riggall, A. C., Gosseries, O., Starrett, M. J., Meyering, E. E., et al. (2016). Reactivation of latent working memories with transcranial magnetic stimulation. Science 354, 1136–1139. doi: 10.1126/science.aah7011

Rotzer, S., Loenneker, T., Kucian, K., Martin, E., Klaver, P., and von Aster, M. (2009). Dysfunctional neural network of spatial working memory contributes to developmental dyscalculia. Neuropsychologia 47, 2859–2865. doi: 10.1016/j.neuropsychologia.2009.06.009

Schneider-Garces, N. J., Gordon, B. A., Brumback-Peltz, C. R., Shin, E., Lee, Y., Sutton, B. P., et al. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. J. Cogn. Neurosci. 22, 655–669. doi: 10.1162/jocn.2009.21230

Schöning, S., Engelien, A., Kugel, H., Schäfer, S., Schiffbauer, H., Zwitserlood, P., et al. (2007). Functional anatomy of visuo-spatial working memory during mental rotation is influenced by sex, menstrual cycle, and sex steroid hormones. Neuropsychologia 45, 3203–3214. doi: 10.1016/j.neuropsychologia.2007.06.011

Silvanto, J. (2017). Working memory maintenance: sustained firing or synaptic mechanisms? Trends Cogn. Sci. 21, 152–154. doi: 10.1016/j.tics.2017.01.009

Stegmayer, K., Usher, J., Trost, S., Henseler, I., Tost, H., Rietschel, M., et al. (2015). Disturbed cortico–amygdalar functional connectivity as pathophysiological correlate of working memory deficits in bipolar affective disorder. Eur. Arch. Psychiatry Clin. Neurosci. 265, 303–311. doi: 10.1007/s00406-014-0517-5

Treble, A., Hasan, K. M., Iftikhar, A., Stuebing, K. K., Kramer, L. A., Cox, C. S., et al. (2013). Working memory and corpus callosum microstructural integrity after pediatric traumatic brain injury: a diffusion tensor tractography study. J. Neurotrauma 30, 1609–1619. doi: 10.1089/neu.2013.2934

Vallat-Azouvi, C., Weber, T., Legrand, L., and Azouvi, P. (2007). Working memory after severe traumatic brain injury. J. Int. Neuropsychol. Soc. 13, 770–780. doi: 10.1017/S1355617707070993

Vartanian, O., Jobidon, M.-E., Bouak, F., Nakashima, A., Smith, I., Lam, Q., et al. (2013). Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task. Neuroscience 236, 186–194. doi: 10.1016/j.neuroscience.2012.12.060

Wang, S., and Gathercole, S. E. (2013). Working memory deficits in children with reading difficulties: memory span and dual task coordination. J. Exp. Child Psychol. 115, 188–197. doi: 10.1016/j.jecp.2012.11.015

Wylie, G. R., Freeman, K., Thomas, A., Shpaner, M., OKeefe, M., Watts, R., et al. (2015). Cognitive improvement after mild traumatic brain injury measured with functional neuroimaging during the acute period. PLoS One 10:e0126110. doi: 10.1371/journal.pone.0126110

Ziaei, M., Salami, A., and Persson, J. (2017). Age-related alterations in functional connectivity patterns during working memory encoding of emotional items. Neuropsychologia 94, 1–12. doi: 10.1016/j.neuropsychologia.2016.11.012

Ziemus, B., Baumann, O., Luerding, R., Schlosser, R., Schuierer, G., Bogdahn, U., et al. (2007). Impaired working-memory after cerebellar infarcts paralleled by changes in bold signal of a cortico-cerebellar circuit. Neuropsychologia 45, 2016–2024. doi: 10.1016/j.neuropsychologia.2007.02.012

Zylberberg, J., and Strowbridge, B. W. (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annu. Rev. Neurosci. 40, 603–627. doi: 10.1146/annurev-neuro-070815-014006

Keywords : working memory, neuroscience, psychology, cognition, brain, central executive, prefrontal cortex, review

Citation: Chai WJ, Abd Hamid AI and Abdullah JM (2018) Working Memory From the Psychological and Neurosciences Perspectives: A Review. Front. Psychol. 9:401. doi: 10.3389/fpsyg.2018.00401

Received: 24 November 2017; Accepted: 09 March 2018; Published: 27 March 2018.

Reviewed by:

Copyright © 2018 Chai, Abd Hamid and Abdullah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Aini Ismafairus Abd Hamid, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Tools and Resources
• Customer Services
• Affective Science
• Biological Foundations of Psychology
• Clinical Psychology: Disorders and Therapies
• Cognitive Psychology/Neuroscience
• Developmental Psychology
• Educational/School Psychology
• Forensic Psychology
• Health Psychology
• History and Systems of Psychology
• Individual Differences
• Methods and Approaches in Psychology
• Neuropsychology
• Organizational and Institutional Psychology
• Personality
• Psychology and Other Disciplines
• Social Psychology
• Sports Psychology
• Share This Facebook LinkedIn Twitter

Article contents

Working memory.

• Tom Hartley Tom Hartley University of York
• , and  Graham J. Hitch Graham J. Hitch University of York
• https://doi.org/10.1093/acrefore/9780190236557.013.768
• Published online: 19 October 2022

Working memory is an aspect of human memory that permits the maintenance and manipulation of temporary information in the service of goal-directed behavior. Its apparently inelastic capacity limits impose constraints on a huge range of activities from language learning to planning, problem-solving, and decision-making. A substantial body of empirical research has revealed reliable benchmark effects that extend to a wide range of different tasks and modalities. These effects support the view that working memory comprises distinct components responsible for attention-like control and for short-term storage. However, the nature of these components, their potential subdivision, and their interrelationships with long-term memory and other aspects of cognition, such as perception and action, remain controversial and are still under investigation. Although working memory has so far resisted theoretical consensus and even a clear-cut definition, research findings demonstrate its critical role in both enabling and limiting human cognition and behavior.

• short-term memory
• serial order
• intelligence

Introduction

The term working memory refers to human memory functions that serve to maintain and manipulate temporary information. There is believed to be a limited capacity to support these functions which combine to play a key role in cognitive processes such as thinking and reasoning, problem-solving, and planning. A common illustration is mental calculation which typically involves maintaining some initial numerical information whilst carrying out a series of arithmetical operations on parts and maintaining any interim results. However, the range of activities that depend on working memory is very much wider than that example might suggest. Thus, perception and action can also depend critically on maintaining and manipulating temporary information, as for instance when identifying a familiar constellation in the night sky, or when preparing a meal.

Information about a stimulus remains available for a few seconds after it is perceived (short-term memory) but without active maintenance it rapidly becomes inaccessible ( Peterson & Peterson, 1959 ; Posner & Konick, 1966 ). Conceptually, working memory extends short-term memory by adding the active, attentional processes required to hold information in mind and to manipulate that information in the service of goal-directed behavior.

The short-term storage required for working memory can be distinguished from long-term memory, which is concerned with more permanent information acquired through learning or experience and includes declarative memory (retention of factual information and events) and procedural memory (underpinning skilled behavior; see Cohen & Squire, 1980 ). Notably, and in contrast to short-term memory, these forms of long-term memory are passive in the sense that, once acquired, memory for facts, events, and well-learned skills can persist over very long periods without moment-to-moment awareness. For example, a vocabulary of many thousands of words, including the relationship between their spoken forms and meanings, can be retained effortlessly over a lifetime. Similarly, once acquired through practice, complex and initially challenging behaviors such as swimming or riding a bicycle can become almost automatic and can be carried out with relatively little conscious control.

In early models of the human memory system (e.g., Atkinson & Shiffrin, 1968 ; see Logie, 1996 ) short-term memory was seen as a staging post or gateway to long-term memory, and it was recognized that it could also support more complex operations, such as reasoning, thus acting as a working memory. Subsequent research has attempted to refine the concept of working memory, characterizing its functional role, limits, and substructure, and distinguishing the processes involved in maintenance and manipulation of information from the storage systems with which they interact.

It has proven difficult, however, to disentangle working memory function from other aspects of cognition with which it overlaps. First, as described in more detail in the section “ Substructure and Relationship to Other Aspects of Cognition ,” many current accounts view the mechanisms of working memory as contributing to other perhaps more fundamental functions such as attention, long-term memory, perception, action, and representation. It is also notable that many informal descriptions of working memory emphasize consciousness and awareness as key features. Intuitively, many working memory functions are accessible to consciousness, and concepts such as mental manipulation, rehearsal, and losing track of information through inattention are subjectively encountered as characteristics of the conscious mind. Of course, by definition, people cannot be subjectively aware of any unconscious contributions to working memory (although they can potentially be inferred from behavior). Some theorists have argued that working memory is central to conscious thought (e.g., Baars, 2005 ; Carruthers, 2017 ), while other empirical researchers have sought to demonstrate nonconscious processes operating in what would typically be considered working memory tasks (e.g., Hassin et al., 2009 ; Soto et al., 2011 ). It is not clear whether, how, or to what extent consciousness is essential for working memory functions, or whether indeed the definition of working memory ought to include, or avoid, aspects of conscious experience. This article steers away from the topic, but the current status of the debate is captured in reviews such as Persuh et al. (2018) . Overall, it is difficult to precisely delineate the boundaries of working memory, whether with other cognitive functions or with consciousness and awareness; in philosophical terms it may not constitute a “natural kind” ( Gomez-Lavin, 2021 ).

These challenges make it difficult to establish a clear-cut and uncontroversial definition of working memory itself, its function, and substructure. Yet it is clear that working memory describes a cluster of related abilities that play a critical role in everyday thinking, placing important constraints on what we can and cannot do. Research on the topic has proved fruitful and although there remain many theoretical controversies about how working memory should be defined and analyzed, these mainly relate to the way in which its operations and substrates can be usefully subdivided, and their interrelationships with other cognitive systems such as those responsible for long-term memory and attention (see Logie et al., 2021 for in-depth discussion).

The following sections begin by identifying relatively uncontroversial characteristics of working memory and its temporal and capacity limits before outlining the main theoretical perspectives on the structure of working memory and its relationship to other forms of cognition. This is followed by a summary of the main experimental tasks and key empirical observations which underpin current understanding. Finally, a brief discussion of the importance of working memory beyond the laboratory is provided.

Temporal Limits

It is broadly agreed that its temporary or labile character is a defining characteristic of working memory. In contrast with established declarative and procedural memories that can be retained indefinitely, recently presented novel information is typically lost after a few seconds unless actively maintained. This active maintenance of short-term memory in order to complete a task is one of the core functions of working memory. As discussed further (see “ Limiting Mechanisms ”), it is less clear how such information is lost over time, or whether forgetting is strictly linked to the passage of time (decay) or merely correlated with it (for example, through an accumulation of interfering information). Nonetheless the vulnerability of short-term memory to degradation over time constrains the uses to which it can be put. Active maintenance processes include rehearsal—covertly subvocalizing verbal material, and attentional refreshing—selectively attending to an item that has not yet become inactive (see e.g., Camos et al., 2009 ). These active processes are themselves limited by the modality and quantity of the stored material, so that for instance subvocal rehearsal is disrupted by speaking aloud at the same time (“articulatory suppression”; Murray, 1967 ), and attentional refreshing can only be directed at a limited number of items in a given period of time ( Camos et al., 2018 ). Even though such active maintenance processes extend the temporal limits of short-term memory, when they do so at the cost of limited attentional resources, this reduces the availability of those resources for other goals.

Capacity Limits

It is also agreed that the limited capacity of working memory is a defining characteristic; in subjective terms, only a limited number of items can be “held in mind” at once. For example, in the classic digit span test of short-term memory capacity, participants are asked to briefly store, and then recall in order, arbitrary sequences of digits of gradually increasing length. In this type of task, accurate performance is typically only possible for very short sequences of up to three or four items beyond which errors of ordering become ever more frequent. Memory span is defined as the sequence length at which recall is correct half the time and is found to be between six and seven for digits, and even less for items such as unrelated words ( Crannell & Parrish, 1957 ). Similar capacity constraints are evident in nonverbal tasks requiring the recall of spatial sequences or the locations or visual properties of objects in spatial arrays. For instance, in the Corsi Block task, participants follow an assessor in tapping out a sequence of blocks in a tabletop array or a sequence of highlighted squares on a computer display. In the standard task, nine blocks are used in a fixed configuration and healthy participants can only recall sequences of around six taps even when tested immediately after presentation ( Corsi, 1972 ; Milner, 1971 ). Such tasks are helpful in identifying the fundamental capacity constraints on short-term memory but working memory capacity is also constrained by the active processes that maintain and manipulate information. This is typically assessed using complex span tasks which measure how many items can be held in mind while carrying out an attention-demanding concurrent task, leading to far lower estimates than simple spans ( Daneman & Carpenter, 1980 ). Similarly, participants show greatly reduced performance on a backward digit span task where mental manipulation is required to reverse the original sequence at recall. (Interestingly the Corsi span is the same in both directions; Kessels et al., 2008 ). Notably, forward and backward digit span and Corsi Block tasks are all used in the clinical assessment of neuropsychological patients as well as in research studies, highlighting the importance of working memory capacity in characterizing healthy and impaired cognitive function.

Just as the temporal limits of short-term memory can be extended by active maintenance processes, its capacity limits can be mitigated through strategic processing. Although it is clear that the number of items that can be stored in working memory is limited, there is some flexibility about what constitutes an item. For example, the sequence “1-0-0” might constitute three digits or might be represented as a single item, “hundred.” The possibility of more efficient forms of coding depends on interactions with long-term memory and can be exploited strategically to extend working memory capacity through “chunking” ( Miller, 1956 ). Thus, for an IT professional, the sequence “CPUBIOSPC” is more easily maintained as the familiar acronyms “CPU,” “BIOS,” and “PC” than as an arbitrary sequence of 10 letters.

While the previous example exploits long-term knowledge, even arbitrary grouping can extend the capacity of working memory, for example, in the immediate serial recall of verbal sequences, performance is improved when items are presented in groups. A spoken sequence of digits like “352-168” (i.e., with a pause between the two groups of digits) is recalled more easily than the ungrouped sequence “352168” ( Ryan, 1969 ). Again, this effect can be deployed strategically, and there is evidence that participants spontaneously group verbal material in memory.

More generally, prior learning and experience can not only expand effective storage capacity but can also contribute to efficient active processing operations. For example, children may initially use a counting-on strategy to perform simple sums such as 2 + 3 = 5, but later typically learn arithmetic number facts that automate such operations, in turn permitting more demanding mental arithmetic to be carried out within working memory ( Raghubar et al., 2010 ). In the extreme, expert calculators may collect extraordinarily large “mental libraries” of number facts ( Pesenti et al., 1999 ). Another powerful strategy for extending working memory capacity is seen in expert abacus operators who in mental calculation are able to use visual imagery to internalize algorithms learned from using the physical device ( Stigler, 1984 ).

Limiting Mechanisms

Despite the clear consensus that limited capacity and duration are defining characteristics of working memory, distinguishing it from other forms of memory and learning, there is less agreement about the mechanisms through which information is limited and forgotten.

In one account, the ultimate capacity limits of the system are determined by its access to a limited number of discrete slots, each of which can be used to hold a chunk of information ( Cowan, 2001 ; Luck & Vogel, 1997 ). However, an alternative and increasingly influential view is that working memory has access to a continuous resource which can be flexibly deployed to support a greater number of chunks or items on the one hand, or greater fidelity and precision on the other ( Bays & Husain, 2008 ; see Ma et al., 2014 for discussion).

The loss of information from working memory over time can similarly be attributed to different mechanisms, although here they do not amount to mutually exclusive models of the same phenomenon. One potential mechanism is decay, assumed to be a fundamental property of the substrate of short-term memory, through which information is lost due to the passage of time alone. In this view the attentional/executive component of working memory is typically deployed to extend its capacity by strategically (but effortfully) refreshing or rehearsing the content of short-term memory before it decays irretrievably. A further potential mechanism is interference. In this account, memory traces are prone to be confused with, or gradually corrupt one another. Several current models incorporate a combination of decay and interference ( Baddeley et al., 2021 ; Barrouillet & Camos, 2021 ; Cowan et al., 2021 ; Vandierendonck, 2021 ), while Oberauer (2021) stands out in rejecting time-based forgetting and maintenance processes, proposing in their place loss due to interference, and requiring a process dedicated to the active removal of outdated information from working memory.

Substructure and Relationship to Other Aspects of Cognition

Because it is linked to such a wide range of cognitive capacities, it can be difficult to clearly distinguish mechanisms of working memory from those of its specialized subcomponents or of general-purpose cognitive mechanisms which contribute to nonmemory functions. There is a broad consensus that working memory involves the interaction of an active process (corresponding to “attention” or “executive control”) with a substrate that can represent the content of memory and thus act as a short-term store. Authors disagree, or are sometimes agnostic, as to the extent to which these components can be usefully subdivided and the degree to which they are uniquely involved in working memory or more generally in cognition. Authors also differ in the emphasis they put on different modalities and tasks. These different emphases may sometimes mask a deeper consensus in which models are complementary rather than incompatible ( Miyake & Shah, 1999 ).

Although the term working memory had already been applied to the use of short-term memory in goal-directed behavior ( Atkinson & Shiffrin, 1968 ), it was the influential work of Baddeley and Hitch ( Baddeley, 1986 ; Baddeley & Hitch, 1974 ), that introduced the separation of attentional control processes (governed by a “central executive”) and short-term storage systems (thought of as “buffers,” i.e., distinct and specialized systems). They further identified a distinction between verbal and visual buffers which were subject to different forms of disruption and appeared to use distinct codes. In particular, verbal information could be stored in a speech-based system (termed the “phonological loop”), in which similar sounding items were more likely to be confused and which was disrupted by concurrent articulation. This work led to the development of the multicomponent model, which subsequently incorporated a richer characterization of the visuo-spatial store (the “visuospatial sketchpad,” see e.g., Baddeley & Logie, 1999 ; Logie, 1995) and, later, an additional store—the “episodic buffer” which holds amodal information and interacts with episodic long-term memory ( Baddeley, 2000 ). The possibility of further substructure within these core components is also recognized (e.g., Logie, 1995 on distinguishing visual and spatial subcomponents; see also Logie et al., 2021 on the possibility of multiple substrates within a multicomponent perspective).

An alternative view, the embedded processes model put forward by Cowan (1999) , is that working memory can be seen as the controlled, temporary activation of long-term memory representations, with access to awareness being limited to three to four items or chunks. A key distinction with the multicomponent model hangs on whether working memory relies on a distinct substrate (as implied by the term “buffer”), or whether the substrate is shared with long-term memory. Oberauer (2002) similarly identifies working memory with activated representations in long-term memory. In this account, the activated region forms a concentric structure within which a subset of individual chunks inside a “region of direct access” compete to be selected as the focus of attention.

Other more recent theoretical accounts have also emphasized the role of attentional control in determining the limits of working memory. For example, Engle (2002) regarded capacity constraints as reflecting the limited ability to control domain-general executive attention in situations where there is the potential for interference among conflicting responses. The time-based resource sharing account ( Barrouillet & Camos, 2004 ) highlights the need to balance the active refreshing of short-term with concurrent processing demands. In this view, constraints arise from the necessary trade-off between maintenance and manipulation, both of which rely on common attentional resources.

Many theoretical approaches to working memory do not follow Baddeley and Hitch in identifying modality-specific substrates for the temporary storage of information and assume instead a unitary system in which many different types of feature can be represented (e.g., Cowan et al., 2021 ; Oberauer, 2021 ). In such accounts, modality-specific phenomena are attributed to differences in the extent to which such features overlap within and between modalities. On the other hand, some authors acknowledge the possibility that there may be many alternative substrates, and that even within a modality further subdivisions may be possible. So, for example substrates supporting memory of verbal/linguistic content might further distinguish auditory-verbal, lexical, and semantic levels of representation ( Barnard, 1985 ; Martin, 1993 ).

Neuroscientific investigations have tended to support the consensus idea of a broad separation between executive and attentional control processes on the one hand, and (often modality-specific) stores on the other, but if anything have highlighted even more extensive overlap of the neural substrate of working memory with other cognitive functions including sensory–perceptual and action–motor representation, and greater granularity and fractionation of function within both storage and control systems. This led Postle (2006) to argue that working memory should be seen as an emergent property of the mind and brain rather than a specialized system in its own right:

Working memory functions arise through the coordinated recruitment, via attention, of brain systems that have evolved to accomplish sensory-, representation-, and action-related functions. ( Postle, 2006 ), p. 23

Even in this view it is clear that the mechanisms of working memory (however they overlap with other cognitive functions) involve the interaction of distinct components (at minimum “attention” is distinguished from sensory/representation and action-related function, and these latter functions may also be further subdivided).

Empirical Investigation and Key Findings

A variety of tasks have been developed to investigate working memory in the laboratory. These tasks, of course, always require participants to briefly retain some novel information, often the identity of a set of items which might be visual (for example, colored shapes) or verbal (digits, words, letters). However, they vary quite considerably in the extent to which they require memory for the structure of the set (such as, for verbal stimuli, their order or the spatial layout of an array of items), the degree to which they place an ongoing or concurrent load on memory and attention, and the precision with which sensory and perceptual properties of the individual items must be represented. An excellent overview of these techniques and associated benchmark findings can be found in Oberauer et al. (2018) .

In an item recognition task, participants determine whether a specific item was in a set (a sequentially presented list or simultaneously displayed array) that they previously studied ( McElree & Dosher, 1989 ). In probed recall , they are provided with a cue that uniquely specifies a given item from a previously presented set, which they are then required to recall ( Fuchs, 1969 ). In free recall tasks, typically employed with verbal stimuli, participants are presented with an ordered list, but are allowed to recall the items in any order ( Postman & Phillips, 1965 ), whereas in serial recall ( Jahnke, 1963 ) they are required to retain the original order of presentation.

The preceding tasks place increasing demands on short-term memory for the structure as well as the content of the presented stimuli, but place relatively little requirement for attention or the manipulation of memory content. To address these aspects of working memory, a range of additional tasks have been developed. In complex span tasks the to-be-remembered items are interleaved with a processing task, placing a greater concurrent load on the attentional system ( Daneman & Carpenter, 1980 ). In the N-back task , items are presented rapidly and continuously, with the participant being required to decide whether each new item repeats one encountered exactly n-items earlier in the sequence; to do this they must not only maintain the order of the previous n-items, but also manage the capacity-limited short-term memory resource as every new item arrives. These demands become increasingly taxing as the value of n increases, again giving an indication of the effects of load on performance or, since it is particularly amenable to neuroimaging, brain activity (see Owen et al., 2005 for review). 1 As mentioned, the manipulation requirements of serial recall can be increased by reversing the order in which items are to be recalled. More involved forms of mental manipulation are explicitly tested in memory updating paradigms ( Morris & Jones, 1990 ), within which, after being presented with an array or description, participants are instructed to carry out a sequence of operations before retrieving the result.

To assess its fidelity over brief intervals, tasks that require memory for detailed properties of the items are useful. In change detection tasks (e.g., Luck & Vogel, 1997 ), participants are required to respond to alterations in the stimulus (typically a visually presented array) between presentation and testing. These alterations can be made arbitrarily small, thus testing the precision of the underlying memory representation. Going beyond recognition -like responses to change, in continuous reproduction or delayed estimation tasks , participants are asked to recall continuous features of the stimuli such as the precise color or orientation of a shape within a previously-studied array (e.g., Bays & Husain, 2008 ). These tasks allow researchers to go beyond the question of whether information is merely retained or lost; they can be used to characterize and quantify the quality of the underlying representation, which in turn can shed light on the potential trade-off between capacity and precision in working memory.

The preceding tasks provide a very useful set of tools for investigating working memory in the laboratory. To investigate the structure and operation of the system, experiments typically manipulate characteristics of the items to be stored, and often employ concurrent tasks devised to selectively disrupt putative components or processes. In their standard forms, the individual items are treated as equally valuable or important, but it is also possible to cue specific items, locations, or serial positions in order to encourage participants to prioritize specific content (e.g., Hitch et al., 2020 ; Myers et al., 2017 ). Improved recall for such prioritized items can then reveal the operation of strategic processes. Overall, such manipulations show a range of replicable effects, not just on overall performance and response times, but also on patterns of error. In turn these benchmark effects have provided the impetus for current theories and provide important constraints for emerging computational models of working memory ( Oberauer et al., 2018 ).

Set Size and Retention Interval Effects

The most important effects relate to capacity and temporal limits that have already been discussed, and these apply across all applicable experimental paradigms and modalities. Specifically, in terms of capacity limits, task accuracy is impaired as the number of items (set size) is increased (response times also generally increase with set size), and in terms of temporal limits, accuracy declines monotonically with the duration of a delay between presentation and testing. The latter effect is reliably seen for both verbal and spatial materials when the retention interval is filled with a distracting task. It does not apply to unfilled delays in tasks with verbal materials, and only sometimes occurs with spatial materials. The difference between filled and unfilled delays forms part of the evidence in favor of the core working memory concept of active executive/attentional processes in sustaining otherwise fleeting short-term memories.

Primacy and Recency Effects

Another signature of working memory is that items are retrieved with greater accuracy if they are presented at the beginning (primacy) or end (recency) of a sequence relative to other items. The operation of primacy and recency effects is seen in immediate serial recall and other tasks where the presentation order is well-defined, and for both verbal and visuo-spatial content. This leads to a serial position curve (in which accuracy is plotted for each serial position in a list) with a characteristic bowed shape. The effect suggests that a shared or general serial ordering mechanism privileges access to these serial positions in an ordered list and/or impairs access to other serial positions. It is important to note that primacy and recency effects are also observed in the immediate free recall of lists of words when the capacity of working memory is greatly exceeded and where they may have a very different explanation (see e.g., Baddeley & Hitch, 1993 ).

Errors and Effects of Similarity

Working memory errors frequently involve confusion between items in the memory set. This is evident in a wide range of tasks (including variants of recognition, change-detection, and continuous reproduction tasks), but is perhaps clearest in immediate serial recall, where the most common forms of error involve the misordering of items. These errors most frequently involve local transpositions in which an item moves to a nearby list position, often exchanging with the item in that position. For example, a sequence like “D, F, E, O, P, Q” might be recalled as “D, F, O, E, P, Q.” Items are most likely to transpose to immediately adjacent list positions, with the probability of a transposition decreasing monotonically as the distance within the sequence increases. Note that there are fewer opportunities for local transpositions at the beginning and end of a sequence so the locality constraint on transpositions likely plays at least some role in primacy and recency effects.

In a verbal working memory task, when items from the memory set are confused with one another, they are most likely to be confused with phonologically similar items making performance for lists of similar sounding items poorer than for phonologically distinct items. In serial recall, this effect manifests itself as an increased tendency for phonologically similar items to transpose with one another, so that in the preceding example, items “D,” “E” and “P” (because they rhyme) would be more likely to transpose with one another than items “F,” “O,” and “Q.” Although these similarity effects are largely reported in verbal paradigms, analogous findings are sometimes observed with visual materials (for example, a sequence of similar colored shapes is harder to reconstruct than a sequence of distinctively colored shapes; Jalbert et al., 2008 ).

The analysis of errors and confusion has been critical in understanding the nature of representation in verbal working memory (for example, demonstrating the importance of speech-based rather than semantic codes), in developing the concept of the phonological loop, and in developing computational models which account for these findings in terms of underpinning serial ordering mechanisms.

Individual Differences and Links With Other Facets of Cognition

Speaking to questions about the relationship between working memory and other aspects of cognition, another set of benchmark findings is concerned with correlations between performance on working memory tasks and other measures. In particular, working memory is correlated with measures of attention and fluid intelligence (the capacity to solve novel problems independent of prior learning; see e.g., Engle, 2002 ) suggesting that all three constructs involve common resources. There is consensus that aspects of attention contribute to working memory, but attention is also relevant to tasks that make minimal demands on memory. At the same time, working memory plays an important role in problem solving in the absence of relevant prior learning, but it can also be applied to tasks that do not involve complex problems. This suggests a hierarchical relationship in which limited cognitive resources (i.e., attention) are applied to maintain and manipulate information in memory (attention + short-term memory = working memory) in the context of demanding problems (working memory + problem solving = fluid intelligence).

This somewhat simplistic sketch of the relationship between constructs omits the contribution of long-term memory and learning to working memory. That contribution is evident in several empirical phenomena. For example, the beneficial effect of chunking on recall often depends on familiarity with the chunks, as in the examples given previously. It is easily overlooked that the familiarity of the materials themselves is also important. For example, familiar words are recalled much better than nonwords ( Hulme et al., 1991 ) suggesting that words act as specialized phonological/semantic “chunks.” Similarly, grammatical sentences are recalled better than arbitrarily ordered lists or jumbled sentences ( Brener, 1940 ). The word–nonword and sentence superiority effects show that well-learned constraints on serial order (whether through syntax or phonotactics) can benefit recall. A related phenomenon, the Hebb repetition effect ( Hebb, 1961 ), can be seen in the laboratory: immediate serial recall for a specific random list gradually improves over successive trials when it becomes more familiar through being repeatedly but covertly presented interleaved among other lists.

The Importance of Working Memory

The laboratory tasks and benchmark findings outlined in the section “ Empirical Investigation and Key Findings ” have established its key characteristics, but the practical significance of working memory extends well beyond these phenomena into everyday cognition and learning. Notably the limits of working memory constrain what we can think about on a moment-to-moment basis and hence how quickly we can learn and what we can ultimately understand. An appreciation of the impact of working memory and its limitations is thus vitally important in the context of education (see e.g., Alloway & Gathercole, 2006 for a review). For example, individual differences in the capacity of phonological storage in verbal working memory are reciprocally linked to vocabulary acquisition in early childhood; children’s ability to repeat nonwords at age four (i.e., unfamiliar phonological sequences) predicts their vocabulary a year later. In turn, the emergence of vocabulary (i.e., phonological chunks) is associated with later improvements in nonword repetition ( Gathercole et al., 1992 ). It is not hard to imagine that this process amplifies the initial effect of variation in capacity, affecting literacy and then more advanced learning (potentially well beyond language abilities) that depends on reading. Working memory can similarly exert an influence on the emergence of numeracy and through it more advanced skills in arithmetic and mathematics. For example, kindergartners’ performance on a backward digit span task predicts their scores on a mathematics test a year later ( Gersten et al., 2005 ). In addition to these effects on the acquisition of foundational skills such as literacy and numeracy, working memory is important in maintaining and manipulating the information needed to carry out complex tasks in the classroom. Thus, students with lower working memory capacity can have difficulty retaining and following instructions ( Gathercole et al., 2008 ) again potentially hampering their ability to build more advanced skills and knowledge. Because of its critical involvement in classroom learning, working memory plays a central role in Cognitive Load Theory” ( Sweller, 2011 ) an influential educational framework which aims to incorporate principles derived from the architecture of human cognition into teaching methods.

Many measures of short-term memory and working memory show marked year-on-year improvement in childhood, with developmental change likely reflecting the maturation of several components that underpin performance ( Gathercole, 1999 ; Gathercole et al., 2004 ). These include changes in processes such as verbal recoding, subvocal rehearsal, the activation of temporary information and executive attentional control ( Camos & Barrouillet, 2011 ; Cowan et al., 2002 ; Hitch & Halliday, 1983 ). As might be expected given the centrality of working memory in the acquisition of language and numeracy, developmental disorders are commonly associated with reduced short-term or working memory capacity. Prominent examples include dyslexia ( Berninger et al., 2008 ), developmental language disorder ( Archibald & Gathercole, 2006 ; Montgomery et al., 2010 ), and dyscalculia ( Fias et al., 2013 ; McLean & Hitch, 1999 ). However, the nature of any causal role for working memory in developmental disorders has been controversial (see e.g., Masoura, 2006 ).

In adulthood, working memory capacity continues to limit the bandwidth that is available for cognitive operations, for example affecting planning and decision-making ( Gilhooly, 2005 ; Hinson et al., 2003 ). As we grow older, working memory capacity tends to decline, and there are some indications that this is associated with failing attention and greater vulnerability to distraction ( Hasher & Zacks, 1988 ; McNab et al., 2015 ; Park & Payer, 2006 ) rather than a mere reversal of earlier developmental gains. Across the entire lifespan, as it waxes and wanes, working memory plays an important part in shaping our daily experience.

Given its central role in constraining human cognitive abilities, extensive efforts have been made to develop interventions that can improve working memory, for example through computerized training programs. However, these efforts have so far met with limited success. Some working memory tasks show improvements with practice, but these effects tend to reflect near or intermediate transfer , specific to the trained task or (often closely-related) direct measures of working memory, rather than far transfer extending to more general improvements in other tasks thought to depend on working memory, such as reading comprehension or arithmetic ( Melby-Lervåg et al., 2016 ; Owen et al., 2010 ; Sala & Gobet, 2017 ). It has been argued that near and intermediate transfer effects arise through improvements in task-specific efficiency via refinement of strategies and long-term memory support (e.g., chunking) whereas more general benefits and far transfer would be expected to depend on the underlying capacity of attentional and storage systems ( von Bastian & Oberauer, 2014 ). The absence of clear evidence for far transfer despite such extensive research thus suggests that working memory capacity limits are a fundamental and unalterable feature of the human cognitive system.

Although it is perhaps premature to rule out the possibility of interventions that achieve increased working memory capacity, it appears at present that it can only be extended in specific contexts through more specialized training with particular tasks and materials. Paradoxically, this resistance to more general training may be what makes working memory so important; to the extent that its capacity limits are unavoidable, working memory helps to determine the scope of human cognition and spurs us to find strategies, technologies and cultural tools that allow us to go beyond them.

In conclusion, through the development of a powerful toolkit of experimental methods and of replicable empirical phenomena, the study of working memory function has provided many useful insights into interactions between attention and short-term memory. On the one hand these interactions can be used strategically to enhance goal-directed behavior and long-term learning while on the other they provide fundamental limits on cognition across the lifespan. Ongoing controversy over the structure of working memory relates to the difficulty in isolating these interactions from other facets of cognition, but there is little doubt about their importance in governing what we can and cannot do.

• Alloway, T. P. , & Gathercole, S. E. (2006). How does working memory work in the classroom? Education Research and Reviews , 1 (4), 134–139.
• Archibald, L. M. D. , & Gathercole, S. E. (2006). Short-term and working memory in specific language impairment . International Journal of Language & Communication Disorders , 41 (6), 675–693.
• Atkinson, R. C. , & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89–195). Academic Press.
• Baars, B. J. (2005). Global workspace theory of consciousness: Toward a cognitive neuroscience of human experience . In S. Laureys (Ed.), Progress in brain research (Vol. 150, pp. 45–53). Elsevier.
• Baddeley, A. (1986). Working memory (pp. xi, 289). Clarendon Press.
• Baddeley, A. (2000). The episodic buffer: A new component of working memory ? Trends in Cognitive Sciences , 4 (11), 417–423.
• Baddeley, A. D. , & Hitch, G. (1974). Working memory . In G. H. Bower (Ed.), Psychology of learning and motivation (Vol. 8, pp. 47–89). Academic Press.
• Baddeley, A. D. , & Hitch, G. (1993). The recency effect: Implicit learning with explicit retrieval? Memory & Cognition , 21 (2), 146–155.
• Baddeley, A. , Hitch, G. , & Allen, R. (2021). A multicomponent model of working memory . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory: The state of the science . Oxford University Press.
• Baddeley, A. , & Logie, R. (1999). Working memory: The multiple-component model . In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 28–61). Cambridge University Press.
• Barnard, P. (1985). Interacting cognitive subsystems: A psycholinguistic approach to short-term memory. In A. W. Ellis (Ed.), Progress in the psychology of language (Vol. 2, pp. 197–258).
• Barrouillet, P. , & Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General , 133 , 83–100.
• Barrouillet, P. , & Camos, V. (2021). The time-based resource-sharing model of working memory . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• Bays, P. M. , & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision . Science , 321 (5890), 851–854.
• Berninger, V. W. , Raskind, W. , Richards, T. , Abbott, R. , & Stock, P. (2008). A multidisciplinary approach to understanding developmental dyslexia within working-memory architecture: Genotypes, phenotypes, brain, and instruction . Developmental Neuropsychology , 33 (6), 707–744.
• Brener, R. (1940). An experimental investigation of memory span . Journal of Experimental Psychology , 26 (5), 467–482.
• Camos, V. , & Barrouillet, P. (2011). Developmental change in working memory strategies: From passive maintenance to active refreshing . Developmental Psychology , 47 (3), 898–904.
• Camos, V. , Johnson, M. , Loaiza, V. , Portrat, S. , Souza, A. , & Vergauwe, E. (2018). What is attentional refreshing in working memory? Annals of the New York Academy of Sciences , 1424 (1), 19–32.
• Camos, V. , Lagner, P. , & Barrouillet, P. (2009). Two maintenance mechanisms of verbal information in working memory . Journal of Memory and Language , 61 (3), 457–469.
• Carruthers, P. (2017). The centered mind: What the science of working memory shows us about the nature of human thought (Reprint ed.). Oxford University Press.
• Cohen, N. J. , & Squire, L. R. (1980). Preserved learning and retention of pattern-analyzing skill in amnesia: Dissociation of knowing how and knowing that . Science , 210 (4466), 207–210.
• Corsi, P. (1972). Memory and the medial temporal region of the brain . McGill University.
• Cowan, N. (1999). An Embedded-Processes Model of working memory . In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 62–101). Cambridge University Press.
• Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity . Behavioral and Brain Sciences , 24 (1), 87–114.
• Cowan, N. , Morey, C. C. , & Naveh-Benjamin, M. (2021). An embedded-processes approach to working memory: How is it distinct from other approaches, and to what ends ? In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• Cowan, N. , Saults, J. S. , & Elliott, E. M. (2002). The search for what is fundamental in the development of working memory . In R. V. Kail & H. W. Reese (Eds.), Advances in child development and behavior (Vol. 29, pp. 1–49). JAI.
• Crannell, C. W. , & Parrish, J. M. (1957). A comparison of immediate memory span for digits, letters, and words . The Journal of Psychology: Interdisciplinary and Applied , 44 , 319–327.
• Daneman, M. , & Carpenter, P. A. (1980). Individual differences in working memory and reading . Journal of Verbal Learning and Verbal Behavior , 19 (4), 450–466.
• Engle, R. W. (2002). Working memory capacity as executive attention . Current Directions in Psychological Science , 11 (1), 19–23.
• Fias, W. , Menon, V. , & Szucs, D. (2013). Multiple components of developmental dyscalculia . Trends in Neuroscience and Education , 2 (2), 43–47.
• Fuchs, A. H. (1969). Recall for order and content of serial word lists in short-term memory . Journal of Experimental Psychology , 82 (1, Pt.1), 14–21.
• Gathercole, S. E. (1999). Cognitive approaches to the development of short-term memory . Trends in Cognitive Sciences , 3 (11), 410–419.
• Gathercole, S. E. , Durling, E. , Evans, M. , Jeffcock, S. , & Stone, S. (2008). Working memory abilities and children’s performance in laboratory analogues of classroom activities . Applied Cognitive Psychology , 22 (8), 1019–1037.
• Gathercole, S. E. , Pickering, S. J. , Ambridge, B. , & Wearing, H. (2004). The structure of working memory from 4 to 15 years of age . Developmental Psychology , 40 (2), 177–190.
• Gathercole, S. E. , Willis, C. S. , Emslie, H. , & Baddeley, A. D. (1992). Phonological memory and vocabulary development during the early school years: A longitudinal study . Developmental Psychology , 28 (5), 887–898.
• Gersten, R. , Jordan, N. C. , & Flojo, J. R. (2005). Early identification and interventions for students with mathematics difficulties . Journal of Learning Disabilities , 38 (4), 293–304.
• Gilhooly, K. J. (2005). Working memory and planning. The Cognitive Psychology of Planning , 71–88.
• Gomez-Lavin, J. (2021). Working memory is not a natural kind and cannot explain central cognition . Review of Philosophy and Psychology , 12 (2), 199–225.
• Hasher, L. , & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view . In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 22, pp. 193–225). Academic Press.
• Hassin, R. R. , Bargh, J. A. , Engell, A. D. , & McCulloch, K. C. (2009). Implicit working memory . Consciousness and Cognition , 18 (3), 665–678.
• Hebb, D. O. (1961). Distinctive features of learning in the higher animal. In J. F. Delafresnaye (Ed.), Brain mechanisms and learning (pp. 37–46). Blackwell.
• Hinson, J. M. , Jameson, T. L. , & Whitney, P. (2003). Impulsive decision making and working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition , 29 (2), 298.
• Hitch, G. J. , Allen, R. J. , & Baddeley, A. D. (2020). Attention and binding in visual working memory: Two forms of attention and two kinds of buffer storage. Attention, Perception, & Psychophysics , 82 (1), 280–293.
• Hitch, G. J. , & Halliday, M. S. (1983). Working memory in children . Philosophical Transactions of the Royal Society of London. B, Biological Sciences , 302 (1110), 325–340.
• Hulme, C. , Maughan, S. , & Brown, G. D. A. (1991). Memory for familiar and unfamiliar words: Evidence for a long-term memory contribution to short-term memory span . Journal of Memory and Language , 30 (6), 685–701.
• Jahnke, J. C. (1963). Serial position effects in immediate serial recall . Journal of Verbal Learning and Verbal Behavior , 2 (3), 284–287.
• Jalbert, A. , Saint-Aubin, J. , & Tremblay, S. (2008). Short article: Visual similarity in short-term recall for where and when . Quarterly Journal of Experimental Psychology , 61 (3), 353–360.
• Kane, M. J. , Conway, A. R. , Miura, T. K. , & Colflesh, G. J. (2007). Working memory, attention control, and the N-back task: A question of construct validity. Journal of Experimental Psychology: Learning, Memory, and Cognition , 33 (3), 615.
• Kessels, R. P. C. , van den Berg, E. , Ruis, C. , & Brands, A. M. A. (2008). The backward span of the Corsi Block-Tapping Task and its association with the WAIS-III Digit Span . Assessment , 15 (4), 426–434.
• Logie R.H. (1995). Visuo-Spatial Working Memory . Psychology Press.
• Logie, R. H. (1996). The seven ages of working memory. In J. T. E. Richardson , R. W. Engle , L. Hasher , R. H. Logie , E. R. Stoltzfus , & R. T. Zacks (Eds.), Working memory and human cognition (pp. 31–65).
• Logie, R. , Camos, V. , & Cowan, N. (Eds.). (2021). Working memory: The state of the science (1st ed.). Oxford University Press.
• Luck, S. J. , & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions . Nature , 390 (6657), 279–281.
• Ma, W. J. , Husain, M. , & Bays, P. M. (2014). Changing concepts of working memory . Nature Neuroscience , 17 (3), 347–356.
• Martin, C. (1993). Short-term memory and sentence processing: Evidence from neuropsychology. Memory & Cognition , 21 (2), 176–183.
• Masoura, E. V. (2006). Establishing the link between working memory function and learning disabilities. Learning Disabilities: A Contemporary Journal , 4 (2), 29–41.
• McElree, B. , & Dosher, B. A. (1989). Serial position and set size in short-term memory: The time course of recognition . Journal of Experimental Psychology: General , 118 (4), 346–373.
• McLean, J. F. , & Hitch, G. J. (1999). Working memory impairments in children with specific arithmetic learning difficulties . Journal of Experimental Child Psychology , 74 (3), 240–260.
• McNab, F. , Zeidman, P. , Rutledge, R. B. , Smittenaar, P. , Brown, H. R. , Adams, R. A. , & Dolan, R. J. (2015). Age-related changes in working memory and the ability to ignore distraction . Proceedings of the National Academy of Sciences , 112 (20), 6515–6518.
• Melby-Lervåg, M. , Redick, T. S. , & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: Evidence From a meta-analytic review . Perspectives on Psychological Science , 11 (4), 512–534.
• Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information . Psychological Review , 63 (2), 81–97.
• Milner, B. (1971). Interhemispheric differences in the localization of psychological processes in man . British Medical Bulletin , 27 (3), 272–277.
• Miyake, A. , & Shah, P. (Eds.). (1999). Models of working memory: Mechanisms of active maintenance and executive control . Cambridge University Press.
• Montgomery, J. W. , Magimairaj, B. M. , & Finney, M. C. (2010). Working memory and specific language impairment: An update on the relation and perspectives on assessment and treatment . American Journal of Speech-Language Pathology , 19 (1), 78–94.
• Morris, N. , & Jones, D. M. (1990). Memory updating in working memory: The role of the central executive . British Journal of Psychology , 81 (2), 111–121.
• Murray, D. J. (1967). The role of speech responses in short-term memory . Canadian Journal of Psychology/Revue Canadienne de Psychologie , 21 (3), 263.
• Myers, N. E. , Stokes, M. G. , & Nobre, A. C. (2017). Prioritizing information during working memory: Beyond sustained internal attention . Trends in Cognitive Sciences , 21 (6), 449–461.
• Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. Journal of Experimental Psychology: Learning, Memory, and Cognition , 28 , 411–421.
• Oberauer, K. (2021). Towards a theory of working memory: From metaphors to mechanisms . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• Oberauer, K. , Lewandowsky, S. , Awh, E. , Brown, G. D. A. , Conway, A. , Cowan, N. , Donkin, C. , Farrell, S. , Hitch, G. J. , Hurlstone, M. J. , Ma, W. J. , Morey, C. C. , Nee, D. E. , Schweppe, J. , Vergauwe, E. , & Ward, G. (2018). Benchmarks for models of short-term and working memory . Psychological Bulletin , 144 (9), 885–958.
• Owen, A. M. , Hampshire, A. , Grahn, J. A. , Stenton, R. , Dajani, S. , Burns, A. S. , Howard, R. J. , & Ballard, C. G. (2010). Putting brain training to the test . Nature , 465 (7299), 775–778.
• Owen, A. M. , McMillan, K. M. , Laird, A. R. , & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies . Human Brain Mapping , 25 (1), 46–59.
• Park, D. C. , & Payer, D. (2006). Working memory across the adult lifespan . In Lifespan cognition: Mechanisms of change (pp. 128–142). Oxford University Press.
• Persuh, M. , LaRock, E. , & Berger, J. (2018). Working memory and consciousness: The current state of play . Frontiers in Human Neuroscience , 12 .
• Pesenti, M. , Seron, X. , Samson, D. , & Duroux, B. (1999). Basic and exceptional calculation abilities in a calculating prodigy: A case study . Mathematical Cognition , 5 (2), 97–148.
• Peterson, L. , & Peterson, M. J. (1959). Short-term retention of individual verbal items . Journal of Experimental Psychology , 58 (3), 193–198.
• Posner, M. I. , & Konick, A. F. (1966). On the role of interference in short-term retention . Journal of Experimental Psychology , 72 (2), 221–231.
• Postle, B. R. (2006). Working memory as an emergent property of the mind and brain . Neuroscience , 139 (1), 23–38.
• Postman, L. , & Phillips, L. W. (1965). Short-term temporal changes in free recall . Quarterly Journal of Experimental Psychology , 17 (2), 132–138.
• Raghubar, K. P. , Barnes, M. A. , & Hecht, S. A. (2010). Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches . Learning and Individual Differences , 20 (2), 110–122.
• Ryan, J. (1969). Grouping and short-term memory: Different means and patterns of grouping . Quarterly Journal of Experimental Psychology , 21 (2), 137–147.
• Sala, G. , & Gobet, F. (2017). Working memory training in typically developing children: A meta-analysis of the available evidence . Developmental Psychology , 53 (4), 671–685.
• Soto, D. , Mäntylä, T. , & Silvanto, J. (2011). Working memory without consciousness . Current Biology , 21 (22), R912–R913.
• Stigler, J. W. (1984). “Mental abacus”: The effect of abacus training on Chinese children’s mental calculation . Cognitive Psychology , 16 (2), 145–176.
• Sweller, J. (2011). Cognitive load theory . In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press.
• Vandierendonck, A. (2021). Multicomponent working memory system with distributed executive control . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• von Bastian, C. C. , & Oberauer, K. (2014). Effects and mechanisms of working memory training: A review . Psychological Research , 78 (6), 803–820.

1. However, note that, in at least one study ( Kane et al., 2007 ) n-back performance correlated only weakly with a measure of span, suggesting that, despite face validity, it may tax distinct cognitive resources.

Related Articles

• Working Memory and Cognitive Aging
• Memory Rehabilitation in Healthy Aging
• Culture and memory

Printed from Oxford Research Encyclopedias, Psychology. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 13 September 2024

• Cookie Policy
• Privacy Policy
• Legal Notice
• Accessibility
• [185.147.128.134]
• 185.147.128.134

Character limit 500 /500

Learn More Psychology

• Memory Psychology

10 Influential Memory Theories and Studies in Psychology

Discover the experiments and theories that shaped our understanding of how we develop and recall memories..

Permalink Print   |  

How do our memories store information? Why is it that we can recall a memory at will from decades ago, and what purpose does forgetting information serve?

The human memory has been the subject of investigation among many 20th Century psychologists and remains an active area of study for today’s cognitive scientists. Below we take a look at some of the most influential studies, experiments and theories that continue to guide our understanding of the function of memory.

1 Multi-Store Model

(atkinson & shiffrin, 1968).

An influential theory of memory known as the multi-store model was proposed by Richard Atkinson and Richard Shiffrin in 1968. This model suggested that information exists in one of 3 states of memory: the sensory, short-term and long-term stores . Information passes from one stage to the next the more we rehearse it in our minds, but can fade away if we do not pay enough attention to it. Read More

Information enters the memory from the senses - for instance, the eyes observe a picture, olfactory receptors in the nose might smell coffee or we might hear a piece of music. This stream of information is held in the sensory memory store , and because it consists of a huge amount of data describing our surroundings, we only need to remember a small portion of it. As a result, most sensory information ‘ decays ’ and is forgotten after a short period of time. A sight or sound that we might find interesting captures our attention, and our contemplation of this information - known as rehearsal - leads to the data being promoted to the short-term memory store , where it will be held for a few hours or even days in case we need access to it.

The short-term memory gives us access to information that is salient to our current situation, but is limited in its capacity.

Therefore, we need to further rehearse information in the short-term memory to remember it for longer. This may involve merely recalling and thinking about a past event, or remembering a fact by rote - by thinking or writing about it repeatedly. Rehearsal then further promotes this significant information to the long-term memory store, where Atkinson and Shiffrin believed that it could survive for years, decades or even a lifetime.

Key information regarding people that we have met, important life events and other important facts makes it through the sensory and short-term memory stores to reach the long-term memory .

Learn more about Atkinson and Shiffrin’s Multi-Store Model

2 Levels of Processing

(craik & lockhart, 1972).

Fergus Craik and Robert Lockhart were critical of explanation for memory provided by the multi-store model, so in 1972 they proposed an alternative explanation known as the levels of processing effect . According to this model, memories do not reside in 3 stores; instead, the strength of a memory trace depends upon the quality of processing , or rehearsal , of a stimulus . In other words, the more we think about something, the more long-lasting the memory we have of it ( Craik & Lockhart , 1972). Read More

Craik and Lockhart distinguished between two types of processing that take place when we make an observation : shallow and deep processing. Shallow processing - considering the overall appearance or sound of something - generally leads to a stimuli being forgotten. This explains why we may walk past many people in the street on a morning commute, but not remember a single face by lunch time.

Deep (or semantic) processing , on the other hand, involves elaborative rehearsal - focusing on a stimulus in a more considered way, such as thinking about the meaning of a word or the consequences of an event. For example, merely reading a news story involves shallow processing, but thinking about the repercussions of the story - how it will affect people - requires deep processing, which increases the likelihood of details of the story being memorized.

In 1975, Craik and another psychologist, Endel Tulving , published the findings of an experiment which sought to test the levels of processing effect.

Participants were shown a list of 60 words, which they then answered a question about which required either shallow processing or more elaborative rehearsal. When the original words were placed amongst a longer list of words, participants who had conducted deeper processing of words and their meanings were able to pick them out more efficiently than those who had processed the mere appearance or sound of words ( Craik & Tulving , 1975).

Learn more about Levels of Processing here

3 Working Memory Model

(baddeley & hitch, 1974).

Whilst the Multi-Store Model (see above) provided a compelling insight into how sensory information is filtered and made available for recall according to its importance to us, Alan Baddeley and Graham Hitch viewed the short-term memory (STM) store as being over-simplistic and proposed a working memory model (Baddeley & Hitch, 1974), which replace the STM.

The working memory model proposed 2 components - a visuo-spatial sketchpad (the ‘inner eye’) and an articulatory-phonological loop (the ‘inner ear’), which focus on a different types of sensory information. Both work independently of one another, but are regulated by a central executive , which collects and processes information from the other components similarly to how a computer processor handles data held separately on a hard disk. Read More

According to Baddeley and Hitch, the visuo-spatial sketchpad handles visual data - our observations of our surroundings - and spatial information - our understanding of objects’ size and location in our environment and their position in relation to ourselves. This enables us to interact with objects: to pick up a drink or avoid walking into a door, for example.

The visuo-spatial sketchpad also enables a person to recall and consider visual information stored in the long-term memory. When you try to recall a friend’s face, your ability to visualize their appearance involves the visuo-spatial sketchpad.

The articulatory-phonological loop handles the sounds and voices that we hear. Auditory memory traces are normally forgotten but may be rehearsed using the ‘inner voice’; a process which can strengthen our memory of a particular sound.

Learn more about Baddeley and Hitch’s working memory model here

4 Miller’s Magic Number

(miller, 1956).

Prior to the working memory model, U.S. cognitive psychologist George A. Miller questioned the limits of the short-term memory’s capacity. In a renowned 1956 paper published in the journal Psychological Review , Miller cited the results of previous memory experiments, concluding that people tend only to be able to hold, on average, 7 chunks of information (plus or minus two) in the short-term memory before needing to further process them for longer storage. For instance, most people would be able to remember a 7-digit phone number but would struggle to remember a 10-digit number. This led to Miller describing the number 7 +/- 2 as a “magical” number in our understanding of memory. Read More

But why are we able to remember the whole sentence that a friend has just uttered, when it consists of dozens of individual chunks in the form of letters? With a background in linguistics, having studied speech at the University of Alabama, Miller understood that the brain was able to ‘chunk’ items of information together and that these chunks counted towards the 7-chunk limit of the STM. A long word, for example, consists of many letters, which in turn form numerous phonemes. Instead of only being able to remember a 7-letter word, the mind “recodes” it, chunking the individual items of data together. This process allows us to boost the limits of recollection to a list of 7 separate words.

Miller’s understanding of the limits of human memory applies to both the short-term store in the multi-store model and Baddeley and Hitch’s working memory. Only through sustained effort of rehearsing information are we able to memorize data for longer than a short period of time.

Read more about Miller’s Magic Number here

5 Memory Decay

(peterson and peterson, 1959).

Following Miller’s ‘magic number’ paper regarding the capacity of the short-term memory, Peterson and Peterson set out to measure memories’ longevity - how long will a memory last without being rehearsed before it is forgotten completely?

In an experiment employing a Brown-Peterson task, participants were given a list of trigrams - meaningless lists of 3 letters (e.g. GRT, PXM, RBZ) - to remember. After the trigrams had been shown, participants were asked to count down from a number, and to recall the trigrams at various periods after remembering them. Read More

The use of such trigrams makes it impracticable for participants to assign meaning to the data to help encode them more easily, while the interference task prevented rehearsal, enabling the researchers to measure the duration of short-term memories more accurately.

Whilst almost all participants were initially able to recall the trigrams, after 18 seconds recall accuracy fell to around just 10%. Peterson and Peterson’s study demonstrated the surprising brevity of memories in the short-term store, before decay affects our ability to recall them.

Learn more about memory decay here

6 Flashbulb Memories

(brown & kulik, 1977).

There are particular moments in living history that vast numbers of people seem to hold vivid recollections of. You will likely be able to recall such an event that you hold unusually detailed memories of yourself. When many people learned that JFK, Elvis Presley or Princess Diana died, or they heard of the terrorist attacks taking place in New York City in 2001, a detailed memory seems to have formed of what they were doing at the particular moment that they heard such news.

Psychologists Roger Brown and James Kulik recognized this memory phenomenon as early as 1977, when they published a paper describing flashbulb memories - vivid and highly detailed snapshots created often (but not necessarily) at times of shock or trauma. Read More

We are able to recall minute details of our personal circumstances whilst engaging in otherwise mundane activities when we learnt of such events. Moreover, we do not need to be personally connected to an event for it to affect us, and for it lead to the creation of a flashbulb memory.

Learn more about Flashbulb Memories here

7 Memory and Smell

The link between memory and sense of smell helps many species - not just humans - to survive. The ability to remember and later recognize smells enables animals to detect the nearby presence of members of the same group, potential prey and predators. But how has this evolutionary advantage survived in modern-day humans?

Researchers at the University of North Carolina tested the olfactory effects on memory encoding and retrieval in a 1989 experiment. Male college students were shown a series of slides of pictures of females, whose attractiveness they were asked to rate on a scale. Whilst viewing the slides, the participants were exposed to pleasant odor of aftershave or an unpleasant smell. Their recollection of the faces in the slides was later tested in an environment containing either the same or a different scent. Read More

The results showed that participants were better able to recall memories when the scent at the time of encoding matched that at the time of recall (Cann and Ross, 1989). These findings suggest that a link between our sense of smell and memories remains, even if it provides less of a survival advantage than it did for our more primitive ancestors.

8 Interference

Interference theory postulates that we forget memories due to other memories interfering with our recall. Interference can be either retroactive or proactive: new information can interfere with older memories (retroactive interference), whilst information we already know can affect our ability to memorize new information (proactive interference).

Both types of interference are more likely to occur when two memories are semantically related, as demonstrated in a 1960 experiment in which two groups of participants were given a list of word pairs to remember, so that they could recall the second ‘response’ word when given the first as a stimulus. A second group was also given a list to learn, but afterwards was asked to memorize a second list of word pairs. When both groups were asked to recall the words from the first list, those who had just learnt that list were able to recall more words than the group that had learnt a second list (Underwood & Postman, 1960). This supported the concept of retroactive interference: the second list impacted upon memories of words from the first list. Read More

Interference also works in the opposite direction: existing memories sometimes inhibit our ability to memorize new information. This might occur when you receive a work schedule, for instance. When you are given a new schedule a few months later, you may find yourself adhering to the original times. The schedule that you already knew interferes with your memory of the new schedule.

9 False Memories

Can false memories be implanted in our minds? The idea may sound like the basis of a dystopian science fiction story, but evidence suggests that memories that we already hold can be manipulated long after their encoding. Moreover, we can even be coerced into believing invented accounts of events to be true, creating false memories that we then accept as our own.

Cognitive psychologist Elizabeth Loftus has spent much of her life researching the reliability of our memories; particularly in circumstances when their accuracy has wider consequences, such as the testimonials of eyewitness in criminal trials. Loftus found that the phrasing of questions used to extract accounts of events can lead witnesses to attest to events inaccurately. Read More

In one experiment, Loftus showed a group of participants a video of a car collision, where the vehicle was travelling at a one of a variety of speeds. She then asked them the car’s speed using a sentence whose depiction of the crash was adjusted from mild to severe using different verbs. Loftus found when the question suggested that the crash had been severe, participants disregarded their video observation and vouched that the car had been travelling faster than if the crash had been more of a gentle bump (Loftus and Palmer, 1974). The use of framed questions, as demonstrated by Loftus, can retroactively interfere with existing memories of events.

James Coan (1997) demonstrated that false memories can even be produced of entire events. He produced booklets detailing various childhood events and gave them to family members to read. The booklet given to his brother contained a false account of him being lost in a shopping mall, being found by an older man and then finding his family. When asked to recall the events, Coan’s brother believed the lost in a mall story to have actually occurred, and even embellished the account with his own details (Coan, 1997).

Read more about false memories here

10 The Weapon Effect on Eyewitness Testimonies

(johnson & scott, 1976).

A person’s ability to memorize an event inevitably depends not just on rehearsal but also on the attention paid to it at the time it occurred. In a situation such as an bank robbery, you may have other things on your mind besides memorizing the appearance of the perpetrator. But witness’s ability to produce a testimony can sometimes be affected by whether or not a gun was involved in a crime. This phenomenon is known as the weapon effect - when a witness is involved in a situation in which a weapon is present, they have been found to remember details less accurately than a similar situation without a weapon. Read More

The weapon effect on eyewitness testimonies was the subject of a 1976 experiment in which participants situated in a waiting room watched as a man left a room carrying a pen in one hand. Another group of participants heard an aggressive argument, and then saw a man leave a room carrying a blood-stained knife.

Later, when asked to identify the man in a line-up, participants who saw the man carrying a weapon were less able to identify him than those who had seen the man carrying a pen (Johnson & Scott, 1976). Witnesses’ focus of attention had been distracted by a weapon, impeding their ability to remember other details of the event.

Which Archetype Are You?

Are You Angry?

Windows to the Soul

Are You Stressed?

Attachment & Relationships

Memory Like A Goldfish?

31 Defense Mechanisms

Slave To Your Role?

Are You Fixated?

Interpret Your Dreams

How to Read Body Language

How to Beat Stress and Succeed in Exams

More on Memory Psychology

Test your short-term memory with this online feature.

Memory Test

False Memories

How false memories are created and can affect our ability to recall events.

Why Do We Forget?

Why do we forget information? Find out in this fascinating article exploring...

Conditioned Behavior

What is conditioning? What Pavlov's dogs experiment teaches us about how we...

Interrupt To Remember?

Explanation of the Zeigarnik effect, whereby interruption of a task can lead to...

Sign Up for  Unlimited Access

• Psychology approaches, theories and studies explained
• Body Language Reading Guide
• How to Interpret Your Dreams Guide
• Self Hypnosis Downloads
• Plus More Member Benefits

You May Also Like...

Why do we dream, persuasion with ingratiation, dark sense of humor linked to intelligence, making conversation, brainwashed, psychology of color, master body language, nap for performance, psychology  guides.

Learn Body Language Reading

How To Interpret Your Dreams

Overcome Your Fears and Phobias

Psychology topics, learn psychology.

• Access 2,200+ insightful pages of psychology explanations & theories
• Insights into the way we think and behave
• Body Language & Dream Interpretation guides
• Self hypnosis MP3 downloads and more
• Behavioral Approach
• Eye Reading
• Stress Test
• Cognitive Approach
• Fight-or-Flight Response
• Neuroticism Test

© 2024 Psychologist World. Home About Contact Us Terms of Use Privacy & Cookies Hypnosis Scripts Sign Up

• Brain Function
• Central Nervous System
• Nervous System
• Neuroanatomy
• Neuroscience

Perspectives of Human Memory Models: A Critical Review

• January 2021
• This person is not on ResearchGate, or hasn't claimed this research yet.

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

No file available

To read the file of this research, you can request a copy directly from the author.

• J Exp Psychol
• Robert G. Crowder
• Rev Canad Psychol
• Robert S. Lockhart

• Nelson Cowan
• A. Baddeley
• Newman Guttman
• Bela Julesz
• Alan Baddeley

• Michael D. Cohen
• Paul Bacdayan
• Endel Tulving
• Psychol Learn Motiv

• R. M. Shiffrin
• A. D. Baddeley
• Edward L. Thorndike

• BEHAV BRAIN SCI

• Michael T. Ullman
• George A. Miller
• A D Baddeley
• A Beversdorf
• Gabriel A Radvansky
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

• Search Menu
• Sign in through your institution
• The Journals of Gerontology, Series B (1995-present)
• Journal of Gerontology (1946-1994)
• Advance Articles
• Editor's Choice
• Virtual Collection
• Supplements
• Special Issues
• Calls for Papers
• Author Guidelines
• Psychological Sciences Submission Site
• Social Sciences Submission Site
• Why Submit to the GSA Portfolio?
• Advertising and Corporate Services
• Advertising
• Reprints and ePrints
• Sponsored Supplements
• Journals Career Network
• About The Journals of Gerontology, Series B
• About The Gerontological Society of America
• Editorial Board - Psychological Sciences
• Editorial Board - Social Sciences
• Self-Archiving Policy
• Dispatch Dates
• GSA Journals
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Early evidence of memory impairment with age, theoretical models of aging and memory decline, age invariance in specific memory domains, inadequacy of single-mechanism theories of age-related memory decline, insights from neuroimaging, conclusions.

• < Previous

Theories of Memory and Aging: A Look at the Past and a Glimpse of the Future

Correspondence should be addressed to Denise C. Park, PhD, Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600 Viceroy Drive, Suite 800, Dallas, TX 75235. E-mail: [email protected]

Decision Editor: Nicole Anderson, PhD

• Article contents
• Figures & tables
• Supplementary Data

Denise C. Park, Sara B. Festini, Theories of Memory and Aging: A Look at the Past and a Glimpse of the Future, The Journals of Gerontology: Series B , Volume 72, Issue 1, 1 January 2017, Pages 82–90, https://doi.org/10.1093/geronb/gbw066

• Permissions Icon Permissions

The present article reviews theories of memory and aging over the past 50 years. Particularly notable is a progression from early single-mechanism perspectives to complex multifactorial models proposed to account for commonly observed age deficits in memory function. The seminal mechanistic theories of processing speed, limited resources, and inhibitory deficits are discussed and viewed as especially important theories for understanding age-related memory decline. Additionally, advances in multivariate techniques including structural equation modeling provided new tools that led to the development of more complex multifactorial theories than existed earlier. The important role of neuroimaging is considered, along with the current prevalence of intervention studies. We close with predictions about new directions that future research on memory and aging will take.

One of the most striking aspects of aging is that memory processes show decline. This was recognized as early as 700 BC by Solon, a Greek philosopher who, in his Elegy on the Ages of Men , noted that intellectual capacities began to diminish around age 56–63 (see Cokayne, 2003 ). Moreover, Virgil, a Roman poet, alluded to the degradation in memory over time in Eclogues IX , writing, “Time robs us of all, even of memory” ( Cokayne, 2003 , p. 67). Although some level of cognitive frailty has always been viewed as an aspect of aging, the study of cognitive aging came into its own right as the science of human behavior advanced and as significant increases in human longevity were realized. Today the study of memory and aging has taken on particular significance and is a center-stage issue due primarily to the increased longevity resulting from the heightened ability to extend life in the face of major diseases. Indeed, due to longevity, Alzheimer’s disease has become a major cause of death and disability for society today, sparking a quest to develop early interventions to prevent or delay neuropathological aging.

In the present article, we review major theories of aging and memory and how they have emerged over the past 50 years (see Figure 1 for a depiction of the exponential growth in research on memory and aging from 1965 to 2013). We start by discussing some of the earliest empirical findings on aging and memory and follow by reviewing initial theoretical explanations for these findings. These early theories were surprisingly insightful, and many versions of them are still viable today. Contemporary models of memory decline build upon these early influential theories, and many have also begun to show a shift toward the brain. Hence, we summarize prominent mechanisms that have been hypothesized to explain why memory declines as we get older, noting early theories that are still dominant today along with newer theories that place additional focus on neural contributions to memory impairment. We also discuss specific memory domains that appear to be more age invariant. Finally, we close with current trends in the field of memory and aging, providing reflections on what we expect for future research.

Depiction of the exponential increase in research on memory and aging. The number of articles published each year is plotted, after using Medline trend: Automated yearly statistics of PubMed results for any query ( Corlan, 2004 ) and the query terms “memory” and “age”. Website: http://dan.corlan.net/medline-trend.html

Some of the earliest findings about age differences in memory stem directly from verbal learning paradigms, which dominated the study of human memory when psychology was in the grip of behaviorism. In 1929, Willoughby documented a gradual age-related differences in incidental memory for digit–symbol pairs, such that recall decreased from age 20 through age 70. Moreover, using an intentional paired-associate learning paradigm, Ruch (1934) demonstrated that adults aged 60–82 years exhibited worse memory than adults aged 34–59 years, and that 12- to 17-year-olds displayed the best performance (for a detailed review, see Kausler, 1991 ). Some early work also assessed strategy use and noted that, compared with younger adults, older adults were less likely to use imagery or to create verbal mnemonics when attempting to remember paired associates ( Hulicka & Grossman, 1967 ). Furthermore, older adults took longer to learn newly re-paired paired associates to criterion in an A–B A–C list-learning paradigm, indicating either greater negative transfer (i.e., interference) for older participants or greater positive transfer from repeated cue presentations in younger adults ( Arenberg, 1967 ).

Another major early finding was that memory effects associated with age were typically larger when participants were asked to recall a list of words, compared with merely recognize them (e.g., Schonfield, 1965 ; Smith, 1977 ). Schonfield (1965) reported that older adults had equivalent recognition performance to younger adults but markedly impaired recall (but see e.g., Erber, 1974 ; Harwood & Naylor, 1969 , who report both impaired recall and impaired recognition in older adults). Also of interest was that picture memory appeared to be protected from dramatic age effects, as older adults recalled and recognized pictures better than words (e.g., Park, Puglisi, & Sovacool, 1983 ). Perhaps the most well-recognized finding of all was that on virtually any task that had a speed component, participants became slower with age (e.g., Brinley, 1965 ). Given these empirical results, scientists began proposing mechanistic theories to account for the commonly observed age differences in memory.

Speed of Processing as a Mechanism of Memory Decline

The initial theories on speed were pioneered by James Birren, and then by John Cerella and Timothy Salthouse (e.g., Birren, 1965 ; Birren, Woods, & Williams, 1980 ; Cerella, 1985 ; Salthouse, 1996 ). In an early study, Birren noted that participants showed increasingly slower processing time for a broad range of cognitive tasks as a function of age ( Birren, 1965 ), resulting in the hypothesis that slowed processing speed was a fundamental mechanism that governed many age deficits, including memory. Cerella suggested that slowing resulted from deletion of random links in the memory network, which created longer, more circuitous memorial processing paths ( Cerella, 1990 ). In a large corpus of work beginning in the 1980s, Salthouse further validated and expanded this notion that processing speed was fundamental to explain age differences in memory ( Salthouse, 1985a , 1985b , 1996 ). His view is best summarized in an article where he proposes that older adults are deficient in two important mechanisms that account for age-related differences in attention, memory, and reasoning ( Salthouse, 1996 ). He posited a limited time mechanism , in which older adults have greater difficulty performing higher-level operations because it takes them longer to process early operations, and a simultaneity mechanism , in which older adults cannot consider as many task-relevant components together compared with younger adults because the products of earlier processing may not be available once ongoing processing is completed. Salthouse repeatedly showed that most age-related variance in cognitive tasks, including memory, could be accounted for by measures of speed.

The Processing Resource Model of Memory Deficits in Cognitive Aging

Age differences in levels of processing.

In 1972, Craik and Lockhart presented the levels of processing theory of memory, which marked an important transition between basic stimulus–response verbal learning to the study of mental models. They provided evidence that an intention to learn was not the most critical component for remembering. Rather, it was the quality of the encoding operation—not the time on task—that best predicted memory. Specifically, they reported that guiding participants to engage in deep semantic processing, even when they were not intentionally trying to remember, resulted in memory recall that was equivalent or superior to that of participants who were actively studying (see also Hyde & Jenkins, 1969 ). The finding that quality of processing could be more important than intention to learn was highly influential in the research community and led to the notion that older adults were deficient in spontaneously engaging in deep processing, as age effects were particularly large when learning was intentional. The inefficiency of older adults spontaneously performing higher-level encoding strategies was termed the production deficit hypothesis ( Kausler, 1970 ). If, however, older adults were guided to process meaning and engage in elaborative encoding, memory could be repaired to be more similar to that of younger adults.

Environmental support

The notion that cognition could be repaired led logically to the concept of environmental support. Environmental support, also termed contextual support, involved the presentation of external cues or processing guidance, which provided “mental crutches” that made stimuli easier to remember, especially for older adults (e.g., Craik, 1986 ). For example, if during a list-learning experiment one was presented with the word “feather” to remember, the provision of the cue word “chicken” at encoding would be a type of environmental support that would facilitate recall of the studied word at test. Experimental research has demonstrated that older adults tend to have better memory when external cues are provided (e.g., Craik & McDowd, 1987 ; Smith, 1977 ). However, even when environmental support is afforded, age differences may be reduced but not entirely alleviated (e.g., Park & Shaw, 1992 ). There is a lengthy literature suggesting that the amount of mental effort required to effectively utilize environmental support determines its effectiveness in minimizing age deficits. For instance, fewer age deficits are observed in familiar situations, whereas novel experiences that require substantial self-initiated processing are more difficult for older adults (e.g., see Craik, 1994 ; Park & Gutchess, 2000 ).

Age-limited processing resources

The discovery that external cues could facilitate memory for older adults contributed to two further theoretical mechanisms of age-related memory decline. First was the hypothesis that older adults possessed fewer processing resources , also termed attentional resources and mental energy , and that the limited availability of processing resources served to restrict the quality and quantity of memory operations ( Craik & Byrd, 1982 ). Older adults’ limited resources were subsequently proposed to induce general processing that led to retention of broad semantic information but left a deficit in specificity (e.g., Rabinowitz & Ackerman, 1982 ; Rabinowitz, Craik, & Ackerman, 1982 ). Under conditions of divided attention (i.e., thought to mimic the inherent impoverished processing capacity of older adults), younger adults were argued to show more general encoding because they exhibited better memory with general as opposed to specific retrieval cues ( Rabinowitz et al., 1982 ). However, additional research questioned the validity of this general encoding theory due to inconsistent and incompatible findings (see Light, 1991 ). For example, Park, Puglisi, Smith, and Dudley (1987) varied the presence or absence of pictorial cues at encoding and retrieval and found that younger and older adults were equally aided by specific encoding and retrieval cues, indicating similar usage of contextual information, and that even when resources were limited by divided attention, older and younger adults still showed equivalent patterns of memory facilitation from specific cues.

Shortly following this general encoding debate, there was burgeoning interest in false memories and the increased susceptibility of older adults to remember information that was never presented but that was semantically similar to the studied material (e.g., Norman & Schacter, 1997 ). Despite the concerns about the validity of general encoding in describing verbal learning, the false memory literature, nevertheless, made it clear that older adults tended to rely on gist at the expense of detail.

The issue of whether age-limited processing resources served as an important mechanism of age-related memory deficits reached a head after publication of a chapter by Leah Light (1988) , where she criticized the circularity of the theory. The limited resource explanation was routinely invoked when older adults showed poor memory performance, yet there was no independent measure or evidence that processing resource was actually depleted. This argument triggered a paradigm shift in the study of cognitive aging. Researchers began focusing on the measurement of individual resource pools that participants possessed, examining candidate cognitive primitives that could be the instantiation of the “limited resource” (i.e., working memory, attention, processing speed, executive function).

This movement gained further momentum by the great success Timothy Salthouse had in using speed as an individual differences variable that accounted for considerable age-related variance on memory tasks ( Salthouse, 1985b , 1996 ). That is, processing speed could be the limited resource sought by researchers. Serendipitously, around the same time, Baddeley and Hitch (1974) developed their model of working memory, which posited two separate resource pools (visuospatial and verbal), which were controlled by a central executive system. This model became a prominent way to envision the structure of the mind. Working memory, which was a combination of storage and processing capacity, quickly became viewed as an excellent way to measure processing resource, independent of any specific experimental manipulations. Arthur Wingfield and Elizabeth Stine conducted some of the earliest work on this topic. They reported that older adults had poorer verbal working memory than younger adults, and that better verbal working memory was correlated with better vocabulary ( Wingfield, Stine, Lahar, & Aberdeen, 1988 ). In a follow-up study, they demonstrated that working memory capacity was also associated with simple text recall, confirming its potential explanatory power ( Stine & Wingfield, 1990 ). This research was followed by other articles that offered a broad perspective of additional cognitive mechanisms that could affect memory decline, such as frontal lobe function and executive control (e.g., Moscovitch & Winocur, 1995 ; West, 1996 ).

Inhibitory Theory of Memory Deficits With Age

Like Craik and Lockhart (1972) ’s article, Hasher and Zacks (1988) ’s theorizing on inhibition represented an important innovation that forever changed the way memory was conceptualized. Reminiscent of Rabbitt (1965) ’s finding that older adults had difficulty ignoring irrelevant information, Hasher and Zacks proposed the hypothesis that the ability to suppress attention to irrelevant thoughts within working memory was an important predictor of episodic memory. Inhibition, they argued, served to reduce the activation level of off-goal-path thoughts in working memory, and facilitated efficient memorial processing. Older adults were thought to be deficient in inhibition, and, consequently, to be easily distracted and to focus on contextual information at the expense of target information. Poor inhibition was posited to lead to a cluttered working memory that also had limited capacity for the entrance of new relevant information. Moreover, this “mental clutter” amplified competition during memory retrieval, which contributed to higher intrusion rates and heightened memorial interference in older adults. This inhibitory theory was very influential for the field, and it still plays a big role today.

Disuse, Motivational, and Other Noncognitive Theories

Although not the most prominent, certain noncognitive theories were also hypothesized to explain age-related memory deficits. It was suggested that perhaps older adults exhibited differential memory performance due to (a) lower motivation, (b) reduced memory self-efficacy, (c) greater test anxiety, (d) different performance goals, (e) greater time out of school, (f) lower formal education, and (g) poorer health. Although these factors may have influenced performance under certain conditions, there was little support that these noncognitive factors contributed entirely to age-related memory differences, especially because not all domains of memory exhibited similar deficits (i.e., implicit memory, semantic memory; see Burke & Light, 1981 ; Light, 1991 ) and because age effects were observed in samples matched on health and education (see Kausler, 1991 ). Consequently, these noncognitive hypotheses have not played a major role in theories of memory and aging.

Not all research was focused on mechanisms of memory differences. Some research sought to identify and explain age-invariant memory domains, that is, domains in which memory performance was equivalent for younger and older adults. In an early instantiation of this concept, Hasher and Zacks (1979) suggested that certain stimulus attributes were encoded automatically and, consequently, showed little effect of age. They reported that both younger and older adults had equivalent memory for the frequency and spatial location of words, whereas older adults demonstrated decreased memory for the words themselves. Later work suggested, however, that age differences could be observed in spatial memory if the tasks were sufficiently demanding (e.g., Park, Puglisi, & Lutz, 1982 ; Puglisi, Park, Smith, & Hill, 1985 ).

Semantic memory was also initially considered to be relatively age invariant. Darlene Howard and colleagues showed comparable semantic memory as a function of age (e.g., Howard, Lasaga, & McAndrews, 1980 ), as did Lars Nyberg, who found age differences for episodic but not semantic memory ( Nyberg, Bäckman, Erngrund, Olofsson, & Nilsson, 1996 ). Nevertheless, under conditions of higher demand, semantic memory deficits appeared. For example, Bowles and Poon (1985) found no evidence for age effects during easy lexical decisions, but when the task required active retrieval of words based on provided definitions, age differences were observed. Similarly, event-based prospective memory was originally claimed to be age invariant (e.g., Einstein & McDaniel, 1990 ), but later studies called this into question (e.g., Park, Hertzog, Kidder, Morrell, & Mayhorn, 1997 ). An analogous pattern of results occurred for picture memory. Early work in this domain suggested that old and young performed similarly on tests of complex picture recognition of real-world scenes ( Park, Puglisi, & Smith, 1986 ). However, later work showed that age effects were evident when the pictures were abstract ( Smith, Park, Cherry, & Berkovsky, 1990 ) and when they required active integration of target and context ( Park, Smith, Morrell, Puglisi, & Dudley, 1990 ).

There are two interesting points about the appealing possibility that some types of memory are protected from the effects of aging. First and most importantly, in retrospect, the question of whether age effects could be observed in each of these domains was not the most crucial question. Rather, the critical focus should have been whether the magnitude of the age effects differed for various types of memory (it did). Second, if so, what mechanism(s) contributed to the observed differences? For each of the domains just discussed, subsequent work yielded evidence that the demands of a given task were more important than the domain of the task, providing strong support for the notion of cognitive resource limitations being the basis for age-related memory dysfunction. That is, when the difficulty of these tasks was increased, age effects were apparent (i.e., Bowles & Poon, 1985 ; Puglisi et al., 1985 ). The findings from each of these memory domains posed perplexing results for current memory theories, and ultimately pointed toward lower resource requirements for tasks where age invariance was observed.

At this point, the field of memory and aging was confronted with multiple rich theoretical viewpoints (i.e., speed, limited resources, inhibition) that plausibly accounted for age-related memory decline. The research was dominated by experimental psychology paradigms that relied on systematic manipulation of variables and analysis of variance techniques to compare performance between younger and older adults. Based on this approach, troubling studies were presented by each theorist that could be explained by their mechanism of choice but not by other models. Discussion of these competing theories was at a fever pitch in the research community, with resolution of which theory was actually correct seemingly unresolvable. Auspiciously, an alternate data analysis approach emerged that simultaneously considered multiple possible antecedents of age-related memory decline and offered a fresh viewpoint—one that allowed for multiple theories to be correct.

There were a number of research groups working on theories of intelligence and aging led by luminaries such as K. Warner Schaie, John Horn, Earl Hunt, and Christopher Hertzog. They began developing nonexperimental, interrelated models of the aging mind that relied on individual differences, multiple constructs, and structural equation modeling to predict cognitive performance (e.g., Hertzog, 1985 ; Horn, 1989 ). Individual differences research made it increasingly obvious that the mechanisms underlying memory and aging were multifactorial, and these new modeling approaches allowed for complex multicausal views of age-related deficits in memory. For the next several years, many influential articles in cognitive aging would take a broad individual differences approach, measuring numerous mechanisms purported to underlie memory function and using structural equation models to predict memory performance.

One article that illustrates this transition was by Park and colleagues (1996) . In this article, multiple measures of speed and working memory were used to predict three types of memory that varied in their degree of environmental support: spatial recall (which was hypothesized to be more automatic), cued recall (which provided some environmental support), and free recall (which required the most mental effort). Speed and working memory constructs provided independent measures of two types of cognitive resource. The resulting model demonstrated that speed contributed to all three types of memory. However, working memory explained additional variance in the two more effortful memory tasks: cued recall and free recall. This result provided support both for the fundamental nature of speed (see also e.g., Lindenberger, Mayr, & Kliegl, 1993 ) and for the additional role that working memory contributed for demanding memory tasks. In a later article, Hedden, Lautenschlager, and Park (2005) demonstrated that both processing resource and knowledge were important mechanisms for successful memory, but their relative contributions varied as a function of task and age. Processing resource (i.e., speed + working memory) explained significant variance in free recall, cued recall, and verbal fluency, whereas knowledge was only related to verbal fluency and cued recall. Moreover, knowledge was more important for older than younger adults in explaining variance in cued recall, suggesting that older adults increasingly rely on knowledge to compensate for processing declines.

Overall, this multifaceted theoretical approach helped identify particular constructs that were most critical in accounting for large amounts of age-related variance in memory, while also delineating the inadequacy of single-mechanism theories of memory. Unquestionably, the construct that controlled the most age-related variance in cognition was speed, even when modeled with measures of resource (i.e., working memory) and measures of crystallized knowledge (i.e., vocabulary). Thus, along with the realization that multiple factors could influence observed age-related memory effects, speed was confirmed as perhaps the most important contributor to age differences in memory.

The next innovation that transformed the conceptualization of memory was the introduction of structural and functional neuroimaging. In the following sections, we briefly note how theories of memory and aging were influenced by neuroimaging.

The advent of magnetic resonance imaging allowed researchers to measure the volume of brain structures in older adults and to relate these measures to memory performance. Naftali Raz conducted influential work in this domain and demonstrated that older adults with smaller brain volume (i.e., hippocampal, parahippocampal) tended to have impaired explicit memory (e.g., Raz, Gunning-Dixon, Head, Dupuis, & Acker, 1998 ; although Raz & Rodrigue, 2006 note that these effects are modest). Analysis of white matter integrity further revealed poorer memory in older adults with white matter hyperintensities (e.g., DeCarli et al., 1995 ; Van Petten et al., 2004 ). Starting in the 2000s, researchers were additionally able to examine the quality of specific white matter tracts in the brain with diffusion tensor imaging, revealing some associations between white matter connectivity and memory in older adults (see Madden, Bennett, & Song, 2009 ). Thus, structural imaging methods enabled links to be drawn between brain structure and memory performance, helping identify physiological factors that were related to age-related memory differences.

Functional neuroimaging also offered insights into how the aging brain performed encoding and retrieval processes. One influential finding was that, under certain task conditions, older adults exhibited greater levels of neural activity than younger adults (e.g., Cabeza et al., 1997 ; Reuter-Lorenz et al., 2000 ). Given that most other age effects documented decrements in older versus younger groups, this somewhat counterintuitive finding led to the hypothesis that older adults might be recruiting additional neural resources to compensate for other neural inefficiency and to boost performance (e.g., Cabeza, 2002 ; Reuter-Lorenz & Cappell, 2008 ). Questions remain about whether this pattern of activation truly reflects compensation (e.g., see Kalpouzos, Persson, & Nyberg, 2012 ). Nevertheless, the ability to track changes in neural reactivity in response to task demands gave cognitive neuroscientists the ability to posit brain-based functional theories of memory and aging.

Other biological factors such as altered neurotransmission and vascular dysfunction have been proposed to contribute to age-related memory differences (e.g., see Bäckman, Nyberg, Lindenberger, Li, & Farde, 2006 ; Braver et al., 2001 ; Buckner, 2004 ). Moreover, the development of in vivo β-amyloid and tau imaging has allowed researchers to examine the relationship between neuropathological insults and memory, even in cognitively normal older adults. Greater levels of amyloid have been associated with worse episodic memory ( Hedden, Oh, Younger, & Patel, 2013 ), deficits in other domains of cognition (e.g., Rodrigue et al., 2012 ), and altered patterns of functional activation during memory encoding (e.g., Kennedy et al., 2012 ; Mormino et al., 2012 ). Although tau imaging is still very new, theories posit that greater levels of tau may also be linked with impaired memory (see Villemagne & Okamura, 2016 ). Ongoing work in this field will help characterize the neuropathology associated with memory performance and the transition from normal aging to Alzheimer’s disease.

In addition to continued research utilizing neuroimaging, which will help characterize biological underpinnings of memory impairment in old age, and to experimental psychology methods, which will continue to critically inform theories of memory and aging, additional focus has turned to applied methods for improving memory across the adult life span. Considerable attention has been given to intervention techniques (i.e., cognitive training, lifestyle adjustments) to boost cognitive function and delay the onset of memory decline. We note, however, that the field of cognitive training is still very young, and continued rigorous scientific studies are needed to determine the reliability, breadth, and duration of training effects.

One of the earliest and most influential intervention studies was an offshoot of the Seattle Longitudinal Study, when Sherry Willis imbedded a cognitive training program within the fifth cycle (see Schaie & Willis, 2010 ). This work provided the foundation for later training efforts, including the ACTIVE trial, which reported improvements in speed, reasoning, and memory after 10 sessions of targeted cognitive training ( Ball et al., 2002 ). Additional intervention studies were founded upon models of cognitive reserve, which posit that certain lifestyle and health factors can influence current cognition and longitudinal cognitive change (e.g., Reuter-Lorenz & Park, 2014 ; Tucker & Stern, 2011 ). As such, some interventions have been developed to determine whether increasing protective factors can heighten memory. Work from our own lab documented increases in episodic memory in older adults who learned complex new skills, such as digital photography or how to use an iPad ( Chan, Haber, Drew, & Park, 2014 ; Park et al., 2014 ). We envision that cognitive aging researchers will maintain their interest in training studies and that additional experimental work will help characterize the conditions under which neural plasticity can be exploited to improve memory.

Related to cognitive reserve is the concept of brain maintenance. Nyberg, Lovden, Riklund, Lindenberger, and Bäckman (2012) propose that successful agers maintain brains that are similar to those of young adults. Whereas cognitive reserve theories center more on characteristics that enable people to retain cognitive function in the face of neuropathological changes, brain maintenance considers what factors reduce functional, anatomical, and neurochemical brain changes in the first place. Future research will undoubtedly consider predictors of brain maintenance and how they relate to preservation of memory with age.

Neurostimulation has also garnered recent interest as a possible method to enhance memory. Researchers have begun examining the influence of transcranial direct current stimulation (tDCS), a noninvasive technique involving the passage of a small electrical current (i.e., 1–2 mA) through the brain, traveling between two electrodes placed on the surface of the head. Thus far, several studies have documented memorial benefits of tDCS in young adults and at least one older adult sample (see Coffman, Clark, & Parasuraman, 2014 ). Moreover, one study noted improved verbal recognition memory in Alzheimer’s patients following tDCS ( Ferrucci et al., 2008 ). If these findings prove to be replicable and reliable, it is conceivable that such brain stimulation may become popular. Needless to say, further research is needed to determine whether this is a viable technique to abate memory decline in older adults and whether the anatomical loci of these effects will help inform theories of memory and aging.

Finally, we predict that future research will place greater emphasis on genetic risk factors and epigenetic environmental triggers that predispose individuals for memory disturbances. At present, the field has identified certain genes with influences on cognition, such as APOE, BDNF, COMT, and KIBRA (e.g., Laukka et al., 2013 ), and evidence suggests that the effects of disadvantageous genes are amplified in old age (see Papenberg, Salami, Persson, Lindenberger, & Bäckman, 2015 ). Additional research will further elucidate these relationships, tracking genetic impacts on memory and cognition throughout the life span.

This brief review highlights major theoretical issues in the study of memory and aging and how they changed over time. No doubt this review is selective and somewhat subjective, but it offers organizing principles for how memory and aging research unfolded. One broad theme evident in the article is that, as research developed, a multitude of variables were recognized to contribute to age-related memory deficits. The shift from early single-mechanism views to involved multifactorial models is striking, and these intricate cognitive models mirror the vast complexity of the aging brain. Advances in neuroimaging clearly delineate that there are age differences in neural structure and function that contribute to memory performance, and contemporary theorists are becoming increasingly interested in using both neural and behavioral measures to differentiate between pathological and normal age-related decline in memory. Additionally, an increased focus has been placed on identifying practical techniques to maintain memory function for life. This research on memory preservation has relied heavily on the rich depth of information generated in earlier research about basic memory function. The investment in memory and aging research over the last 50 years has provided the knowledge base needed to develop increasingly effective interventions and to identify early markers of pathological aging. Continued and sustained investment in this critically important research domain is likely to yield advances that have the potential to enhance the quality of life of both sufferers of age-related memory disorders and their families.

Preparation of this manuscript was supported by the National Institutes of Health Grant 5R37AG-006265-29 to D. C. Park. S. B. Festini is supported by the Aging Mind Foundation.

Arenberg D . ( 1967 ). Age differences in retroaction . Journal of Gerontology , 22 , 88 – 91 .

Google Scholar

Bäckman L. Nyberg L. Lindenberger U. Li S. C. , & Farde L . ( 2006 ). The correlative triad among aging, dopamine, and cognition: current status and future prospects . Neuroscience and Biobehavioral Reviews , 30 , 791 – 807 . doi: 10.1016/j.neubiorev.2006.06.005

Baddeley A. D. , & Hitch G . ( 1974 ). Working memory . In Bower G. H. (Ed.), The psychology of learning and motivation (Vol. 8 , pp. 47 – 89 ). New York, NY : Academic Press .

Google Preview

Ball K. Berch D. B. Helmers K. F. Jobe J. B. Leveck M. D. Marsiske M. , … Willis S. L . ( 2002 ). Effects of cognitive training interventions with older adults: A randomized controlled trial . Journal of the American Medical Association , 288 , 2271 – 2281 . doi: 10.1001/jama.288.18.2271

Birren J. E . ( 1965 ). Age changes in speed of behavior: Its central nature and physiological correlates . In Welford A. T. Birren J. E. (Eds.), Behavior, aging and the nervous system: Biological determinants of speed and behavior (pp. 191 – 216 ). Charles C. Thomas: Springfield, IL .

Birren J. E. Woods A. M. , & Williams M. V . ( 1980 ). Behavioral slowing with age: Causes, organization, and consequences . In Poon L. W. (Ed.), Aging in the 1980s: Psychological issues (pp. 293 – 308 ). Washington, DC : American Psychological Association .

Bowles N. L. , & Poon L. W . ( 1985 ). Aging and retrieval of words in semantic memory . Journal of Gerontology , 40 , 71 – 77 . doi: 10.1093/geronj/40.1.71

Braver T. S. Barch D. M. Keys B. A. Carter C. S. Cohen J. D. Kaye J. A. , … Reed B. R . ( 2001 ). Context processing in older adults: Evidence for a theory relating cognitive control to neurobiology in healthy aging . Journal of Experimental Psychology: General , 130 , 746 – 763 . doi: 10.1037/0096-3445.130.4.746

Brinley J. F . ( 1965 ). Cognitive sets, speed and accuracy of performance in the elderly . In Welford A. T. Birren J. E. (Eds.), Behavior, aging and the nervous system (pp. 114 – 149 ). Springfield, IL : Thomas .

Buckner R. L . ( 2004 ). Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate . Neuron , 44 , 195 – 208 . doi: 10.1016/j.neuron.2004.09.006

Burke D. M. , & Light L. L . ( 1981 ). Memory and aging: The role of retrieval processes . Psychological Bulletin , 90 , 513 – 546 . doi: 10.1037/0033-2909.90.3.513

Cabeza R . ( 2002 ). Hemispheric asymmetry reduction in older adults: The HAROLD model . Psychology and Aging , 17 , 85 – 100 . doi: 10.1037//0882-7974.17.1.85

Cabeza R. Grady C. L. Nyberg L. McIntosh A. R. Tulving E. Kapur S. , … Craik F. I. M . ( 1997 ). Age-related differences in neural activity during memory encoding and retrieval: A positron emission tomography study . Journal of Neuroscience , 17 , 391 – 400 .

Cerella J . ( 1985 ). Information processing rates in the elderly . Psychological Bulletin , 98 , 67 – 83 . doi: 10.1037/0033-2909.98.1.67

Cerella J . ( 1990 ). Aging and information-processing rate . In Birren J. E. Schaie K. W. (Eds.), Handbook of the psychology of aging (3rd ed., pp. 201 – 221 ). New York, NY : Academic Press .

Chan M. Y. Haber S. Drew L. M. , & Park D. C . ( 2014 ). Training older adults to use tablet computers: Does it enhance cognitive function? The Gerontologist . doi: 10.1093/geront/gnu057

Coffman B. A. Clark V. P. , & Parasuraman R . ( 2014 ). Battery powered thought: Enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation . Neuroimage , 85 , 895 – 908 . doi: 10.1016/j.neuroimage.2013.07.083

Cokayne K . ( 2003 ). Experiencing old age in ancient Rome . New York, NY : Routledge .

Corlan A. D . ( 2004 ). Medline trend: Automated yearly statistics of PubMed results for any query . Retrieved from http://dan.corlan.net/medline-trend.html

Craik F. I. M . ( 1986 ). A functional account of age differences in memory . In Klix F. Hagendorf H. (Eds.), Human memory and cognitive capabilities: Mechanisms and performances (pp. 409 – 421 ). North Holland, the Netherlands : Elsevier .

Craik F. I. M . ( 1994 ). Memory changes in normal aging . Current Directions in Psychological Science , 3 , 155 – 158 .

Craik F. I. M. , & Byrd M . ( 1982 ). Aging and cognitive deficits: The role of attentional resources . In Craik F. I. M. Trehub S. (Eds.), Aging and cognitive processes (pp. 191 – 211 ). New York, NY : Plenum .

Craik F. I. M. , & Lockhart R. S . ( 1972 ). Levels of processing: A framework for memory research . Journal of Verbal Learning and Verbal Behavior , 11 , 671 – 684 . doi: 10.1016/S0022-5371(72)80001-X

Craik F. I. M. , & McDowd J. M . ( 1987 ). Age differences in recall and recognition . Journal of Experimental Psychology: Learning, Memory, and Cognition , 13 , 474 – 479 . doi: 10.1037/0278-7393.13.3.474

DeCarli C. Murphy D. G. Tranh M. Grady C. L. Haxby J. V. Gillette J. A. , … Rapoport S. I . ( 1995 ). The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults . Neurology , 45 , 2077 – 2084 .

Einstein G. O. , & McDaniel M. A . ( 1990 ). Normal aging and prospective memory . Journal of Experimental Psychology: Learning, Memory, and Cognition , 16 , 717 – 726 . doi: 10.1037/0278-7393.16.4.717

Erber J. T . ( 1974 ). Age differences in recognition memory . Journal of Gerontology , 29 , 177 – 181 . doi: 10.1093/geronj/29.2.177

Ferrucci R. Mameli F. Guidi I. Mrakic-Sposta S. Vergari M. Marceglia S. , … Priori A . ( 2008 ). Transcranial direct current stimulation improves recognition memory in Alzheimer disease . Neurology , 71 , 493 – 498 . doi: 10.1212/01.wnl.0000317060.43722.a3

Harwood E. , & Naylor G. F. K . ( 1969 ). Recall and recognition in elderly and young subjects . Australian Journal of Psychology , 21 , 251 – 257 . doi: 10.1080/00049536908257794

Hasher L. , & Zacks R. T . ( 1979 ). Automatic and effortful processes in memory . Journal of Experimental Psychology: General , 108 , 356 – 388 . doi: 10.1037/0096-3445.108.3.356

Hasher L. , & Zacks R. T . ( 1988 ). Working memory, comprehension, and aging: a review and a new view . The Psychology of Learning and Motivation , 50 , 193 – 225 . doi: 10.1016/S0079-7421(08)60041-9

Hedden T. Lautenschlager G. , & Park D. C . ( 2005 ). Contributions of processing ability and knowledge to verbal memory tasks across the adult life-span . The Quarterly Journal of Experimental Psychology A , 58 , 169 – 190 . doi: 10.1080/02724980443000179

Hedden T. Oh H. Younger A. P. , & Patel T. A . ( 2013 ). Meta-analysis of amyloid-cognition relations in cognitively normal older adults . Neurology , 80 , 1341 – 1348 .

Hertzog C . ( 1985 ). An individual differences perspective: Implications for cognitive research in gerontology . Research on Aging , 7 , 7 – 45 . doi: 10.1177/0164027585007001002

Horn J . ( 1989 ). Models of intelligence . In Linn R. L. (Ed.), Intelligence: Measurement, theory, and public policy (pp. 29 – 73 ). Chicago, IL : University of Illinois Press .

Howard D. V. Lasaga M. I. , & McAndrews M. P . ( 1980 ). Semantic activation during memory encoding across the adult life span . Journal of Gerontology , 35 , 884 – 890 . doi: 10.1093/geronj/35.6.884

Hulicka I. M. , & Grossman J. L . ( 1967 ). Age-group comparisons for the use of mediators in paired-associate learning . Journal of Gerontology , 22 , 46 – 51 .

Hyde T. S. , & Jenkins J. J . ( 1969 ). Differential effects of incidental tasks on the organization of recall of a list of highly associated words . Journal of Experimental Psychology , 82 , 472 – 481 . doi: 10.1037/h0028372

Kalpouzos G. Persson J. , & Nyberg L . ( 2012 ). Local brain atrophy accounts for functional activity differences in normal aging . Neurobiol Aging , 33 , 623.e1 – 623.e13 . doi: 10.1016/j.neurobiolaging.2011.02.021

Kausler D. H . ( 1970 ). Retention-forgetting as a nomological network for developmental research . In Goulet L. R. Baltes P. B. (Eds.), Life-span developmental psychology: Research and theory . New York, NY : Academic Press .

Kausler D. H . ( 1991 ). Experimental psychology, cognition, and human aging (2nd ed.). New York, NY : Springer-Verlag .

Kennedy K. M. Rodrigue K. M. Devous M. D. Sr Hebrank A. C. Bischof G. N. , & Park D. C . ( 2012 ). Effects of beta-amyloid accumulation on neural function during encoding across the adult lifespan . Neuroimage , 62 , 1 – 8 . doi: 10.1016/j.neuroimage.2012.03.077

Laukka E. J. Lövdén M. Herlitz A. Karlsson S. Ferencz B. Pantzar A. , … Bäckman L . ( 2013 ). Genetic effects on old-age cognitive functioning: A population-based study . Psychology and Aging , 28 , 262 – 274 . doi: 10.1037/a0030829

Light L. L . ( 1988 ). Language and aging: Competence versus performance . In Birren J. E. Bengtson V. L. (Eds.), Emergent theories of aging (pp. 177 – 213 ). New York, NY : Springer .

Light L. L . ( 1991 ). Memory and aging: Four hypotheses in search of data . Annual Review of Psychology , 42 , 333 – 376 . doi: 10.1146/annurev.ps.42.020191.002001

Lindenberger U. Mayr U. , & Kliegl R . ( 1993 ). Speed and intelligence in old age . Psychol Aging , 8 , 207 – 220 . doi: 10.1037/0882-7974.8.2.207

Madden D. J. Bennett I. J. , & Song A. W . ( 2009 ). Cerebral white matter integrity and cognitive aging: Contributions from diffusion tensor imaging . Neuropsychology Review , 19 , 415 – 435 . doi: 10.1007/s11065-009-9113-2

Mormino E. C. Brandel M. G. Madison C. M. Marks S. Baker S. L. , & Jagust W. J . ( 2012 ). A-beta deposition in aging is associated with increases in brain activation during successful memory encoding . Cerebral Cortex , 22 , 1813 – 1823 . doi: 10.1093/cercor/bhr255

Moscovitch M. , & Winocur G . ( 1995 ). Frontal lobes, memory, and aging . Annals of the New York Academy of Sciences , 769 , 119 – 150 . doi: 10.1111/j.1749–6632.1995.tb38135.x

Norman K. A. , & Schacter D. L . ( 1997 ). False recognition in younger and older adults: Exploring the characteristics of illusory memories . Memory & Cognition , 25 , 838 – 848 . doi: 10.3758/BF03211328

Nyberg L. Bäckman L. Erngrund K. Olofsson U. , & Nilsson L. G . ( 1996 ). Age differences in episodic memory, semantic memory, and priming: Relationships to demographic, intellectual, and biological factors . The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences , 51 , P234 – P240 . doi: 10.1093/geronb/51B.4.P234

Nyberg L. Lovden M. Riklund K. Lindenberger U. , & Bäckman L . ( 2012 ). Memory aging and brain maintenance . Trends in Cognitive Sciences , 16 , 292 – 305 . doi: 10.1016/j.tics.2012.04.005

Papenberg G. Salami A. Persson J. Lindenberger U. , & Bäckman L . ( 2015 ). Genetics and functional imaging: Effects of APOE, BDNF, COMT, and KIBRA in aging . Neuropsychology Review , 25 , 47 – 62 . doi: 10.1007/s11065-015-9279-8

Park D. C. , & Gutchess A. H . ( 2000 ). Cognitive aging and everyday life . In Park D. C. Schwarz N. (Eds.), Cognitive aging: A primer . Philadelphia, PA : Psychology Press .

Park D. C. Hertzog C. Kidder D. P. Morrell R. W. , & Mayhorn C. B . ( 1997 ). Effect of age on event-based and time-based prospective memory . Psychology and Aging , 12 , 314 – 327 . doi: 10.1037/0882-7974.12.2.314

Park D. C. Lodi-Smith J. Drew L. Haber S. Hebrank A. Bischof G. N. , & Aamodt W . ( 2014 ). The impact of sustained engagement on cognitive function in older adults: The Synapse Project . Psychological Science , 25 , 103 – 112 . doi: 10.1177/0956797613499592

Park D. C. Puglisi J. T. , & Lutz R . ( 1982 ). Spatial memory in older adults: Effects of intentionality . Journal of Gerontology , 37 , 330 – 335 . doi: 10.1093/geronj/37.3.330

Park D. C. Puglisi J. T. , & Smith A. D . ( 1986 ). Memory for pictures: Does an age-related decline exist? Journal of Psychology and Aging , 1 , 11 – 17 . doi: 10.1037/0882-7974.1.1.11

Park D. C. Puglisi J. T. Smith A. D. , & Dudley W. N . ( 1987 ). Cue utilization and encoding specificity recognition by older adults . Journal of Gerontology , 42 , 423 – 425 . doi: 10.1093/geronj/42.4.423

Park D. C. Puglisi T. , & Sovacool M . ( 1983 ). Memory for pictures, words, and spatial location in older adults: Evidence for pictorial superiority . Journal of Gerontology , 38 , 582 – 588 . doi: 10.1093/geronj/38.5.582

Park D. C. , & Shaw R. J . ( 1992 ). Effect of environmental support on implicit and explicit memory in younger and older adults . Psychology and Aging , 7 , 632 – 642 . doi: 10.1037/0882-7974.7.4.632

Park D. C. Smith A. D. Lautenschlager G. Earles J. L. Frieske D. Zwahr M. , & Gaines C. L . ( 1996 ). Mediators of long-term memory performance across the life span . Psychology and Aging , 11 , 624 – 637 . doi: 10.1037/0882-7974.11.4.621

Park D. C. Smith A. D. Morrell R. W. Puglisi J. T. , & Dudley W. N . ( 1990 ). Effects of contextual integration on recall of pictures by older adults . Journal of Gerontology , 45 , P52 – 57 . doi: 10.1093/geronj/45.2.P52

Puglisi J. T. Park D. C. Smith A. D. , & Hill G. W . ( 1985 ). Memory for two types of spatial location: Effects of instructions, age, and format . American Journal of Psychology , 98 , 101 – 118 . doi: 10.2307/1422770

Rabbitt P . ( 1965 ). An age-decrement in the ability to ignore irrelevant information . Journal of Gerontology , 20 , 233 – 238 . doi: 10.1093/geronj/20.2.233

Rabinowitz J. C. , & Ackerman B. P . ( 1982 ). General encoding of episodic events by elderly adults . In Craik F. I. M. Trehub S. (Eds.), Advances in the study of communication and affect: Aging and cognitive processes (Vol. 8 , pp. 145 – 154 ). New York, NY : Plenum Press .

Rabinowitz J. C. Craik F. I. M. , & Ackerman B. P . ( 1982 ). A processing resource account of age differences in recall . Canadian Journal of Psychology , 36 , 325 – 344 .

Raz N. Gunning-Dixon F. M. Head D. Dupuis J. H. , & Acker J. D . ( 1998 ). Neuroanatomical correlates of cognitive aging: Evidence from structural magnetic resonance imaging . Neuropsychology , 12 , 95 – 114 . doi: 10.1037/0894-4105.12.1.95

Raz N. , & Rodrigue K. M . ( 2006 ). Differential aging of the brain: Patterns, cognitive correlates and modifiers . Neuroscience & Biobehavioral Reviews , 30 , 730 – 748 . doi: 10.1016/j.neubiorev.2006.07.001

Reuter-Lorenz P. A. , & Cappell K. A . ( 2008 ). Neurocognitive aging and the compensation hypothesis . Current Directions in Psychological Science , 17 , 177 – 182 . doi:10.1111/j.1467-8721.2008.00570.x

Reuter-Lorenz P. A. Jonides J. Smith E. E. Hartley A. Miller A. Marshuetz C. , & Koeppe R. A . ( 2000 ). Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET . Journal of Cognitive Neuroscience , 12 , 174 – 187 . doi: 10.1162/089892900561814

Reuter-Lorenz P. A. , & Park D. C . ( 2014 ). How does it STAC up? Revisiting the scaffolding theory of aging and cognition . Neuropsychology Review , 24 , 355 – 370 . doi: 10.1007/s11065-014-9270-9

Rodrigue K. M. Kennedy K. M. Devous M. D. Sr Rieck J. R. Hebrank A. C. Diaz-Arrastia R. , … Park D. C . ( 2012 ). β-Amyloid burden in healthy aging: Regional distribution and cognitive consequences . Neurology , 78 , 387 – 395 . doi: 10.1212/WNL.0b013e318245d295

Ruch F. L . ( 1934 ). The differentiative effects of age upon human learning . The Journal of General Psychology , 11 , 261 – 286 . doi: 10.1080/00221309.1934.9917838

Salthouse T. A . ( 1985 a). Speed of behavior and its implications for cognition . In Birren J. E. Schaie K. W. (Eds.), Handbook of the psychology of aging (2nd ed.). New York, NY : Van Nostrand Reinhold .

Salthouse T. A . ( 1985 b). A theory of cognitive aging . New York, NY : North-Holland .

Salthouse T. A . ( 1996 ). The processing-speed theory of adult age differences in cognition . Psychological Review , 103 , 403 – 428 . doi: 10.1037/0033-295X.103.3.403

Schaie K. W. , & Willis S. L . ( 2010 ). The Seattle Longitudinal Study of adult cognitive development . ISSBD Bulletin , 57 , 24 – 29 .

Schonfield D . ( 1965 ). Memory changes with age . Nature , 208 , 918 . doi: 10.1038/208918a0

Smith A. D . ( 1977 ). Adult age differences in cued recall . Developmental Psychology , 13 , 326 – 331 . doi: 10.1037/0012-1649.13.4.326

Smith A. D. Park D. C. Cherry K. , & Berkovsky K . ( 1990 ). Age differences in memory for concrete and abstract pictures . Journal of Gerontology , 45 , P205 – P209 . doi: 10.1093/geronj/45.5.P205

Stine E. A. L. , & Wingfield A . ( 1990 ). How much do working memory deficits contribute to age differences in discourse memory? European Journal of Cognitive Psychology , 2 , 289 – 304 . doi: 10.1080/09541449008406209

Tucker A. M. , & Stern Y . ( 2011 ). Cognitive reserve in aging . Current Alzheimer Research , 8 , 354 – 360 .

Van Petten C. Plante E. Davidson P. S. Kuo T. Y. Bajuscak L. , & Glisky E. L . ( 2004 ). Memory and executive function in older adults: Relationships with temporal and prefrontal gray matter volumes and white matter hyperintensities . Neuropsychologia , 42 , 1313 – 1335 . doi: 10.1016/j.neuropsychologia.2004.02.009

Villemagne V. L. , & Okamura N . ( 2016 ). Tau imaging in the study of ageing, Alzheimer’s disease, and other neurodegenerative conditions . Current Opinion in Neurobiology , 36 , 43 – 51 . doi: 10.1016/j.conb.2015.09.002

West R. L . ( 1996 ). An application of prefrontal cortex function theory to cognitive aging . Psychological Bulletin , 120 , 272 – 292 . doi: 10.1037/0033-2909.120.2.272

Willoughby R. R . ( 1929 ). Incidental learning . Journal of Educational Psychology , 20 , 671 – 682 . doi: 10.1037/h0071404

Wingfield A. Stine E. A. Lahar C. J. , & Aberdeen J. S . ( 1988 ). Does the capacity of working memory change with age? Experimental Aging Research , 14 , 103 – 107 . doi: 10.1080/03610738808259731

Author notes

Email alerts

Citing articles via, looking for your next opportunity.

• Recommend to Your Librarian

Affiliations

• Online ISSN 1758-5368
• Copyright © 2024 The Gerontological Society of America
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Multi-Store Memory Model: Atkinson and Shiffrin

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the Multi-Store Model?

• The multi-store model is an explanation of memory proposed by Atkinson and Shiffrin which assumes there are three unitary (separate) memory stores, and that information is transferred between these stores in a linear sequence.
• The three main stores are the sensory memory, short-term memory (STM) and long-term memory (LTM).
• Each of the memory stores differs in the way information is processed (encoding), how much information can be stored (capacity), and for how long (duration).
• Information passes from store to store in a linear way, and has been described as an information processing model (like a computer) with an input, process and output.
• Information is detected by the sense organs and enters the sensory memory , which stores a fleeting impression of sensory stimuli. If attended to this information enters the STM and if the information is given meaning (elaborative rehearsal) it is passed on to the LTM

The multi-store model of memory (also known as the modal model) was proposed by Richard Atkinson and Richard Shiffrin (1968) and is a structural model. They proposed that memory consisted of three stores: a sensory register, short-term memory (STM) and long-term memory (LTM).

The Memory Stores

Each store is a unitary structure and has its own characteristics in terms of encoding, capacity and duration.

Encoding is the way information is changed so that it can be stored in the memory. There are three main ways in which information can be encoded (changed):

1. visual (picture),

2. acoustic (sound),

3. semantic (meaning).

Capacity concerns how much information can be stored.

Duration refers to the period of time information can last in the memory stores.

Sensory Memory

• Duration: ¼ to ½ second

• Capacity: all sensory experience (v. larger capacity)

• Encoding: sense specific (e.g. different stores for each sense)

The sensory stores are constantly receiving information but most of this receives no attention and remains in the sensory register for a very brief period.

In the sensory memory store , information arrives from the 5 senses such as sight (visual information), sounds and touch. The sensory memory store has a large capacity but a very brief duration, it can encode information from any of the senses and most of the information is lost through decay.

Attention is the first step in remembering something, if a person’s attention is focused on one of the sensory stores then the data is transferred to STM.

Short Term Memory

• Duration: 0-18 seconds

• Capacity: 7 +/- 2 items

• Encoding: mainly auditory

The short-term memory store has a duration of up to 30 seconds, has a capacity of 7+/-2 chunks and mainly encodes information acoustically. Information is lost through displacement or decay.

Maintenance rehearsal is the process of verbally or mentally repeating information, which allows the duration of short-term memory to be extended beyond 30 seconds. An example of maintenance rehearsal would be remembering a phone number only long enough to make the phone call.

This type of rehearsal usually involves repeating information without thinking about its meaning or connecting it to other information.

Continual rehearsal “regenerates” or “renews” the information in the memory trace, thus making it a stronger memory when transferred to the Long Term store.

If maintenance rehearsal (repetition) does not occur, then information is forgotten, and lost from short term memory through the processes of displacement or decay.

Long Term Memory

• Duration: Unlimited

• Capacity: Unlimited

• Encoding: Mainly Semantic (but can be visual and auditory)

Long-term memory store has unlimited capacity and duration and encodes information semantically. Information can be recalled from LTM back into the STM when it is needed.

If the information is given meaning (elaborative rehearsal) it is passed on to the LTM.

Elaborative rehearsal involves the process of linking new information in a meaningful way with information already stored in long-term memory. For example,

you could learn the lines in a play by relating the dialogue and behavior of your character to similar personal experiences you remember.

Elaborative rehearsal is more effective than maintenance rehearsal for remembering new information as it helps to ensure that information is encoded well. It is a deeper level of information-processing.

Key Studies

Glanzer and Cunitz showed that when participants are presented with a list of words, they tend to remember the first few and last few words and are more likely to forget those in the middle of the list, i.e. the serial position effect.

This supports the existence of separate LTM and STM stores because they observed a primacy and recency effect.

Words early on in the list were put into long term memory (primacy effect) because the person has time to rehearse the word, and words from the end went into short term memory (recency effect).

Other compelling evidence to support this distinction between STM and LTM is the case of KF (Shallice & Warrington, 1977) who had been in a motorcycle crash where he had sustained brain damage.

His LTM seemed to be unaffected but he was only able to recall the last bit of information he had heard in his STM.

Critical Evaluation

One strength of the multistore model is that is gives us a good understanding of the structure and process of the STM. This is good because this allows researchers to expand on this model.

This means researchers can do experiments to improve on this model and make it more valid and they can prove what the stores actually do. Therefore, the model is influential as it has generated a lot of research into memory.

Many memory studies provide evidence to support the distinction between STM and LTM (in terms of encoding, duration and capacity). The model can account for primacy & recency effects .

The case of HM also supports the MSM as he was unable to encode new long-term memories after surgery during which his hippocampus was removed but his STM was unaffected.

He has remembered little of personal (death of mother and father) or public events (Watergate, Vietnam War) that have occurred over the last 45 years. However his short-term memory remains intact.This supports the view that the LTM and the STM are two separate stores.

The model is oversimplified, in particular when it suggests that both short-term and long-term memory each operate in a single, uniform fashion.  We now know is this not the case.

It has now become apparent that both short-term and long-term memory are more complicated that previously thought.  For example, the Working Model of Memory proposed by Baddeley and Hitch (1974) showed that short term memory is more than just one simple unitary store and comprises different components (e.g. central executive, Visuospatial etc.).

In the case of long-term memory, it is unlikely that different kinds of knowledge, such as remembering how to play a computer game, the rules of subtraction and remembering what we did yesterday are all stored within a single, long-term memory store.

Indeed different types of long-term memory have been identified, namely episodic (memories of events), procedural (knowledge of how to do things) and semantic (general knowledge).

Rehearsal is considered a too simple explanation to account for the transfer of information from STM to LTM. For instance, the model ignores factors such as motivation, effect and strategy (e.g. mnemonics) which underpin learning.

Also, rehearsal is not essential to transfer information into LTM. For example, why are we able to recall information which we did not rehearse (e.g. swimming) yet unable to recall information which we have rehearsed (e.g. reading your notes while revising).

Therefore, the role of rehearsal as a means of transferring from STM to LTM is much less important than Atkinson and Shiffrin (1968) claimed in their model.

The models main emphasis was on structure and tends to neglect the process elements of memory (e.g. it only focuses on attention and maintenance rehearsal). For example, elaboration rehearsal leads to recall of information than just maintenance rehearsal.

Elaboration rehearsal involves a more meaningful analysis (e.g. images, thinking, associations etc.) of information and leads to better recall. For example, giving words a meaning or linking them with previous knowledge. These limitations are dealt with by the levels of processing model (Craik, & Lockhart, 1972).

Note: although rehearsal was initially described by Atkinson and Shiffrin as maintenance rehearsal (repetition of information), Shiffrin later suggested that rehearsal could be elaborative (Raaijmakers, & Shiffrin, 2003).

The multi store model has been criticized for being a passive/one way/linear model.

Atkinson, R. C., & Shiffrin, R. M. (1968). Chapter: Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.

Baddeley, A .D., & Hitch, G. (1974). Working memory. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.

Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal behavior, 11, 671-684.

Raaijmakers, J.G.W. & Shiffrin, R.M. (2003). Models versus descriptions: Real differences and langiage differences . behavioral and Brain Sciences , 26, 753.

Shallice, T., & Warrington, E. K. (1977). Auditory-verbal short-term memory impairment and conduction aphasia. Brain and Language, 4(4) , 479-491.

Long-Standing Alzheimer’s Mystery Solved: New Study Reveals Two Paths of Brain Aging

Researchers have identified distinct cellular pathways in aging brains that lead to Alzheimer’s , offering new possibilities for personalized treatments to delay or prevent the disease.

A recent study has solved a long-standing mystery in aging research: Is Alzheimer’s disease-related dementia a form of accelerated aging, or does it represent a different pathway toward brain aging? In a collaborative international effort, researchers mapped 1.65 million cells from 437 aging brains and identified distinct cellular changes. Their findings revealed two divergent paths of brain aging—one leading to Alzheimer’s disease and the other to a healthier form of brain aging.

They also point to specific cell signatures predicted to advance disease once they appear in the aging brain. These findings offer new insights into the disease’s development and how it is different from healthy brain aging. As these changes in brain cells may occur many years prior to the development of symptoms and memory loss, this discovery opens the door to personalized prevention medicine that could alter disease progression and improve outcomes for individuals at risk.

A new study, published in Nature , led by an international team, including Dr. Naomi Habib and Gilad Green from the Hebrew University of Jerusalem , Dr. Philip L. De Jager and Dr. Vilas Menon from Columbia University Irving Medical Center , Dr. David Bennett from Rush Alzheimer’s Disease Center and Dr. Hyun-Sik Yang from Harvard Medical School , has uncovered crucial insights into the cellular dynamics that contribute to brain aging and the cellular events leading to the onset and progression of Alzheimer’s disease (AD).

By creating one of the biggest resources in the brain aging field, mapping over 1.65 million cells from 437 aging brains, and developing new machine learning (AI) algorithms, the research team has revealed distinct cellular paths in brain aging, providing a foundation for personalized therapeutic development targeting Alzheimer’s disease.

Mapping Brain Aging: A Closer Look at Brain Cells

This study took an in-depth approach to map the brain’s cellular environment, analyzing a unique dataset of 1.65 million single-nucleus RNA sequencing profiles from the prefrontal cortex of 437 older adults in the ROSMAP cohort at Rush University in Chicago, IL, USA. With this large dataset, researchers were able to pinpoint specific glial and neuronal cell groups linked to traits related to Alzheimer’s disease (AD).

Moreover, the study zeroed in on the complex dynamics within the brain cells along the progression of aging and disease, using a new algorithm called BEYOND to model these dynamics. This approach revealed two distinct paths of brain aging, each marked by gradual coordinated changes in distinct groups of cells, which the researchers termed as “cellular communities” in the brain.

Interestingly, they showed that one of these paths leads to Alzheimer’s disease, gradually leading to dementia – featured by memory loss and cognitive decline, while the other represents a healthier, non-Alzheimer’s form of brain aging. The researchers predict that these cellular changes, that start early – before any clinical signs of dementia – are actively determining the fate of the aging brain and the progression of the disease.

Important Discoveries in Alzheimer’s Disease

Alzheimer’s disease is characterized by hallmark brain pathologies, with the classical Amyloid theory of AD describing the cascade of events thought to follow the progression of the disease – starting with the accumulation of amyloid-β plaques, which then lead to the accumulation of toxic neurofilament tangles, eventually leading to substantial neuronal damage and symptoms of clinical dementia.

Glial cells, such as microglia and astrocytes, are supportive cells that are critical for the correct function of the brain and of neuronal cells, yet have only been recently suggested to take part in the cascade leading to Alzheimer’s disease. For example, a previous article, published in Nature Neuroscience in 2023 led by the same team with Anael Cain a PhD student in the Habib lab, laid the scientific foundation for the findings on specific cellular communities and glial cells related to Alzheimer’s disease. A key discovery from this study is the identification of specific glial cells predicted to contribute to the progression of the disease.

The current study uncovered two different subsets of microglial cells, both linked to altered lipid metabolism: one was predicted by the team to drive the buildup of amyloid-β plaques, the initial hallmark pathology of Alzheimer’s disease, while the other is predicted to drive the later buildup of neurofilament tangles. The team also highlighted a group of astrocyte cells that influence directly cognitive decline, shedding more light on the complex interactions between different brain cells in the progression of Alzheimer’s disease, and highlighting the key role that glial cells are taking in the progression of the disease.

Impact on Personalized Treatment Development

“The insights from this research provide a fresh understanding of how Alzheimer’s disease develops, from the very early stages, which was not possible to measure without our large dataset and unique algorithmic approach,” said Dr. Habib, “by identifying the specific cells involved in each unique path of brain aging, Alzheimer’s and alternative aging, we paved the way to early identification of people at risk of Alzheimer’s disease and for creating targeted treatments for each form of brain aging to promote healthy aging.”

The findings lay a cellular foundation for understanding the different paths leading to Alzheimer’s. This knowledge is vital for developing personalized treatments that can act at the cellular level, potentially changing the course of the disease.

Reference: “Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease” by Gilad Sahar Green, Masashi Fujita, Hyun-Sik Yang, Mariko Taga, Anael Cain, Cristin McCabe, Natacha Comandante-Lou, Charles C. White, Anna K. Schmidtner, Lu Zeng, Alina Sigalov, Yangling Wang, Aviv Regev, Hans-Ulrich Klein, Vilas Menon, David A. Bennett, Naomi Habib and Philip L. De Jager, 28 August 2024, Nature . DOI: 10.1038/s41586-024-07871-6

Related Articles

These signs of memory loss could actually predict alzheimer’s brain changes, are we evolving new research reveals that human brains are getting larger, scientists discover new telltale sign of alzheimer’s, certain protein may predict mild cognitive impairment years before symptoms, losing your sense of smell – a potential warning of future memory and thinking problems, early menopause may increase risk of alzheimer’s disease, alleviating symptoms: brain stimulation could help treat alzheimer’s disease, alzheimer’s breakthrough: new therapeutic target found, a diabetes drug could protect against alzheimer’s.

Save my name, email, and website in this browser for the next time I comment.

Type above and press Enter to search. Press Esc to cancel.

Eating Eggs May Lower Your Risk Of Cognitive Decline, New Research Says

Dietitians credit the food's "anti-inflammatory benefits."

Women's Health may earn commission from the links on this page, but we only feature products we believe in. Why Trust Us?

About two out of three Americans have some level of cognitive decline by the age of 70. Given that, it’s understandable to want to keep your mind sharp as you age. Now, a new study suggests that eating eggs may help.

The study, which was published in the journal Nutrients , found a link between eating eggs and a lowered risk of cognitive decline as you age. It seems random, but given how easily accessible the ingredient is, this is definitely worth paying attention to. Here’s what the study found—plus, how you can incorporate more eggs into your diet.

Meet the experts : Scott Keatley, RD, is co-owner of Keatley Medical Nutrition Therapy . Jessica Cording, RD , is the author of The Little Book of Game-Changers . Keri Gans, RD , is the author of The Small Change Diet .

What did the study find?

For the study, researchers analyzed data from 890 adults over 55 who participated in a long-term observational study called the Rancho Bernardo Study. The researchers looked at how often the participants ate eggs, as well as the results of performance tests that looked at things like executive function, language, recall, and mental flexibility.

When looking at dietary patterns, the researchers discovered that 16.5 percent of women and 14 percent of men said they never ate eggs, but nearly four percent of women and seven percent of men said they had eggs more than five times a week.

After crunching the data, the researchers found that women who ate more eggs had less of a drop in fluency scores—which look at things like executive function and semantic memory (i.e. recalling words, concepts, and numbers)—over time. The researchers specifically found that the risk dropped by 0.1 for every category of egg consumption.

What does that mean? Basically, eating more eggs was linked to a lower risk of cognitive decline.

Do eggs prevent cognitive decline?

It’s tough to say based on this study alone that eggs prevent cognitive decline. In fact, the study simply found that there was a link, but didn’t prove that eating eggs actually caused the lower risk.

However, research has found that some nutrients in eggs can help support brain function. Choline, which is found in the egg yolk , has been linked to better cognition, and eggs in general are thought of as a brain-friendly food.

"Research has found this nutrient to have anti-inflammatory benefits which may help protect against cognitive decline," explains Keri Gans, RD , author of The Small Change Diet .

A 2021 observational study published in the Journal of Nutritional Science also found that people who ate an intermediate number of eggs (up to 1.5 eggs a week) had lower rates of cognitive decline than those who ate about half an egg or less on a weekly basis.

“Eggs are one of the best sources of choline, a nutrient that is crucial for brain development and function,” explains Scott Keatley, RD , co-owner of Keatley Medical Nutrition Therapy. “Choline is a precursor to acetylcholine, a neurotransmitter involved in memory and learning.”

Eggs also contain vitamin B12, “which is important for maintaining the health of nerve cells and can help prevent memory loss and cognitive decline,” Keatley says. They also have lutein and zeaxanthin, antioxidants that help protect against oxidative stress and inflammation. Both of those are linked to cognitive decline, Keatley says.

“Eggs are a really good source of protein and fat, too,” says Jessica Cording, RD , is the author of The Little Book of Game-Changers . “That’s important for blood sugar stability. We’ve learned in recent years that blood sugar plays a role in cognitive health.”

Should I incorporate eggs into my diet?

Eggs are thought of as an overall healthy food and a good source of protein, Keatley says.

“They are nutrient-dense, providing high-quality protein and essential nutrients such as vitamins A, D, E, and K, along with minerals like selenium and zinc,” he explains. “The protein and fat content in eggs can aid in satiety and weight management by helping you feel full longer. Additionally, the amino acids found in eggs support muscle repair and growth, which is particularly beneficial for maintaining muscle mass as you age.”

If you’re looking for ways to get more eggs in your life , Keatley recommends eating them scrambled, poached, or boiled. You can also add them to a veggie omelet or use them as a topping for avocado toast .

“ Hard-boiled eggs make a convenient and nutritious snack option,” Keatley points out. “They can also be chopped and added to salads for extra protein and flavor.”

If you’re not in the habit of eating eggs, Cording suggests hard-boiling a few in advance so you can grab and go, or making egg-and-veggie cups in muffin tins for a quick snack.

Cording says that most people can safely consume up to seven eggs a week without worrying about a poor impact on their cholesterol levels. As a result, there are plenty of chances to load up on eggs when the mood strikes.

Why Is Puberty Starting Earlier And Earlier?

Zepbound May Now Be Easier to Get—And Cheaper

‘My Back Pain Turned Out To Be A Rare Disease’

Are We Entering The Fiber Supplement Renaissance?

Are Tampons Safe? Study Finds Arsenic, Lead

Where Taylor Swift Stands On Reproductive Rights

The Red Light Therapy Trend, Explained By Experts

How Dense Breasts Affect Your Breast Cancer Risk

Best Sauna Blankets, Tested By Experts And Editors

New Study Says Coffee May Help With Muscle Mass

Selena Gomez Opens Up About Not Carrying Children

Kate Middleton's Cancer Diagnosis, Explained

Stock market prediction using optimized long-short term memory based on improved salp swarm optimization

Article sidebar, main article content.

The prediction of stock price volatility is thought to be one of the most fascinating and important study topics in the financial sector.  Deep learning (DL) uses cutting-edge computing technology to analyze data, particularly in the discipline of finance, to deliver insightful analysis.  Due to the advantages of sequential learning, the LSTM network has demonstrated notable performance when it comes to time series prediction.  It might be very difficult for a researcher to build and choose the best computationally optimal LSTM network for stock market forecasting.  The model's ability to learn is impacted by multiple hyperparameters that it must control due to the nature of the data. In addition, several earlier research used heuristics based on trial and error to guess these parameters, which may not always result in the best network.  Furthermore, the hyper-parameter values have a significant impact on LSTM model accuracy.  To increase the efficiency and precision of the stock prediction method, an improved salp swarm optimization (ISSA) algorithm is used in this research to find satisfactory LSTM model parameters.  The ISSA approach uses partial opposition-based learning (POBL) to enhance the population diversity and avoid local optima problems.  To verify the ability of the proposed ISSA-LSTM prediction method, four different stock market datasets are considered for experiments.  The experimental results confirmed that the developed optimized ISSA-LSTM approaches produced high prediction accuracy and fast convergence rate. 

Article Details

K. kiruthika.

Department of Computer Science, Vellalar College for Women, Erode, Tamil Nadu, India

E.S. Samundeeswari

• Follow University of Limerick on Facebook
• Follow University of Limerick on X/Twitter
• Follow University of Limerick on Instagram
• Follow University of Limerick on LinkedIn
• Follow University of Limerick on YouTube
• Follow University of Limerick on TikTok
• Search University of Limerick website

UL researchers discover building blocks that could ‘revolutionise computing’

A research team at University of Limerick has made a major discovery by designing molecules that could revolutionise computing.

The researchers at UL’s Bernal Institute have discovered new ways of probing, controlling and tailoring materials at the most fundamental molecular scale.

The results have been used in an international project involving experts worldwide to help create a brand-new type of hardware platform for artificial intelligence that achieves unprecedented improvements in computational speed and energy efficiency.

The research has just been published in world leading scientific journal Nature .

The UL team, led by Damien Thompson, Professor of Molecular Modelling at UL and director of SSPC, the Research Ireland Centre for Pharmaceuticals, in an international collaboration with scientists at the Indian Institute of Science (IISc) and Texas A&M University, believe that this new discovery will lead to innovative solutions to societal grand challenges in health, energy and the environment.

Professor Thompson explained: “The design draws inspiration from the human brain, using the natural wiggling and jiggling of atoms to process and store information. As the molecules pivot and bounce around their crystal lattice, they create a multitude of individual memory states.

“We can trace out the path of the molecules inside the device and map each snapshot to a unique electrical state. That creates a kind of tour diary of the molecule that can be written and read just like in a conventional silicon-based computer, but here with massively improved energy and space economy because each entry is smaller than an atom.

“This outside the box solution could have huge benefits for all computing applications, from energy hungry data centres to memory intensive digital maps and online gaming.”

To-date, neuromorphic platforms – an approach to computing inspired by the human brain - have worked only for low-accuracy operations, such as inferencing in artificial neural networks. This is because core computing tasks including signal processing, neural network training, and natural language processing require much higher computing resolution than what existing neuromorphic circuits could offer.

For this reason then, achieving high resolution has been the most daunting challenge in neuromorphic computing.

The team’s reconceptualization of the underlying computing architecture achieves the required high resolution, performing resource-intensive workloads with unprecedented energy efficiency of 4.1 tera-operations per second per watt (TOPS/W).

The team’s breakthrough extends neuromorphic computing beyond niche applications in a move that can potentially unleash the long-heralded transformative benefits of artificial intelligence and augment the core of digital electronics from the cloud to the edge.

Project lead at IISc Professor Sreetosh Goswami said: “By precisely controlling the vast array of available molecular kinetic states, we created the most accurate, 14-bit, fully functional neuromorphic accelerator integrated into a circuit board that can handle signal processing, AI and machine learning workloads such as artificial neural networks, auto-encoders, and generative adversarial networks.

“Most significantly, leveraging the high precision of the accelerators, we can train neural networks on the edge, addressing one of the most pressing challenges in AI hardware.”

Further enhancements are coming, as the team works to expand the range of materials and processes used to create the platforms and increase the processing power even further.

Professor Thompson explained: “The ultimate aim is to replace what we now think of as computers with high-performance ‘everyware’ based on energy efficient and eco-friendly materials providing distributed ubiquitous information processing throughout the environment integrated in everyday items from clothing to food packaging to building materials.”