latent learning rat maze experiment

Latent Learning In Psychology and How It Works

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.

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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.

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Latent learning is a type of learning which is not apparent in the learner’s behavior at the time of learning, but which manifests later when a suitable motivation and circumstances appear. This shows that learning can occur without any reinforcement of a behavior.

Edward Tolman is widely credited with discovering and disseminating the concept of latent learning through his experiments in the 1930s.

While the broader idea that learning can occur without immediate reinforcement might have been discussed or observed in various forms earlier, Tolman’s systematic experiments with rats in mazes clearly demonstrated and popularized the specific concept of latent learning in psychology.

Edward Tolman argued that humans engage in this learning daily as we drive or walk the same route daily and learn the locations of various buildings and objects. Only when we need to find a building or object does learning become obvious

Tolman conducted experiments with rats and mazes to examine the role of reinforcement in how rats learn their way through complex mazes. These experiments eventually led to the theory of latent learning.

An example of latent learning in children is when a child watches their parents drive multiple times without actively trying to learn the process.

Later, during a toy car game, the child mimics the steps of starting a car, checking mirrors, and using indicators, even though they’ve never been formally taught or reinforced for learning these actions.

The child’s earlier observations became evident when there was a relevant situation to apply the knowledge.

Cognitive maps as an example of latent learning in rats

Tolman coined the term cognitive map, which is an internal representation (or image) of an external environmental feature or landmark.

He thought that individuals acquire large numbers of cues (i.e., signals) from the environment and could use these to build a mental image of an environment (i.e., a cognitive map).

By using this internal representation of physical space, they could get to the goal by knowing where it is in a complex set of environmental features. Shortcuts and changeable routes are possible with this model.

In his experiments with rats in mazes, Tolman observed that even without direct rewards, rats seemed to develop a “mental map” of the maze. When later introduced to a reward, these rats could navigate the maze more efficiently than those without prior exposure, suggesting they had learned about the maze (latent learning) even without reinforcement.

To demonstrate that rats could make navigational decisions based on knowledge of the environment, rather than their directional choices being dictated by the effects of rewards.

In their study, 3 groups of rats had to find their way around a complex maze. At the end of the maze, there was a food box. Some groups of rats got to eat the food, some did not, and for some rats the food was only available after 10 days.

In their famous experiments Tolman and Honzik (1930) built a maze to investigate latent learning in rats. The study also shows that rats actively process information rather than operating on a stimulus response relationship.

Group 1 : Rewarded

Day 1 – 17: Every time they got to end, given food (i.e. reinforced).

Group 2 : Delayed Reward

Day 1 – 10: Every time they got to end, taken out.
Day 11 -17: Every time they got to end, given food (i.e. reinforced).

Group 3 : No reward

Day 1 – 17: Every time they got to end, taken out.

The delayed reward group learned the route on days 1 to 10 and formed a cognitive map of the maze. They took longer to reach the end of the maze because there was no motivation for them to perform.

From day 11 onwards, they had the motivation to perform (i.e. food) and reached the end before the reward group.

This shows that between stimulus (the maze) and response (reaching the end of the maze) a mediational process was occurring the rats were actively processing information in their brains by mentally using their cognitive map (which they had latently learned).

Critical Evaluation

Beyond Behaviorism : Latent learning challenged the dominant behaviorist idea that reinforcement is necessary for learning. It broadened the understanding of how and when learning occurs to include cognitive factors, such as information processing.

Support from Experiments : Edward Tolman’s maze experiments provided empirical evidence for latent learning, solidifying its legitimacy in the field of psychology.

Explains Everyday Learning : The theory accounts for how we often learn from our environment even when there isn’t a clear reward or feedback. This aligns with many everyday learning experiences.

Cognitive Emphasis : Latent learning paved the way for a more cognitive approach to understanding learning, emphasizing mental processes and internal representations.

Measurement Challenges : Since latent learning is not immediately observable, it can be difficult to measure and quantify.

Over-reliance on Animal Studies : While Tolman’s experiments were groundbreaking, they were primarily conducted with rats. Extrapolating findings from animal studies to human behavior can be problematic.

Limited in Scope : Latent learning mainly addresses situations where learning is not immediately apparent. It doesn’t provide a comprehensive view of all learning processes or explain why some learned behaviors might manifest immediately while others don’t.

Ambiguity in Mechanism : While latent learning describes a phenomenon, it doesn’t fully elucidate the underlying cognitive mechanisms responsible for this type of learning.

Tolman, E. C., & Honzik, C. H. (1930). Introduction and removal of reward, and maze performance in rats. University of California Publications in Psychology .

Tolman, E. C. (1948). Cognitive maps in rats and men . Psychological Review , 55(4), 189.

What is the difference between latent learning and observational learning?

Latent learning refers to knowledge acquired without immediate reinforcement, becoming evident when there’s a reason to use it. Observational learning, on the other hand, involves learning by watching and imitating others.

While latent learning is about internalizing information without immediate outward behavior, observational learning emphasizes learning through modeling or mimicking observed behaviors.

What is the difference between implicit and latent learning?

Implicit learning involves unconsciously acquiring knowledge about patterns and structures, typically without awareness that learning is occurring.

Latent learning refers to acquiring knowledge without immediately demonstrating the learned behavior, but it becomes evident when a relevant situation arises.

Both deal with learning outside of conscious awareness, but while implicit learning emphasizes the unconscious process, latent learning emphasizes the delay in demonstrating the acquired knowledge.

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How Latent Learning Works According to Psychology

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Observations

In psychology, latent learning refers to knowledge that only becomes clear when a person has an incentive to display it. For example, a child might learn how to complete a math problem in class, but this learning is not immediately apparent. Only when the child is offered some form of reinforcement (like a grade) for completing the problem does this learning reveal itself.

Why Latent Learning Matters

Latent learning is important because in most cases the information we have learned is not always recognizable until the moment that we need to display it. While you might have learned how to cook a roast by watching your parents prepare dinner, this learning may not be apparent until you find yourself having to cook a meal on your own.

When we think about the learning process, we often focus only on learning that is immediately obvious. We teach a rat to run through a maze by offering food as a reward for correct responses. We train a student to raise their hand in class by offering praise for the appropriate behaviors. But not all learning is immediately apparent.

Sometimes learning only becomes evident when we need to utilize it. According to psychologists, this "hidden" learning that only manifests itself when something motivates us is known as latent learning.

Discovery of Latent Learning

The term latent learning was coined by Hugh Blodgett in 1929. In experiments that involved having groups of rats run a maze, rats that initially received no reward still learned the course, and demonstrated their learning only after a reward was presented.

Edward Tolman expanded on Blodgett’s research and explained that the rats were able to draw upon their "cognitive map" of the maze once rewards were introduced.

A cognitive map refers to a mental representation of an environment. Such maps can be formed through observation as well as through trial and error. These cognitive maps allow people to orient themselves in their environment.

Even more surprising, the group of rats who weren’t given a reward until the 11 th day of the experiment, outperformed the group of rats who were given a reward from day one, once the reward was introduced to them. These observations demonstrated that active learning could take place outside of the stimulus-response relationship, even though an organism does not display it right away.

Tolman rejected the standard behaviorist theory of his day that indicated behavior could only be learned by reinforcement. He asserted that there were cognitive processes involved and he applied these concepts to human learning. He suggested that we are always taking in facts and information around us creating a framework of how everything is related to each other, and we can access it when we need it.

Consider your knowledge of various routes in your hometown. Every day you travel a variety of routes and learn the locations of different businesses in your town. However, this learning is latent because you are not using it most of the time. It is only when you need to find a specific location such as the nearest coffee shop or bus stop that you are required to draw on and demonstrate what you have learned.

Decades later, neuroscientists have been able to explain this cognitive map at the cellular level. Specific neurons in the hippocampus and other brain regions are involved in spatial navigation skills.

Latent Learning Observations

In his book History of Psychology , author David Hothersall explained that while there was initially some controversy surrounding the phenomenon, numerous researchers also reported that lab rats did learn in the absence of rewards.

This notion challenged much of what the behaviorists believed, which was that learning could only occur with reinforcement . As a result, some of the more entrenched behaviorists suggested that there must have been some sort of reinforcement present during the non-reward trials, even if that reinforcement was not immediately obvious .

Research has demonstrated that the latent learning phenomenon is, as Hothersall explained, "reliable and robust."

It is well understood that rats placed in a maze will learn the route they need to follow to obtain a food reward, but research has also demonstrated that the rats learned the rest of the maze as well.

The burning questions are: Why do the rats learn the whole maze when it doesn't seem relevant? And how do investigators demonstrate that this latent learning has taken place?

Simple. When experimenters block the learned route, the rats would then use the next shortest path to get to the food. In order to do this, the rats must have learned the rest of the maze as well (all of the wrong ways and dead ends that didn't lead to food), even if such learning occurred without reinforcement.

These findings suggest that learning occurs as we go, often by accident, but not just because of incentives and rewards. So how does such latent learning take place? Some experts suggest that simply satisfying our curiosity often serves as the reward for our learning.

Latent learning and cognitive maps correlate with many higher-level mental abilities, such as problem-solving and planning for the future.

Highly Complex Decision Makers

Are humans simple stimulus-response machines or are they highly complex decision-makers?

Consider the idea of distant future rewards as motivators for learning. If students learn something in the present, according to a traditional behaviorist, they may be rewarded with good grades, a high GPA, and praise from their parents. They should then continue on this path because it is reinforcing.

However, a cognitive psychologist may consider the complex mental processes taking place. The student may also be motivated by the hope of gaining acceptance to the college of their choice in the future. Their future success holds future rewards, like a good job, decent pay, and the ability to support a family. They consider the way they should go, and the way they shouldn't go. The dots are connecting, the framework is forming, goals can be set, and they can plan for the future.

The rewards of this learning may not be apparent or immediate, but in this example, learning may take place in anticipation of a reward later on down the road. Tolman would say that the student is taking it all in, creating their cognitive map, so they can later demonstrate what they have learned by solving problems as they present themselves, and making highly complex decisions when the right time comes.

Blodgett HC. The effect of the introduction of reward upon the maze performance of rats . University of California Publications in Psychology. 1929;4:113-134.

Tolman EC. Cognitive maps in rats and men . Psychological Review . 1948;55(4):189–208. doi:10.1037/h0061626

BehrensTEJ, Muller TH, Whittington JCR, et al. What is a cognitive map? Organizing knowledge for flexible behavior . Neuron . 2018;100(2):490-509. doi:10.1016/j.neuron.2018.10.002

Eichenbaum H. The hippocampus as a cognitive map … of social space . Neuron . 2015;87(1):9-11. doi:10.1016/j.neuron.2015.06.013

Hothersall D. History of Psychology . McGraw-Hill Humanities Social; 2004.

Iordanova MD, Good MA, Honey RC. Configural learning without reinforcement: Integrated memories for correlates of what, where, and when . Quarterly Journal of Experimental Psychology . 2008;61(12):1785-1792. doi:10.1080/17470210802194324

Wang MZ, Hayden BY. Latent learning, cognitive maps, and curiosity . Current Opinion in Behavioral Sciences . 2021;38:1-7. doi:10.1016/j.cobeha.2020.06.003

Chen J. Cognitive Mapping for Problem-Based and Inquiry Learning: Theory, Research, and Assessment . 1st ed. Routledge; 2022. doi:10.4324/9781003305439

Coon D. Mitterer JO. Introduction to Psychology: Gateways to Mind and Behavior With Concept Maps . Wadsworth; 2010.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Latent Learning

Learning objectives.

Explain latent learning and cognitive maps

Although strict behaviorists such as Skinner and Watson refused to believe that cognition (such as thoughts and expectations) plays a role in learning, another behaviorist, Edward C. Tolman, had a different opinion. Tolman’s experiments with rats demonstrated that organisms can learn even if they do not receive immediate reinforcement (Tolman & Honzik, 1930; Tolman, Ritchie, & Kalish, 1946).

Latent learning is a form of learning that is not immediately expressed in an overt response. It occurs without any obvious reinforcement of the behavior or associations that are learned. Latent learning is not readily apparent to the researcher because it is not shown behaviorally until there is sufficient motivation. This type of learning broke the constraints of behaviorism, which stated that processes must be directly observable and that learning was the direct consequence of conditioning to stimuli.

An illustration shows three rats in a maze, with a starting point and food at the end.

Latent learning also occurs in humans. Children may learn by watching the actions of their parents but only demonstrate it at a later date, when the learned material is needed. For example, suppose that Ravi’s dad drives him to school every day. In this way, Ravi learns the route from his house to his school, but he’s never driven there himself, so he has not had a chance to demonstrate that he’s learned the way. One morning Ravi’s dad has to leave early for a meeting, so he can’t drive Ravi to school. Instead, Ravi follows the same route on his bike that his dad would have taken in the car. This demonstrates latent learning. Ravi had learned the route to school, but had no need to demonstrate this knowledge earlier.

Everyday Connection: This Place Is Like a Maze

Have you ever gotten lost in a building and couldn’t find your way back out? While that can be frustrating, you’re not alone. At one time or another we’ve all gotten lost in places like a museum, hospital, or university library. Whenever we go someplace new, we build a mental representation—or cognitive map—of the location, as Tolman’s rats built a cognitive map of their maze. However, some buildings are confusing because they include many areas that look alike or have short lines of sight. Because of this, it’s often difficult to predict what’s around a corner or decide whether to turn left or right to get out of a building. Psychologist Laura Carlson (2010) suggests that what we place in our cognitive map can impact our success in navigating through the environment. She suggests that paying attention to specific features upon entering a building, such as a picture on the wall, a fountain, a statue, or an escalator, adds information to our cognitive map that can be used later to help find our way out of the building.

Link to Learning

Watch this video to learn more about Laura Carlson’s studies on cognitive maps and navigation in buildings .

CC licensed content, Shared previously

Operant Conditioning. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/6-3-operant-conditioning . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction
Latent Learning. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/learning-7/cognitive-approaches-to-learning-48/latent-learning-202-12737/ . License : CC BY-SA: Attribution-ShareAlike

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The Learning Theorists

Tolman is the great systematic learning theorist whose thought seems closest to how we think of learning today. He uses cognitive terminology to describe behavior on the part of his rats that looks to us today as a description of intelligent behavior. But, be careful. His position is a bit more ambiguous. He thinks of himself as a behaviorist who believes that the best way to describe the behavior he is observing is that it is as if the rat was behaving intelligently, demonstrating a purpose, or executing a plan. He describes himself as a purposive behaviorist. He is proposing an approach to explanation that would seem acceptable to his fellow behaviorists. Yet, at all other points he proceeds to explain behavior in what looks to us as in cognitive, goal directed terms. He speaks of his rats as forming cognitive maps, and as developing expectations and hypotheses. My own view is that the behaviorism part of purposive behaviorism was simply Tolman stating that he was going to be basing his theories on the observations of behavior. The terms of his theories, though, certainly look cognitive.

Tolman was also a very creative experimentalist. Although he didn’t invent the rat maze-running experiment, he was among the most creative maze builders, and who tied the nature of the mazes to his theoretical questions. Sometimes those mazes could be elaborate such as his sunburst maze or as simple such as the + maze. Incidentally, both of these mazes were ways of testing the response-learning vs place-learning hypothesis. Other behaviorist learning theories claim that what is being learned are specific behaviors (response learning), but Tolman argues that when mastering a maze the subject learns the spatial layout of the maze (place learning). Furthermore, he is well known for introducing the idea that place learning is accomplished by having the subject form a cognitive map of the maze. This is a far cry from the theoretical terminology used by Thorndike, Guthrie, Hull, and Spence.

As for Tolman’s theories. He was always willing to change his views, but he certainly believed that you could not explain behavior simply by correlating observable stimuli with observable responses. You need to posit theoretical entities that operated on the incoming environmental information in order to produce the observed behavior. Tolman called these theoretical entities, intervening variables. Now, we have seen that others like Guthrie, Hull, and Spence have hypothesized that there are internal stimuli (such as drive stimuli) that play a role in explaining behavior. Tolman feels no need to couch these intervening variables in stimulus response terms.

Tolman’s most systematic account of the intervening variables he felt were needed was “Determiners of Behavior at a Choice Point” from 1938. ‘Choice point’ here refers to the point in a maze in which a rat can either left or right. The chart from this article is reproduced in our text and identifies an intervening (theoretical) variable corresponding to each independent (environmental variable). Corresponding to the independent variable maintenance schedule (how long the subject has been deprived of food) is the intervening variable of demand (demand on the part of the subject for food, for example). Corresponding to the independent variable goal (appropriate to the particular deprivation state) is the intervening variable appetite (for food as opposed to water, for example). In all, his intervening variables are demand (being hungry, e.g.), appetite (for a specific goal), differentiation (discriminating between various stimuli in the maze relevant to reaching one’s goal), motor skill (what behaviors are required in this particular maze), hypothesis (expectations formed from previous runs through the maze), and bias (does the nature of the maze encourage the rat to turn right at this choice point?). The above intervening variables are best understood as events happening internal to the organism between environmental changes and observable behaviors. These six intervening variables each correspond to an observable environmental variable and, in principle, can be studied by manipulating that environmental variable while holding all the other environmental variables constant.

Perhaps, Tolman’s most well-known result is demonstrating the distinction between learning vs performance. This was demonstrated in his famous latent learning experiments. He would have one group of rats wander around the maze for a number of days without being reinforced for wherever they ended up. Then after a number of days they started getting reinforced upon reaching the goal box. Those rats showed nearly immediate mastery of the maze (as opposed to a gradual mastery found by a group of rats that received consistent rewards). Why did this group show sudden learning of the maze as opposed to gradual learning (predicted by most learning theories)? Because they had been learning all along--- even when they weren’t being reinforced. There was no sudden jump in learning. Only a sudden jump in performance. This was a finding that other learning theorists had to take seriously, and caused some of them (such as Spence) to drop the assumption that reinforcement was necessary for learning. Reinforcement influences performance, not learning.

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Tolman’s Rat Experiments

Tolman’s Rat Experiments: Understanding Cognitive Maps

In the field of psychology, Tolman’s rat experiments have played a pivotal role in shaping our understanding of cognitive processes and behavior. Conducted by behaviorist Edward C. Tolman in the 1940s, these experiments provided groundbreaking insights into the cognitive abilities of rats and their capacity to create mental representations of physical space.

Key Definition:

Tolman’s rat experiments were conducted by psychologist Edward C. Tolman to study the cognitive processes of rats. In these experiments, rats were placed in mazes and their behavior was observed to understand how they learned to navigate the maze and find food. Tolman’s findings suggested that rats developed cognitive maps of the maze, implying that they were using mental processes to navigate, rather than simply forming stimulus-response associations. This challenged the prevailing behaviorist views of the time and contributed to the development of cognitive psychology.

Tolman’s research focused on the concept of “cognitive maps,” which refers to the mental representations of spatial relationships that enable organisms to navigate their environment. Through a series of experiments in controlled environments, Tolman observed the behavior of rats as they encountered and learned to navigate through complex maze structures.

One of the key findings of Tolman’s experiments was the demonstration of latent learning in rats. Unlike the traditional behaviorist perspective, which emphasized the role of reinforcement in learning, Tolman’s studies indicated that rats were capable of acquiring and retaining spatial information without immediate reinforcement. This finding suggests that the rats were forming mental maps of the mazes. These maps later assisted them to choose efficient paths to obtain a reward.

Furthermore, Tolman’s research highlighted the role of environmental cues and landmarks in shaping the rats’ cognitive maps. The rats demonstrated the ability to use visual and spatial cues to orient themselves within the maze, implying an advanced level of spatial cognition.

History of Tolman’s Experiments

Edward C. Tolman first presented his research on purposive behaviorism in his major work, “Purposive Behavior in Animals and Men,” published in 1932 . He developed his theories while teaching psychology at the University of California, Berkeley, where he began his influential studies on rats and maze.

The Experiments

Edward C. Tolman’s rat experiments were pivotal in the development of the concept of latent learning. In the 1930s, Tolman conducted a series of experiments using rats in mazes to explore the role of reinforcement in learning. He divided the rats into three groups:

Group 1 : Received a food reward at the end of the maze every time.
Group 2 : Never received a food reward.
Group 3 : Did not receive a food reward for the first 10 days but did receive one afterwards.

The results showed that the rats in Group 1 quickly learned to navigate the maze. Group 2 appeared to wander aimlessly without the incentive of a reward. However, when Group 3 began receiving a food reward, they quickly caught up to Group 1 in their ability to navigate the maze efficiently.

Tolman concluded that the rats in Group 3 had formed a “cognitive map” of the maze during their unrewarded trials and demonstrated their learning only when a motivation (the food reward) was introduced. This suggested that learning could occur without reinforcement. Tolman’s finding challenged the behaviorist notion that learning is solely the result of conditioning forming associations.

Psychological Concepts

Edward Tolman’s rat experiments provided several key insights into the nature of learning and cognition:

Latent Learning : Tolman’s experiments demonstrated that rats could learn the layout of a maze without any reinforcement or rewards. The lab researchers only became aware of the rats cognitive maps when they introduced a reward. Once a reward was introduced, the rats performed with their latent acquired knowledge about the layout of the map.
Cognitive Maps : The rats developed mental representations of the maze, which Tolman referred to as “cognitive maps.” These internal maps allowed the rats to navigate the maze more efficiently when experimenters introduced a reward, even if they had previously explored the maze without any incentive.
Challenge to Behaviorism : At the time, behaviorism dominated psychology, emphasizing observable behaviors and disregarding internal mental processes. Tolman’s findings challenged this view by suggesting that internal cognitive processes play a significant role in learning.
Importance of Purpose : Tolman believed that behavior is goal-directed. Basically, meaning that purposes and objective motivate behavior. His experiments suggested that rats were not just reacting to stimuli but were actively seeking goals.
Information Processing : The experiments indicated that rats, and by extension other animals, actively process information from their environment rather than simply operating on a stimulus-response relationship.

These insights from Tolman’s rat experiments contributed to the development of cognitive psychology and our understanding of the complex processes underlying learning and behavior. They highlighted the importance of internal mental states. Moreover, this research also enhanced scientific knowledge about the ability of organisms to navigate and make decisions based on cognitive maps and latent learning.

How Tolman’s Discovery Challenges Behaviorism

Behaviorism contends that all behaviors are a conditioned reflex to stimuli or a group of stimuli. John B. Watson that behavior can be understood “without lugging in consciousness or any other so-called mental processes” ( Watson, 1924 ). The hardline rejection of mental processes was later softened by B.F. Skinner. He explains, “not only does a behavioral analysis not reject any of these ‘higher mental processes’; it has taken the lead in investigating the contingencies under which they occur” ( Skinner, 1974 ).

Perhaps, it was studies such as Tolman’s that required behavioralist to take a deeper look at their blanket rejection of mental processes. Tolman referred to goal directed behavior as ‘purposive behaviorism.’ Basically, with the experimental rats, their behavior in navigating the maze was goal directed to achieve the reward. This is no surprize and in agreement with previous behaviorism experiments. However, behaviorism believed that the correct route to the treat was strictly a trial and error adventure in which the correct route was reinforced with a reward.

Tolman’s experiments suggested that the rats were unconsciously making a cognitive map of the maze before any reward was presented. Basically, he was suggesting a ‘mental process’ was taking place without conditioning. Tolman explains, for an animal to remember, he must not only expect a given character in a past significate, but he must also expect a means-end-relation of pastness as obtaining between the present sign and that past significate” (Tolman, 1932, p. 140). However, rats that explored the maze previor to the experimenter introducing a reward performed better than rats that never explored the maze. Some mental process had taken place in the exploration that contributed to the later navigations with an end-means purposeness.

A Narrative Example

The new route to work.

Emma had recently moved to a new city for work. Every day, she took the same route to her office, passing through a series of streets and landmarks. She became familiar with the general layout of the city. She never really paid attention to the alternative routes since her GPS always guided her along the fastest path.

One morning, construction blocked her usual route, and her GPS malfunctioned. Emma was initially worried about being late to work. However, to her surprise, she found herself taking turns and choosing streets with confidence. Without realizing it, she had developed a cognitive map of the city through her daily commutes.

Even though she had never consciously tried to learn the alternative paths, her mind had been paying attention. This is an example of latent learning—Emma had absorbed knowledge of the city layout without any deliberate effort or immediate need to recall it.

As she navigated the city streets, her cognitive map became more apparent. She remembered certain landmarks, like a distinctive mural on a building or a quirky coffee shop on a corner, which helped her orient herself and make decisions about which way to go.

Eventually, Emma reached her office without getting lost. Her experience was a testament to the power of latent learning and the existence of cognitive maps. Even when we’re not actively trying to learn something, our brains are constantly processing and storing information. Later, we may draw upon this information when needed.

Latent Learning in Action

This story demonstrates how cognitive maps and latent learning work together in our daily lives, often without our conscious awareness. Emma’s ability to find her way without active guidance is a practical application of these concepts. Daniel J. Siegel, a clinical professor of psychiatry at the UCLA School of Medicine and the executive director of the Mindsight Institute, explains that “children come to expect what typically comes first and what comes next in a given situation, with at times intense and passionate reactions to deviations. Associated with this hippocampal ability is the establishment of a spatial representational map of the locations of things in the world” ( Siegel, 2020. Kindle location: 1,470 ).

Cognitive Processes Beyond the Maze

Tolman’s findings suggest much more than navigating a maze or city streets. Latent learning refers to knowledge gained without the incentive of a reward. Markedly, latent learning can apply to predicting behavior from behavioral patterns. While we may not be aware of learning at the time of acquisition, the knowledge may manifest later when a situation arises that requires that particular piece of knowledge.

In terms of predicting behavior, understanding the latent learning that has occurred can provide insights into potential future behaviors. For example, if an individual has been exposed to certain behavioral patterns, even without active engagement or reinforcement, they may later exhibit behaviors consistent with those patterns when the context or motivation arises.

This concept is particularly relevant in situations where individuals learn by observation or through exposure to certain environments. The knowledge gained latently can influence their decisions and actions, even if they were not explicitly taught or reinforced for that behavior.

Mental Models

Siegel explains that “each of us filters our interactions with others through the lenses of mental models created from patterns of experiences in the past” ( Siegel, 2020. Kindle location: 1,455 ). Merlin Donald, a Canadian psychologist, neuro-anthropologist, and cognitive neuroscientist known for his work on the evolution of human cognition and consciousness, wrote, “the ultimate result of having so much tertiary cortex is our ability to build mental models on a very abstract level. Mental models are the most deliberate, conscious productions of the mind. The ultimate model of models, the human self-in-its-environment, is the most frequently and intensively rehearsed of our mental constructs. It is developed and rehearsed through play reflection, and self correction, with the advice and contributions of parents, siblings, friends, enemies, institutions and society” ( Donald, 2002 ).

A Few Words by Psychology Fanatic

Tolman’s rat experiments significantly contributed to the development of cognitive psychology. Not only did Tolman’s findings challenge the behaviorist view of learning but they also paved the way for the study of internal mental processes. The concept of cognitive maps introduced by Tolman has had a lasting impact on various fields. These fields include neuroscience, animal behavior, and human cognition.

In conclusion, Tolman’s rat experiments offer valuable insights into the cognitive abilities of rats and their capacity to form mental representations of space. The research provided a foundation for understanding the complex cognitive processes involved in spatial navigation and learning.

Last Update: April 12, 2024

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References:

Donald, Merlin ( 2002 ). A Mind So Rare: The Evolution of Human Consciousness. W. W. Norton & Company; Reprint edition.

Siegel, Daniel J. ( 2020 ). The Developing Mind: How Relationships and the Brain Interact to Shape Who We Are. The Guilford Press; 3rd edition.

Skinner, B.F. (1974/ 2011 ). About Behaviorism. Vintage; 1st edition.

Tolman, Edward C. (1932). Purposive behavior in Animals and Man. The Century Company. Link

Watson. John B. (1924/ 2012 ). Behaviorism. Forgotten Books.

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Latent learning, cognitive maps, and curiosity

Curiosity is a desire for information that is not motivated by strategic concerns. Latent learning is not driven by standard reinforcement processes. We propose that curiosity serves the purpose of motivating latent learning. While latent learning is often treated as a passive or incidental process, it normally reflects a strong evolved pressure to actively seek large amounts of information. That information in turn allows curious decision makers to represent the structure of their environment, that is, to form cognitive maps. These cognitive maps then drive adaptive flexible behavior. Based on recent data, we propose that orbitofrontal cortex (OFC) and dorsal anterior cingulate cortex (dACC) play complementary roles in curiosity-driven learning. Specifically, we propose that (1) OFC tracks intrinsic value of information and incorporates new information into a cognitive map; and (2) dACC tracks the environmental demands and information availability to then use the cognitive map from OFC to guide behavior.

The natural environment offers a plethora of rewards to most foragers but acquiring these rewards requires knowledge [ 1 , 2 , 3 , 4 ]. For example, red knots (arctic shorebirds), feed on bivalves that are patchily distributed and buried in the mud [ 5 ]. Notably, the locations of these prey cannot be guessed based on visual inspection, but can be inferred based on a rich knowledge of likely patch structure and distribution of other prey. When foraging, the birds reside in patches longer than predicted by simple foraging models; their overstaying can explained by modified models that include a bonus for the information that the extra residence time provides. Knots are typical of many natural decision makers, which are constantly starved for information. Notably, this is an area where laboratory experiments tend to differ most starkly from natural decision-making contexts; in the lab relevant information is typically made available and, if obscured, simple.

Curiosity, which we can define as a drive for non-strategic information, is a major driver of learning and determinative of the success of development in humans and other animals [ 6 , 7 , 8 , 9 , 10 , 11 ]. Its features are preserved across species and over the lifespan. It appears to be associated with at least somewhat discrete neural circuits [ 12 , 13 , 14 ].

Latent learning

Classical concepts of learning held that all learning is driven by reinforcement contingencies. These ideas are fundamental to the “Law of Effect” and to Hebbian learning [ 15 , 16 ]. That work, in elaborated form, is central to reinforcement learning, one of the most successful psychological theories and the basis of a generation of systems neuroscience, and to much of machine learning.

However robust learning can occur in the absence of reward [ 17 , 18 ]. This idea poses a challenge to simple reinforcement learning models, which Tolman termed the “ stimulus response school .” In a classic latent learning setup, a rat is released into a large maze with no reward. Naive rats typically amble around the maze, ostensibly with no purpose. Later, the experimenters introduce a reward to a specific location in the maze. The rats with maze exposure learned to locate the reward much more quickly than ones who were naive to that maze. The rats learned the maze structure – and formed a cognitive map – latently.

Curiosity and cognitive maps

Any forager placed within a complex natural environment must naturally trade off between the costs and benefits of exploration. In addition to the metabolic costs of locomotion, sensory processing, and learning, active exploration carries opportunity costs: that time could be better spent searching for food, courting and reproducing, or avoiding predators. For example, in the case of the knot, the delay in travel time imposes an opportunity cost in the form of foregone large rewards at new patches. Even motivational processes driven by distal reward seeking must necessarily discount future rewards and uncertain rewards, and the benefits of exploration are unavoidably delayed beyond the temporal horizon and, individually, infinitesimally unlikely. So reward-maximizing calculation is unlikely to sufficiently motivate search. Instead, evolution must endow the decision maker with intrinsic motivation to learn and ultimately to map its environment [ 8 , 19 , 20 , 21 ].

Indeed, curiosity would seem to go hand in hand with the learning of cognitive maps. Cognitive maps refer to detailed mental representations of the relationship between various elements in the world and their sequelae [ 22 , 23 , 24 ]. Having a cognitive map allows a decision maker to not just guess what will happen but also to deal with unexpected changes in our environment. The classic idea about cognitive maps - also attributable to Tolman - is that they allow us to respond flexibly when contingencies change (e.g. when the layout of a maze changes, [ 18 , 25 ]. That kind of flexibility is very difficult to implement with basic reinforcement learning processes [ 18 , 22 , 23 , 24 , 25 ]. Instead, it requires a sophisticated representation of the structure of the world.

Critically, cognitive maps typically require a rich representation of the world- they require a level of detail that is not normally available from reinforcement learning processes. That detailed representation of the linkages between adjacent spaces allows for vicarious travel along those linkages. Because it is so much more detailed, it requires orders of magnitude more information than standard reward-motivated reinforcement learning can give. Getting that information cannot occur if it needs extrinsic rewards - those rewards simply are not available in the environment.

We propose, therefore, that latent learning is motivated and enabled by curiosity. However, Tolman conceived of latent learning as a fundamentally passive process, one that took place during apparently purposeless exploration - almost as if by accident. We propose, instead, that latent learning in practice tends to be more actively driven. However, this purposive exploration must be e driven by the evolutionary advantage brought by curiosity and ultimately by the extreme information gap experienced by foragers in the natural world.

The analogy to artificial intelligence

The problems faced by a naturalistic decision-maker or forager are similar in many ways to the problems faced by artificially intelligence (AI). When AI performs classic Atari games, it uses straightforward RL principles [ 26 ]. But those games, especially the ones that AI is good at differ from natural situations. The real world – and some games like Pitfall and Montezuma’s Revenge - are what is known as hard-exploration problems [ 27 , 28 , 29 ]. Rewards are sparse (they require dozens or hundreds of correct actions), so gradient descent procedures are nearly useless. For example, in Pitfall, the first opportunity to gain any points comes after ~60 seconds of perfect play involving dozens of precisely timed moves. Moreover, rewards are often deceptive (they result in highly suboptimal local minima, so getting a small reward promotes adherence to a suboptimal strategy). RL agents that do well at relatively naturalistic hard-exploration games tend to have deliberate hard-coded exploration bonuses [ 28 , 29 ].

The AI domain provides a good illustration of how cognitive maps can be crucial for the success of curiosity. The optimal search strategy in sparse (natural) environments is typically to identify a locally promising region and then perform strategic explorations from that spot to identify subsequent ones [ 30 ]. That exploration will not be random, but will take place along identified high-value destinations. AI agents suffer from the problem of detachment , that is, when they explore the environment, they leave the relatively high-reward areas of space to explore lower-reward ones [ 28 ]. Most such areas are likely to be dead ends, and, when a dead end is detected, the agent ought to return to the high reward area and pursue other promising paths. However, the basic curiosity-based approach, which gives intrinsic rewards for novelty, repels the agent from returning to the promising region of space, precisely because it’s the most familiar and least intrinsically rewarded (it’s also not extrinsically rewarding, because any extrinsic reward has been consumed on the path there, and does not replenish in the meantime). This in turn requires making some kind of internal map of space so that the agent can return to the locus of high potential reward and explore more efficiently than a wholly random path.

A closely related problem that AI agents - and real-world agents as well - face, is the problem of derailment [ 28 ]. To explore a space efficiently, an agent must be able to return to promising states and use those as a starting point for efficient exploration. From there, the agent must engage in random search. However, in real environments, returning to a promising state may require a very precise sequence of actions that cannot be deviated from - so stochasticity must be controlled until that state is achieved, at which case it must begin again in earnest. As such, a stochastic search must be carefully controlled depending on one’s place in the larger environment - which requires basic mapping functions, and cannot be done with simple RL-type learning. Moreover, important factors governing the exploration process, such as detecting an information gap, deriving the value of information itself, and directing exploration towards potential sites that might be low in external reward but high in information/entropy, simply cannot be supported by only experienced reward history. The key to achieve this, again, is to have a mental map, or internal model, of what is available, and what is novel and potentially offer high information content (high entropy).

Operational definition of curiosity

Developing these ideas about curiosity, latent learning, and cognitive maps holds great potential in neuroscience. However, it faces several problems from the get-go. We and others have defined curiosity as a motivation to seek information that lacks instrumental or strategic benefit [ 6 , 7 , 8 , 10 ]. By this definition, many explorative and playing behaviors qualify as a demonstration of curiosity [ 9 , 31 ]; even risk-seeking and other learning behaviors may be explained that way [ 32 , 33 , 34 , 35 ]. But this definition is vague and does not readily lend itself to many laboratory contexts. In an effort to remedy these drawbacks, we developed a conservative operational definition that combines three criteria: (1) a curious research subject is willing to sacrifice primary reward in order to obtain information; (2) the amount of reward a subject is willing to pay scales with the amount of potentially available additional information; and (3) additionally gained information provides no obvious instrumental or strategic benefit.

We devised a more complex task [ 37 ] that would circumvent published criticisms [ 36 ] of the observing task. This task is based on the observation that monkeys seek counterfactual information - information about what would have happened had they chosen differently [ 38 , 39 ]. In the counterfactual curiosity task , monkeys choose between two risky offers. During testing, monkeys are sometimes given the opportunity to choose an option that will provide valid information about the outcome that would have occurred had they chosen the other option. Monkeys are willing to pay to choose this option, indicating that they are curious about counterfactual outcomes. Moreover, monkeys paid more for options that provide more counterfactual information. We speculate that this curiosity-driven information-seeking helps monkeys to develop a sophisticated cognitive map of their task environments [ 37 ].

Functional neuroanatomy of curiosity in the frontal lobes

Our ultimate goal is to understand the neural circuitry underlying curiosity-driven choice. Here we summarize the tentative picture, with a focus on two prefrontal regions, the orbitofrontal cortex (OFC) and the dorsal anterior cingulate cortex (dACC). Both regions are implicated in neuroimaging studies of curiosity [ 40 , 41 , 42 ]. The neuroanatomy of curiosity is more complex and includes other areas such as hippocampal areas [ 12 , 43 , 44 ] and basal ganglia [ 33 ]. But we would like to highlight OFC and dACC for their potential involvement that bridges curiosity, latent learning, and cognitive maps.

Orbitofrontal cortex:

We propose that OFC serves to (i) track the intrinsic value of information, (ii) maintain a cognitive map of state space, and (iii) update that map when new information is gained. The clear role of OFC in cognitive mapping has been one of the major intellectual advances of the past decade, and is demonstrated in rodents, monkeys, and humans [ 23 , 24 , 45 , 46 ]. This theory, for example, integrates economic findings in OFC with evidence that it carries non-economic variables [ 46 , 48 , 49 , 50 ]. This extends to curiosity [ 51 ]. In that study, we found that OFC neurons encode the value of information and (confirming much previous work) the value of offers. Critically, OFC uses distinct codes for informational value and more standard juice value. This distinction presumably reflects the role of that information in updating the cognitive map -- even though this information may not offer immediate strategic benefit. In other words, OFC doesn’t use a single coherent value code across contexts, but rather, represents task-relevant information in multiple formats, as would be expected in a map rather than a simple reinforcement learning situation. Of course, OFC does not achieve this alone. Studies using similar paradigms show that information is signaled by other systems, including the midbrain dopamine system (e.g. [ 52 , 53 , 54 ]).

Dorsal anterior cingulate cortex:

We propose that dACC plays a distinct and complementary role to OFC. Specifically, it appears to track both information delivery and level and task demands for use by OFC in updating the cognitive map and applying it to instrumental use. This idea is motivated by the observation that dACC tracks informativeness, counterfactual information [ 33 , 38 ], environmental demands [ 55 , 56 ], as well as various economic variables (e.g. [ 57 , 58 , 59 , 60 ]. It is further motivated by observations about the relative hierarchical positions of the two regions and their relative contributions to choice [ 61 , 62 , 63 ]. Perhaps most relevantly, in a recent study, White et., al. [ 33 ] trained monkeys to associate juice rewards with various reward probabilities with different fractals. Single units in dACC showed increased firing rates to increased uncertainty, and thus to higher expectation of information (when the uncertainty resolved). Moreover, dACC firing rates ramped up to the anticipation of the information that came with the resolution of the uncertainty. In other words, dACC neurons did not simply encode different levels of uncertainty which remained at a constant level for each trial; nor did they ramp up firing rates in anticipation to reward delivery (see also our own results, which paint a somewhat similar picture, [ 64 ]).

Conclusion and future directions

Curiosity has long been treated mystically, as if it is impenetrable to scholarly study. Even when treated as a regular psychological phenomenon, curiosity is often studied in an ad hoc manner. More recent work has made great progress in developing formal approaches to understand the phenomenon systemically study its neural substrates. That formal approach, aided by remarkable progress in AI, has in turn allowed neuroscientists to tentatively start to understand the circuity of curiosity. That work in turn will likely be critical for understanding naturalistic decision-making, which is marked by the need to make quick decisions with orders of magnitude less information than would be ideal.

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A virtual maze our lab has used for monkeys based on the classic alley maze of Tolman. Tolman and his graduate students placed rats in mazes like this one and found that they would explore the maze unrewarded and would demonstrably learn the features of the structure of the maze in the absence of rewards, a result that is difficult to explain using then-dominant simple stimulus-response learning theories. Tolman proposed that the rats generated a cognitive map that instantiated features of the maze and could be consulted to drive flexible behavior.

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Monkey in a tree, illustrating the problem of derailment in curiosity research. The monkey must learn foraging strategy through trial and error, which requires a highly variable exploration of the environment. But getting to the end of a branch is somewhat risky and requires suppressing stochastic variability. To successfully deploy curiosity the monkey must have a cognitive map of where variability is good and where it is bad.

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In our curiosity task, subjects could choose between risky options for juice rewards. In some trials, they could also gain information about what would have occurred had they chosen differently. By analyzing preference curves on such trials, we could quantify their subjective value of counterfactual information. We found a small but significant positive valuation of counterfactual information in both subjects tested (Wand and Hayden, 2019).

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Responses of an ensemble of OFC neurons to offers varying in informational value and reward value (Blanchard et al., 2015). We find that individual neurons encode both variables (horizontal and vertical axes indicate tuning coefficients for the two dimensions respectively). However, those codes are themselves uncorrelated, as indicated by the lack of a significant slope between the two dimensions.

Acknowledgements

We thank Ethan Bromberg-Martin for helpful discussions.

Funding statement

This research was supported by a National Institute on Drug Abuse Grant R01 DA038106 (to BYH).

Competing interests

The authors have no competing interests to declare.

The authors declare no conflicts of interest

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Latent Learning (Definition + Examples)

Latent learning challenges the idea that all behavior is learned through reinforcement - it’s an essential addition to the study of psychology.

What is Latent Learning?

Latent learning is the process of subconsciously retaining information without motivation or reinforcement. You aren’t consciously thinking about the consequences of what you are learning while learning it. Because there is no cheese at the end of the maze doesn’t mean we aren’t learning our way around.

When we read about psychology, learning, and behavior studies, we read a lot about rewards and punishments. Most of these rewards involve food. For years, psychologists believed that learning was only done through reinforcements.

But that’s not exactly how the world works. Our learning is not always reinforced. We may observe or engage in a skill without any tests to take later or punishments to fear. The fact that you have learned these things isn’t apparent until later when you are asked to apply the information to get a reward.

Latent learning offers an explanation for retaining information never enforced by teachers, authority figures, or yourself!

How Does Latent Learning Take Place?

During latent learning, information is absorbed subconsciously. For example, you may watch Survivor as you wind down from a long day. You have no intention of using survival skills. The show is just fascinating, and you like watching it with friends. If you were to try and learn from it, you'd likely watch with a notebook in hand. Instead, you turn it on, eat popcorn, and enjoy the show.

After watching a few seasons of Survivor, you may be surprised that you have survival skills. Maybe, a few years after watching the show, you go on a hike and accidentally get lost. You have to set up camp for the night and light a fire. As you search for materials to start the fire, information from years of watching Survivor returns to you. Even though you have never taken a survival course or thought too much about learning how to build a fire, your latent learning pays off, and you can successfully light and keep the fire going.

Not all latent learning is immediately apparent or serendipitous. You might not realize the value of specific skills or knowledge for many years. However, that doesn't mean your efforts spent learning new information or acquiring skills are in vain!

Is Latent Learning Operant Conditioning or Classical Conditioning?

Neither! Conditioning requires rewards and punishments for the behavior to stick. There is no latent conditioning, only latent learning that involves no reinforcements.

Difference Between Insight and Latent Learning

Many forms of learning lead to solving problems or performing behaviors. Often, latent learning is confused with another form of learning - insight learning. But these two processes are slightly different.

Insight learning occurs when you've faced a problem, take a pause, and then suddenly put together memories and information that will help you solve the problem. Many people refer to this as an "a-ha" moment. Maybe you are facing a conflict at work and don't know the best solution to make all your team members happy. You've read plenty of books and have tons of experience with conflicts like this, but the correct answer isn't coming. So you walk around the block, make a cup of coffee, and let your mind think about other things.

All of a sudden, the answer comes to you! You realize you can solve the problem by piecing together a few strategies you've learned. This is insight learning.

Latent learning may also "catch you by surprise," but the answers that come to you may be information that you didn't even know you retained!

Who Introduced The Idea of Latent Learning?

Almost five decades after Pavlov used dogs to support his theories on classical conditioning , Edward Tolman used rats to support his theories on latent learning. Tolman did not discover latent learning, but his experiment brought the idea into mainstream psychology.

Tolman's Rats

Tolman devised an experiment to dive into the intricacies of learning behaviors using rats navigating mazes. In this study , Tolman aimed to investigate how rewards—or the lack thereof—influence the learning process. He split the rats into three groups, each subjected to different reward conditions as they traversed a maze over 17 days. The intention was to observe how consistent, absent, or delayed rewards affected the rats' ability to learn and remember the maze's layout. Through this setup, Tolman hoped to explore the underlying mechanisms of learning and challenge prevailing behaviorist theories of the time.

Methodology :

Consistently Rewarded Group : In this group, rats were placed in a maze for 17 consecutive days. Each time they successfully navigated the maze, they were rewarded with food. This consistent reward system was designed to reinforce their learning of the maze's structure.
Never-Rewarded Group : This second group also navigated the maze for 17 days. However, these rats never received any rewards, irrespective of their performance or whether they reached the end of the maze.
Delayed Reward Group : Rats in the third group were initially not rewarded for the first 10 days they spent in the maze. However, starting on the 11th day, they began receiving food as a reward for successful completion. Like the other groups, they spent 17 days in the maze.

As expected, the consistently rewarded rats in the first group became quite adept at navigating the maze due to the immediate reinforcement they received. In contrast, the second group, without any rewards to motivate them, showed no particular urgency or pattern in their exploration.

The intriguing results emerged from the third group. After being introduced to the reward system on the 11th day, these rats rapidly demonstrated a strong understanding of the maze's layout. By the end of the 17-day period, they consistently outperformed even the first group, taking fewer wrong turns and reaching the end more efficiently.

Implications :

Tolman's experiment provided significant insights into 'latent learning.' Even without immediate rewards, the rats in the third group had been learning and forming a cognitive map of the maze. The sudden introduction of food merely acted as a catalyst to reveal their latent knowledge. This study challenged the conventional behaviorist notion that learning is solely a product of reinforcement and posited that organisms can learn and form cognitive maps of their environment without direct rewards.

What Does This Say About Latent Learning?

This shows that the rats had retained at least some of the information about the maze before they started getting rewarded for their learning. If we only learned things when motivated by rewards, the rats who received rewards later might not have been able to learn the maze as fast as the first group in the remaining 7 days of the study.

So what was happening? In the first 10 days, even though they were not asked to display their knowledge, the rats in the third group had been making “cognitive maps” of the maze. The rats didn’t even display their learning independently - until they were asked. Once motivated to display their knowledge, they pulled from what they had learned in the first 10 days.

This is latent learning in action.

Examples of Latent Learning In Everyday Life

Observational.

Latent learning can be done in many ways. You observe your parents repeatedly as they tend to their garden. You’re never given this task, but join a community garden years later. You pick up your gloves and start weeding and tending to the garden as your parents did.

This is an example of both observational learning and latent learning. You observed someone else’s actions and retained the information even though you were never asked to display your knowledge of working in a garden.

But latent learning isn’t just observational.

Learning By Doing

Think about your commute to work. You pass a lot of exits, stores, and street names on your drive to and from work every day. If you need to one day go to one of those stores that you saw on your commute, you could probably know how to get there without using a map, right?

That’s also latent learning in action.

What if you have to take a detour on your commute? Maybe you pull from your knowledge of the town and find another way to get to work. You’re also applying latent learning here. Additional studies on rats show that when the fastest route to food is cut off in a maze, they can easily find an alternative route that still takes them to the food. Even if they have a “preferred” route, they learn the whole map through latent learning and know how to problem-solve when faced with obstacles.

Latent Learning in Infants

How early does latent learning start? Studies suggest it begins earlier than you might think. Babies as young as three months may retain memories before they have the ability to imitate them. At three months, researchers presented one group of 3-month-old participants with two puppets and performed a target behavior. The other group of 3-month-old participants was only exposed to one of the puppets.

One puppet was exposed to both groups multiple times for the next three months. Then, when the infants reached six months, researchers reintroduced the second puppet.

The infants exposed to the second puppet months earlier were more likely to imitate the target behavior of the second puppet. They remembered the connection between the first and second puppets, even though they could not imitate any behavior at that stage of development.

How Teachers Bring Latent Learning Into the Classroom

Great teachers understand that latent learning could be the best strategy for struggling students. If a teacher allows their student to play with material in their own ways, they might learn more than what is being presented to them. On the science subreddit , two teachers share their experiences with latent learning.

"This is why I always give my students 'fiddle time' when introducing new software and workflows. About half of them figure out the basics of what they need to do by the time the lesson begins, and they’re proud of figuring out themselves, so they’re eager to help classmates who are still learning. Then I can focus on technique, advanced tools, and details when I do the lesson instead of reviewing the UI and essentials."

"As a teacher, this has always been one of most important concepts I try to employ in the classroom.

For kids, everything can't remain so simple and one-sided forever. Plus the world is connected now more than any time in history. We don't have to throw everything in their face, but we need to at least give them a chance to see what all is out there. And it's amazing sometimes, even though it can be rough seeing more of the world and its harsher realities.

It's easy for me as a language teacher since I can easily employ linguistic culturology any chance I want. But there's a lot of beauty to be shared from all around the word in terms of math, science, history, and really just anything. One country, or one subject, or one class, or one culture doesn't hold all the secrets."

Get Learning!

Remember, latent learning is all done subconsciously. This means that you don’t know what you’re capable of! You may already have the skills and knowledge to complete tasks and accomplish things, even if you have never done them. That means that you can't force yourself into latent learning. If you want to learn something new, just go out and experience something you've never experienced! Try a new hobby. Talk to a stranger. Read a book in a genre that is new to you. You never know what you'll gain from it.

Now go out and discover what you have learned through latent learning!

Latent Inhibition (Definition + Examples)
The Psychology of Long Distance Relationships
Beck’s Depression Inventory (BDI Test)
Operant Conditioning (Examples + Research)
Skinner’s Box Experiment (Behaviorism Study)

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Operant Conditioning

Classical Conditioning

Observational Learning

Latent Learning

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The Little Albert Study

Bobo Doll Experiment

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Module 9: Learning & Conditioning

Latent learning, what you’ll learn to do: describe latent learning and observational learning.

Classical and operant conditioning are responsible for a good bit of the behaviors we learn and develop, but certainly there are other things we learn simply through observation and thought. Latent learning is a form of learning that occurs without any obvious reinforcement of the behavior or associations that are learned.

According to Albert Bandura, learning can occur by watching others and then modeling what they do or say. This is known as observational learning. There are specific steps in the process of modeling that must be followed if learning is to be successful. These steps include attention, retention, reproduction, and motivation. Through modeling, Bandura has shown that children learn many things both good and bad simply by watching their parents, siblings, and others. What have you learned by observation?

Learning Objectives

Explain latent learning and cognitive maps
Describe Edward Tolman’s experiment on latent learning

Figure 1. Psychologist Edward Tolman found that rats use cognitive maps to navigate through a maze. Have you ever worked your way through various levels on a video game? You learned when to turn left or right, move up or down. In that case you were relying on a cognitive map, just like the rats in a maze. (credit: modification of work by “FutUndBeidl”/Flickr)

Everyday Connection: This Place Is Like a Maze

Link to Learning

Watch this video to learn more about Carlson’s studies on cognitive maps and navigation in buildings.

Tolman’s Experiment

Edward Tolman was studying traditional trial-and-error learning when he realized that some of his research subjects (rats) actually knew more than their behavior initially indicated. In one of Tolman’s classic experiments, he observed the behavior of three groups of hungry rats that were learning to navigate mazes.

The first group always received a food reward at the end of the maze, so the payoff for learning the maze was real and immediate. The second group never received any food reward, so there was no incentive to learn to navigate the maze effectively. The third group was like the second group for the first 10 days, but on the 11th day, food was now placed at the end of the maze.

As you might expect when considering the principles of conditioning, the rats in the first group quickly learned to negotiate the maze, while the rats of the second group seemed to wander aimlessly through it. The rats in the third group, however, although they wandered aimlessly for the first 10 days, quickly learned to navigate to the end of the maze as soon as they received food on day 11. By the next day, the rats in the third group had caught up in their learning to the rats that had been rewarded from the beginning. It was clear to Tolman that the rats that had been allowed to experience the maze, even without any reinforcement, had nevertheless learned something, and Tolman called this latent learning. Latent learning is to learning that is not reinforced and not demonstrated until there is motivation to do so . Tolman argued that the rats had formed a “cognitive map” of the maze but did not demonstrate this knowledge until they received reinforcement.

A sample maze showing blue doors and green curtains that made it even tricker for a rat to know how to navigate the maze.

Figure 1. The maze. As you can see from the map, the maze had lots of doors and curtains to make it difficult for the rats to master. The blue marks represent doors that swung both directions, which prevented the rat from seeing most of the junctions as it approached. This forced the rat to go through the door to discover what was on the other side. The green forms show curtains. These hung down and prevented the rat from getting a long distance perspective and it also meant that they could not see a wall at the end of a wrong turn until they had already made a choice and moved in that direction. The rat was always in a small area, unable to see beyond the next door or curtain, so learning the maze was a formidable task.

Now that you’ve learned the design of the study, let’s take a closer look at what happened in the study. The results for the three groups will be shown in these graphs. The graph on the left is for the group that always received food. The middle graph is for the rats that did not received food for the first 10 trials and then, on Trial #11, started to receive food. The graph on the right is for rats that never received food. The red dots indicate how the rats did in each of the three conditions. The Y-axis (vertical axis) indicates how many wrong turns, or errors, the rats in each condition made on average. The X-axis (horizontal axis) shows the different trials. This is the first trial, so none of the rats knew there was food in the food box.

3 graphs depicting the three groups in Tolman's experiment: food on every trial group, the no food until trial 11 group, and the no food on any trial group. Each of the groups took 30 wrong turns on their first trial.

Let’s see how the rats in each group did over the next four trials. Notice all the groups made fewer errors and continue to do so in Trial 4. Now look more closely at trials 4 and 5. Are you starting to see a difference between the groups? Use the dotted line in the middle of the graphs as a reference point for comparing the groups. Which group seems to be getting to the line faster?

3 graphs depicting the three groups in Tolman's experiment: Group 1: food on every trial, Group 2: no food until trial 11, and Group 3: no food on any trial. Group one shows the number of wrong turns decreasing down to 16 by the sixth trial. Group two gets slightly better, with about 21 wrong turns, and Group 3 makes around 18 wrong turns.

Let’s pick up at Trial 7. Notice that the group on the left, which receives food on every trial, continues to improve at a faster rate than the other two groups. These two groups are both performing at the same level and are making about 20 wrong turns on each trial on average. At Trial 10, we are at a critical point in the experiment because things are about to change on the next trial for the rats shown in the middle graph. Something special will happen to this group. Food will now appear in the food box! Of course, they won’t know this until they get there, so the effects of the change should not appear on the next trial. As you can see from the graphs for Trial 11, the groups shown in the middle and right graphs still look the same. The rats in the left group are now making fewer wrong turns than either of the other two groups.

Work It out

Your task here is to predict what is going to happen on Trial 12 for the “no food until Trial 11” group.

Option A : Notice that this result is the same as the “no food on any trial” group. So, if you choose option A, you think that they will not act differently now than they acted on the first 11 trials and they will continue to make a lot of wrong turns.

Option B : This option suggests that they are now motivated to learn the path to the food, but that they will do so in small steps, just as we have seen for all three groups up to this point. Option B says that they are moving in the direction of the “food on every trial” group, but that it will take some extra learning to get there.

Three graphs depicting the options that the rats in the Group B: No Food until Trial 11 may choose for their 12th trial. Will they continue to make 16 wrong turns (graph A), will they improve and make 15 wrong turns (graph B), or will they improve dramatically and make just 5 wrong turns (graph C)?

So, what happened to the rats in the group that began to receive food at Trial 11? They were immediately able to make their way through the maze without making many wrong turns to get to the food. They made about the same number of errors as the “Food on Every Trial” group! Tolman interpreted this to mean that they had created a mental map of the maze during the first 11 trials…and when they needed to get food, they could find their way to the food box very efficiently!

As we look at trials 13, 14, and 15, notice how the graph for the group of rats on the left –- the ones that received food on every trial — and the graph for the group of rats in the middle — the ones that started receiving food at trial 11 — now look similar. And the rats that never received food continued to make more than 15 errors in each trial on average.

Authored by : Patrick Carroll for Lumen Learning. Provided by : Lumen Learning. License : CC BY: Attribution
Latent Learning: Learning Before Doing. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=df3e71c60a0001dc051db622d622b3f7 . Project : Psychology. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Operant Conditioning and Observational Learning. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:r470BCFb@7/Operant-Conditioning . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]
Latent Learning. Authored by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/learning-7/cognitive-approaches-to-learning-48/latent-learning-202-12737/ . License : CC BY-SA: Attribution-ShareAlike
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Table of Contents

Latent Learning: A Simple Explanation With Examples & More

In the fast-paced world of constant information and rapid skill development, there exists a remarkable and often overlooked phenomenon—latent learning. It’s the secret weapon in your cognitive arsenal, the hidden treasure trove of knowledge and skills waiting for the perfect moment to emerge.

In this article, we will embark on a journey into the fascinating realm of latent learning. We will unravel its intricacies, explore real-life examples, and unveil the psychology that makes it tick.

You’ll discover how latent learning challenges traditional notions of how we acquire knowledge and skills, and how it can revolutionize your approach to personal growth, problem-solving, and more. But that’s not all. We’ll also delve into the practical aspects of encouraging and harnessing latent learning in your life.

So, are you ready to uncover the hidden gems of your mind?

Understanding Latent Learning

Latent learning psychology definition.

Latent learning in psychology refers to a type of learning that occurs without any immediate or apparent reinforcement or reward. It involves the acquisition of knowledge, skills, or information in a passive manner, often without the learner consciously intending to learn.

Unlike more traditional forms of learning where the individual actively seeks to acquire knowledge, in latent learning, the learner absorbs information from their environment and stores it for later use. This stored knowledge may not be immediately expressed or demonstrated until a situation arises where it becomes relevant or beneficial.

In simple terms, latent learning is a type of learning that happens without you realizing it. It’s like when you pick up knowledge or skills just by being around something or someone, even if you’re not actively trying to learn. This learning remains hidden until you need to use it later on.

The Power of Unconscious Learning

Latent Learning challenges the notion that learning is always a conscious effort. It highlights our brain’s remarkable ability to absorb information passively, waiting for the right moment to apply it.

Related: What is True Education and How You Can Access It

Discovery of Latent Learning

The concept of latent learning was first discovered and extensively studied by American psychologist Edward C. Tolman in the early 20th century. Tolman’s research played a huge role in reshaping our understanding of how learning occurs, challenging traditional behaviorist theories that emphasized reward-based, stimulus-response associations.

Tolman conducted a series of experiments using rats as subjects to investigate the nature of learning and cognition. His most famous experiment, the “ cognitive map ” study, showcased the phenomenon of latent learning.

In this experiment, Tolman placed rats in a maze without providing any immediate rewards or punishments. Initially, the rats roamed the maze aimlessly. However, over time, they began to explore and navigate the maze more efficiently, despite no apparent motivation or reinforcement.

Tolman concluded that the rats had developed a mental representation or “cognitive map” of the maze’s layout. They had acquired latent knowledge about the maze’s structure through passive observation and exploration. Importantly, this knowledge remained hidden until a motivation (in the form of food rewards) was introduced.

This discovery challenged the prevailing behaviorist theories of the time, which posited that learning was solely a result of conditioned responses to stimuli. Tolman’s research demonstrated that learning could occur without immediate reinforcement and that cognitive processes, such as forming mental maps, played a crucial role in learning.

Today, the discovery of latent learning remains a foundational concept in the study of psychology, highlighting the complexity of the human mind’s ability to acquire and apply knowledge, even when the rewards are not immediately apparent.

How does Latent Learning Work

To comprehend Latent Learning fully, we must unravel the intricate mechanisms underpinning this covert process:

Information Absorption

Our brains perpetually soak up information from our surroundings, even when we are not actively seeking knowledge. This information is stored in our long-term memory, forming the foundation of latent knowledge.

Associative Connections

As information is absorbed, the brain forms associative connections between various pieces of data. These connections create a web of latent knowledge, where seemingly unrelated information is linked together. These associations serve as the foundation of latent learning.

Triggered Application

The true essence of this type of learning is unveiled when a situation arises that necessitates the application of the knowledge we’ve unknowingly amassed. It’s akin to discovering a missing puzzle piece when you didn’t even realize a puzzle was incomplete.

The Psychology Behind Latent Learning

To delve deeper into Latent Learning, we must explore the psychological principles that underlie this captivating phenomenon:

Cognitive Mapping

Latent learning often involves the creation of mental maps or cognitive representations of physical spaces. These mental maps allow individuals to navigate unfamiliar environments effectively. The brain’s ability to form and update these maps is a fundamental aspect of latent learning.

Motivation and Reinforcement

While latent learning may not be driven by immediate rewards, motivation and reinforcement still play significant roles. Individuals are more likely to apply latent skills or knowledge when they perceive a need or when reinforcement, such as positive feedback or recognition, is available.

The Role of Memory

Memory processes are central to this type of learning. The information acquired through observation or passive learning must be effectively stored in long-term memory to be accessible when needed. Memory consolidation ensures that latent knowledge remains intact until the appropriate context arises for its application.

Latent Learning Example

here are some example of latent learning that illustrate how this intriguing phenomenon operates:

Driving a Car

An excellent example of latent learning is acquiring the skill of driving. Before you even sit behind the wheel, you may have observed others driving, learned traffic rules, and absorbed road etiquette. This latent knowledge lies dormant until you take the driver’s seat for the first time. It’s a classic case of latent learning in action, where you apply what you’ve absorbed without consciously realizing it. This example demonstrates how observation and passive absorption of information can prepare you for practical tasks.

Problem-Solving

Another instance of this type of learning can be seen in problem-solving scenarios. You may not be aware of possessing a particular problem-solving skill until you encounter a challenging situation. Your latent knowledge comes to the forefront as you navigate the problem, drawing on previously absorbed information and experiences. This example highlights how our brains quietly accumulate knowledge that becomes invaluable when needed.

Language Acquisition

Children growing up in multilingual environments provide a prime example of latent learning. While they may not immediately speak all the languages they are exposed to, they passively absorb latent knowledge of these languages. This latent language proficiency emerges when they decide to learn and speak those languages later in life, demonstrating this process’s flexibility and adaptability.

Cooking Skills

Consider a scenario where someone regularly observes their parent or guardian cooking. Over time, they unconsciously accumulate latent knowledge about various cooking techniques, ingredient combinations, and flavor profiles. When they eventually decide to cook independently, their latent cooking skills come to the fore, allowing them to prepare meals effectively. This example of latent learning emphasizes how passive observation can lead to practical expertise.

These real-life examples of latent learning underscore the concept’s pervasive nature, showing how our brains quietly amass knowledge, ready to spring into action when the need arises. So, whether it’s driving, problem-solving, language acquisition, or cooking, this learning plays a significant role in our everyday lives, often without us even realizing it.

How to Encourage Latent Learning

Encouraging latent learning is a powerful way to tap into your hidden reservoir of knowledge and skills. By actively fostering an environment conducive to latent learning, you can enhance your ability to apply acquired knowledge when needed. Here are some strategies to encourage and promote latent learning:

Create a Stimulating Environment : Surround yourself with a rich and diverse range of experiences. Engage in activities that expose you to new information, cultures, and ideas. The more varied your experiences, the more latent knowledge you accumulate. Travel, explore, and immerse yourself in different environments to stimulate your learning.
Embrace Curiosity : Cultivate a curious mindset. Ask questions, seek answers, and explore topics that pique your interest. Curiosity is a potent driver of any learning, as it motivates you to absorb information passively, even when there’s no immediate need for it.
Reflect on Experiences : Regularly reflect on your experiences and what you’ve learned from them. Take time to ponder the insights gained from past situations, both positive and negative. Conscious reflection helps bring latent knowledge to the forefront of your consciousness, making it readily available for application when the opportunity arises.
Practice Mindfulness: Mindfulness techniques, such as meditation and mindfulness exercises, can enhance latent learning. They encourage focused attention and awareness, allowing you to absorb information more effectively and make stronger associative connections between various pieces of knowledge.
Challenge Yourself : Push beyond your comfort zone and embrace challenges. When you tackle new and unfamiliar tasks, you activate latent knowledge acquired from past experiences. This willingness to step into the unknown can lead to surprising discoveries and insights.
Interact with Diverse People : Engage in conversations and collaborations with individuals from diverse backgrounds. Different perspectives and experiences can trigger latent knowledge, as you adapt and integrate new information into your own cognitive framework.

You may also like: How To Develop The Essential Skills For Success

Common Challenges of Latent Learning

While latent learning is a powerful concept that can significantly enhance your cognitive abilities, it’s not without its challenges. Recognizing and addressing these challenges is essential to unlock the full potential of latent knowledge.

Lack of Awareness : Latent knowledge often goes unnoticed until needed, leading to missed opportunities.
Ineffective Retrieval : Difficulty in recalling latent knowledge when required can hinder its practical application.
Motivational Barriers : Without immediate rewards, there may be little motivation to actively seek opportunities for this type of learning.
Fear of Mistakes : A fear of making errors can deter individuals from applying their latent skills and knowledge.
Limited Exposure to Novelty : Sticking to routines and avoiding new experiences can limit opportunities to activate this type of learning.

Key Takeaways: What is Latent Learning

In conclusion, latent learning represents a profound and often untapped wellspring of human potential. As we’ve journeyed through the intricacies of this phenomenon, we’ve come to understand that learning is not always a deliberate, conscious effort. Instead, our minds quietly absorb and store knowledge and skills, ready to deploy them when the time is right.

By recognizing the power of latent learning, we can approach life with a newfound sense of wonder and possibility. We can embrace curiosity, seek out diverse experiences, and reflect on our past to nurture the latent knowledge within us. Remember, your latent potential is boundless—unleash it, and the possibilities are limitless.

We also invite you to keep pace with our website The Futuristic Minds , where we unravel the puzzles of tech advancements , the intricacies of finance , guide your career journey, and illuminate the path to an awe-inspiring future. Stay informed, stay motivated.

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What is latent learning in psychology?

Latent learning in psychology is when you learn things without trying or without an immediate reward. You might not even realize you’ve learned until you need that knowledge later on.

Who first introduced the concept of latent learning?

The concept of latent learning was first introduced by American psychologist Edward C. Tolman in the early 20th century through his experiments with rats in mazes.

How does latent learning relate to cognitive maps?

Latent learning is linked to cognitive maps as it suggests that organisms, like rats in Tolman’s experiments, can mentally construct maps of their environment through passive observation, enabling them to navigate effectively when motivation arises.

What are the advantages and disadvantages of latent learning?

Advantages of latent learning include the acquisition of hidden knowledge and problem-solving skills. However, a disadvantage is that the learned information may remain unused until motivation or a specific need arises.

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13.7 Cosmos & Culture

Of rats and men: edward c. tolman.

Tania Lombrozo

You've probably never heard of Edward C. Tolman , unless you're an experimental psychologist. If you're a Berkeley alumnus, you might be familiar with Tolman Hall , home to my office and lab. It's an unappealing and outdated homage to a man who was neither.

In classic experiments, Tolman convincingly demonstrated that you need some notion of mental representation — like a mental map — to explain rat behavior. This idea challenged behaviorist dogma and paved the way for cognitive science.

In my favorite experiment, rats were placed in a maze like that below (left), and had to make their way from point A to point G, where they found a treat.

Image of two mazes, one shaped like a hook (left), the other like a sunburst (right)

After four nights of practice, the familiar maze was replaced with a new one (right). The rats typically tried the top path first, generalizing what they'd learned from the initial maze. But this familiar path was blocked. The question was: which path would they choose to pursue instead?

A lab rat stuck in a maze iStockphoto.com hide caption

A lab rat stuck in a maze

If the rats had formed only associations about which behaviors were or weren't reinforced, they wouldn't have a spatial map to guide them. Instead, they would likely choose the path most similar to the one that had originally lead to food and take path 9 or 10.

In contrast, if the rats had formed something like a mental map of the original maze, they'd know that the food was ahead and to the right, and should choose a path like 6, which pointed in that direction. And that's exactly what they did. Clever, no?

But what I admire most about Tolman isn't clever experiments with rats — it's that he didn't stop with experiments or with rats. In a paper that summarizes the study just described, " Cognitive maps in rats and men " (1948), Tolman concludes with an argument that he calls "cavalier and dogmatic," proposing that humans have cognitive maps that not only situate them in space, but within a broader network of causal, social and emotional relationships. A narrow map can lead one to discount outsiders; a broader map to understanding and empathy. Tolman wrote:

Over and over again men are blinded by too violent motivations and too intense frustrations into blind and unintelligent and in the end desperately dangerous hates of outsiders. And the expression of these their displaced hates ranges all the way from discrimination against minorities to world conflagrations. What in the name of Heaven and Psychology can we do about it? My only answer is to preach again the virtues of reason—of, that is, broad cognitive maps.

Tolman took this attitude beyond the pages of scholarly journals. In the 1950s he lead opposition to a loyalty oath at Berkeley requiring faculty to state that they were not members of the communist party. He was fired for failing to sign the oath (Tolman was reinstated two years later).

Perhaps if more of us shared Tolman's broad thinking and social engagement, we'd be in a better position to navigate what he called "that great God-given maze which is our human world."

You can keep up with more of what Tania Lombrozo is thinking on Twitter: @TaniaLombrozo

University of California, Berkeley
cognitive science
Edward Tolman

Latent Learning

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First Online: 01 January 2024
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Ryota Kanai 4 &
Daw-An Wu 4

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Incidental learning; Unreinforced learning; Implicit learning

Latent learning is an acquisition of neutral information in the absence of external reinforcement or punishment. In latent learning, the acquisition of information does not lead to an immediate change in behavior until the subject is given an incentive to demonstrate the knowledge.

Characteristics

A brief introduction.

Learning is typically defined operationally as a process whereby pre-existing behavioral patterns undergo long-term modification. However, there are many cases in which the impact of a learning process may not immediately express itself, instead remaining latent. For example, one may learn how to perform a task by observing someone else, but this acquired knowledge may not become behaviorally expressed until performance of that task becomes necessary some time in the future. The study of latent learning helps to bridge behaviorist, operational definitions of learning with the more...

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Ryota Kanai & Daw-An Wu

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Department of Physiology & Biophysics, University of Washington School of Medicine, Seattle, Washington, USA

Marc D. Binder

Department of Cell Biology and Anatomy, Graduate School of Medicine University of Tokyo Hongo, Bunkyo‐ku, Tokyo, Japan

Nobutaka Hirokawa

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Kanai, R., Wu, DA. (2009). Latent Learning. In: Binder, M.D., Hirokawa, N., Windhorst, U. (eds) Encyclopedia of Neuroscience. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-29678-2_2705

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Narrative Summary of The Concept of the Habit-Family Hierarchy and Maze Learning: Part I

Overview:

This text dives into Clark L. Hull’s theory of habit-family hierarchies as a way to explain animal learning, particularly in the context of maze experiments. Hull argues that animals develop a repertoire of “habit families” through early experiences, which are essentially sets of alternative actions that all achieve the same goal. He suggests that when one member of a habit family is successful in a new situation, the learning automatically transfers to the other members of the family, even without specific practice.

Main Parts:

Introduction: This section introduces the two main mechanisms of habit formation: divergent and convergent excitatory tendencies. Divergent mechanisms involve a single stimulus leading to multiple responses, while convergent mechanisms involve multiple stimuli converging on a single response.
Associative Convergence and Habit Transfer: Hull argues that convergent mechanisms are key to explaining how animals can transfer learned responses to new situations, even if those situations share no objective similarity with the original learning environment. He uses a study by Shirley as an example, showing how a flash of light could evoke a finger retraction response, even though the flash had only been paired with a shock, which also elicited a finger retraction.
Habit-Family Hierarchy: This section combines the concepts of divergent and convergent tendencies to introduce the “habit-family hierarchy”. It describes how a single stimulus can trigger multiple, distinct actions that all lead to the same outcome. Hull uses the example of a maze with multiple paths to the goal, where one path may be preferred over another. He argues that this hierarchy is a fundamental mechanism for learning in diverse situations, including problem-solving and knowledge acquisition.
Automatic Transfer of Practice Effects: Hull introduces two key hypotheses: (1) animals possess a repertoire of habit-family hierarchies from early life experiences, and (2) practice with one member of a family automatically transfers to other members. He explores evidence for these hypotheses, suggesting that animals raised in enriched environments may have more developed hierarchies compared to animals raised in restricted environments.
Fractional Anticipatory Goal Reactions: Hull dives into the mechanism that he believes underlies the transfer of practice. He suggests that the “fractional anticipatory goal reaction” (rG), which is a small but significant portion of the full goal reaction, is crucial for integrating the habit-family hierarchy. He argues that rG is transferred to new situations and acts as a trigger for the entire goal-achieving sequence.
Frustration and Disinhibition: This section explores the impact of frustration and disinhibition on habit-family hierarchies. Hull explains that when a preferred path is blocked, an inhibitory tendency develops, preventing the animal from taking the preferred path. However, disinhibition can occur due to external stimuli, temporarily overcoming the inhibitory tendency and allowing for the choice of the preferred path.

View on Life:

Hull’s theory offers a deterministic view of learning and behavior, emphasizing the importance of prior experiences and the automatic nature of habit transfer. His focus on physical mechanisms and the role of the brain in shaping behavior aligns with a mechanistic view of life, suggesting that our actions are determined by our physiological processes.

Maze Learning: Hull uses maze learning as a central example to illustrate his theory. The scenarios involve rats navigating mazes with various paths, making choices based on learned preferences and the strength of excitatory tendencies.
Shirley’s Conditioning Experiment: This scenario involves humans learning to associate a flash of light with a finger retraction response through conditioning.
Johnson’s Dog Experiment: This scenario explores the differences in habit transfer between blind and seeing dogs, demonstrating how sensory abilities affect the development of habit-family hierarchies.
Valentine’s Maze Experiment: This scenario involves rats learning to navigate a maze with a blind alley, showcasing the role of frustration, disinhibition, and the influence of external stimuli in shaping behavior.

Challenges:

Transferring Learning to Novel Situations: The challenge is to explain how animals can adapt their behavior to new situations, especially when those situations lack objective similarity to previously learned experiences.
Overcoming Frustration and Inhibitory Tendencies: The challenge is to explain how animals can overcome learned inhibitory tendencies when presented with an alternative, potentially more desirable path.
The Role of External Stimuli: The challenge is to understand how external stimuli can disrupt learned behaviors and influence the choices animals make.
Competition Between Excitatory Tendencies: The conflict arises when different actions compete for dominance, such as when a rat chooses between a longer, well-learned path and a shorter, less familiar path.
The Struggle Between Learned Tendencies and Novel Opportunities: The conflict arises when previously learned habits clash with the benefits of exploring new paths and adopting novel strategies.
The Story of Habit-Family Hierarchies: The story arc progresses through an introduction to Hull’s basic concepts, the development of the habit-family hierarchy theory, the exploration of mechanisms for habit transfer, and the analysis of how frustration and disinhibition influence the process.
The introduction of divergent and convergent excitatory tendencies as the building blocks for habit formation.
The emergence of the habit-family hierarchy as a unified mechanism for learning and transfer.
The introduction of the “fractional anticipatory goal reaction” as a critical component for integrating and transferring learned behaviors.
The exploration of frustration and disinhibition as factors that can disrupt and alter established habits.

Point of View:

Hull’s Perspective: The text presents Hull’s theory as a comprehensive explanation for learning and behavior, emphasizing the role of physiological processes and the automatic nature of habit transfer.
A Mechanistic View of Behavior: The perspective is rooted in a mechanistic understanding of behavior, emphasizing physical mechanisms and conditioned responses as the driving forces behind animal actions.

How It’s Written:

Academic Tone: The text uses formal language and a scientific style, with precise definitions and references to research findings.
Example: “It is to be expected that the adaptive potentialities of the mechanism obtained by combining the two dynamic tendencies discussed above should differ in certain respects from those manifested by either alone.” (This sentence showcases the formal tone and emphasis on scientific reasoning.)
Informative and Analytical: The tone is informative and focused on presenting Hull’s theory and its implications, with an analytical approach to explaining the underlying mechanisms.

Life Choices:

Choice of Action: Animals make choices between alternative paths in mazes based on the strength of their learned excitatory tendencies, the presence of inhibitory tendencies, and the influence of external stimuli.
Reasoning: The choices are determined by a combination of previously learned habits, the perceived rewards associated with different paths, and the influence of environmental factors.
The Importance of Early Experience: The theory emphasizes that early life experiences play a crucial role in shaping an animal’s repertoire of habits and their capacity for learning new skills.
The Role of Transfer: The theory highlights the importance of transferring learned behaviors to new situations, allowing for efficient adaptation and problem-solving.
The Impact of Frustration: The theory sheds light on the significance of frustration in disrupting learned behaviors and creating opportunities for new learning.

Characters:

The Rat: The primary character in the text is the rat, which is used as a model organism for studying learning and behavior.
The Experimenter: The experimenter plays a crucial role in designing and conducting experiments, influencing the learning environment and observing the animals’ behavior.
Learning and Adaptability: The text explores the process of learning in animals, emphasizing their ability to adapt to new situations and transfer knowledge.
The Importance of Experience: The theory underscores the importance of prior experiences in shaping an animal’s behavior and their capacity for learning.
The Role of the Brain: The text implicitly emphasizes the role of the brain in processing information, forming associations, and controlling behavior.

Principles:

Association: The text emphasizes the principle of association, where stimuli and responses are linked through conditioning and repetition.
Habit Formation: The theory proposes that habits are formed through repeated associations and become automatic, influencing subsequent behavior.
The Law of Effect: The text implicitly references the law of effect, where behaviors that lead to positive outcomes are more likely to be repeated.

Intentions of the Characters in the text or the reader of the text:

Intentions of the Animals: Animals in the text are driven by basic drives like hunger and a desire to reach the goal.
Intentions of the Reader: The reader is likely seeking to understand the theory of habit-family hierarchies and its application to learning, potentially seeking insight into the mechanisms of learning and behavior.

Unique Vocabulary:

Divergent Excitatory Tendencies: A stimulus leading to multiple responses.
Convergent Excitatory Tendencies: Multiple stimuli leading to a single response.
Habit-Family Hierarchy: A set of alternative actions that all achieve the same goal, with a preferred order.
Fractional Anticipatory Goal Reaction: A portion of the goal reaction that can be triggered by a stimulus before the full goal is reached.
External Inhibition: A disruptive stimulus that weakens an excitatory tendency.
Disinhibition: The removal of an inhibitory tendency, allowing for a previously suppressed behavior to occur.
Shirley’s Conditioning Experiment: The story of Shirley’s experiment illustrates how a flash of light can evoke a finger retraction response, even though it was never directly paired with the response, demonstrating the power of convergent mechanisms in habit transfer.
Johnson’s Dog Experiment: The story of Johnson’s experiment highlights the differences in habit transfer between blind and seeing dogs, suggesting that sensory experience plays a role in developing effective habit-family hierarchies.
Valentine’s Maze Experiment: The story of Valentine’s experiment illustrates the role of frustration and disinhibition in shaping behavior. The rats initially follow a longer, well-learned path, but external stimuli can temporarily disinhibit their preference for the shorter path.
Habit-Family Hierarchies as a Foundation for Learning: The text proposes that habit-family hierarchies are a fundamental mechanism for learning in diverse situations, allowing animals to acquire a repertoire of skills that can be transferred to new environments.
Automatic Transfer as a Key to Adaptability: The theory suggests that automatic transfer of learned behaviors is a crucial component of animal adaptability, enabling them to efficiently solve problems and navigate new environments.
The Role of Frustration in Learning: The text suggests that frustration can actually be a catalyst for learning, as it temporarily disrupts established habits and creates opportunities for new learning.

Facts and Findings:

Rats Develop Habit-Family Hierarchies: The text emphasizes that rats develop a repertoire of habit-family hierarchies through early experiences, allowing them to navigate mazes and solve problems.
Practice Transferred Automatically: The text argues that practice with one member of a habit-family hierarchy automatically transfers to other members, even without specific practice.
External Stimuli Can Disrupt Habits: The text highlights that external stimuli can disrupt learned behaviors, temporarily disinhibiting previously suppressed responses and creating opportunities for new learning.

Statistics:

The text does not contain specific statistics.

Points of View:

Third-Person Perspective: The text is written from a third-person perspective, allowing for an objective presentation of Hull’s theory and the research findings that support it.

Perspective:

The perspective is focused on the mechanistic explanation of animal learning. It emphasizes the role of physical mechanisms, conditioned responses, and the automatic nature of habit transfer. The text avoids interpretations that attribute intentionality or consciousness to the animals.

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IMAGES

An illustration shows three rats in a maze, with a starting point and
Latent Learning (Definition + Examples)
What Is Latent Learning? (with pictures)
tolman maze
Tolman's learning theory/ maze-rat theory/ sign learning/ latent-learning theory
Tolman theory of latent learning| Tolman work in Psychology

COMMENTS

Latent Learning In Psychology and How It Works
In their famous experiments Tolman and Honzik (1930) built a maze to investigate latent learning in rats. The study also shows that rats actively process information rather than operating on a stimulus response relationship.
Psych in Real Life: Latent Learning
Latent learning is to learning that is not reinforced and not demonstrated until there is motivation to do so. Tolman argued that the rats had formed a "cognitive map" of the maze but did not demonstrate this knowledge until they received reinforcement. Figure 1. The maze.
Psych in Real Life: Latent Learning
In one of Tolman's classic experiments, he observed the behavior of three groups of hungry rats that were learning to navigate mazes. The first group always received a food reward at the end of the maze, so the payoff for learning the maze was real and immediate.
Latent Learning in Psychology and How It Works
Discovery of Latent Learning The term latent learning was coined by Hugh Blodgett in 1929. In experiments that involved having groups of rats run a maze, rats that initially received no reward still learned the course, and demonstrated their learning only after a reward was presented.
Latent Learning
This is known as latent learning: learning that occurs but is not observable in behavior until there is a reason to demonstrate it. Figure 1. Psychologist Edward Tolman found that rats use cognitive maps to navigate through a maze. Have you ever worked your way through various levels on a video game?
Behaviorism, Latent Learning, and Cognitive Maps: Needed Revisions in
Tolman's Latent Learning Research Tolman and Honzik took two groups of rats from the 1930a study and one group from the 1930b study to prepare the data for comparisons that were then published in the 1930b report. The studies employed three groups of food-deprived rats. The researcher placed each rat in the start box of a 14-unit T maze (see Figure 1 ), and the rat was then left to its own ...
Latent Learning
Tolman's experiments with rats demonstrated that organisms can learn even if they do not receive immediate reinforcement (Tolman & Honzik, 1930; Tolman, Ritchie, & Kalish, 1946). Latent learning is a form of learning that is not immediately expressed in an overt response. It occurs without any obvious reinforcement of the behavior or ...
Latent Learning
The term "latent learning" was coined by Blodgett ( 1929) to describe the sudden improvement in rats' maze performance that accompanied the introduction of a previously withheld reward. Blodgett ran three groups of rats through a maze and measured the number of blind-alley entrances that occurred each day.
Edward Tolman
Perhaps, Tolman's most well-known result is demonstrating the distinction between learning vs performance. This was demonstrated in his famous latent learning experiments. He would have one group of rats wander around the maze for a number of days without being reinforced for wherever they ended up. Then after a number of days they started getting reinforced upon reaching the goal box. Those ...
Latent Learning
So, latent learning, as promoted by Tolman, was an artifact created by a researcher who did not really understand rat behavior. In addition to the evidence of MacCorquodale and Meehl, others, like Barker ( 2001 ), pointed out that Tolman and Honzik's "unrewarded" subjects were still taken to their home cages after leaving the maze every ...
Uncovering the Insights: Tolman's Rat Experiments in Psychology
Latent Learning: Tolman's experiments demonstrated that rats could learn the layout of a maze without any reinforcement or rewards. The lab researchers only became aware of the rats cognitive maps when they introduced a reward.
What Is Latent Learning? Examples and Concept
Edward Tolman expanded on latent learning in the 1940s by introducing the concept of cognitive mapping. Like Blodgett, Tolman also experimented with rodent maze tests.
Latent learning, cognitive maps, and curiosity
Tolman and his graduate students placed rats in mazes like this one and found that they would explore the maze unrewarded and would demonstrably learn the features of the structure of the maze in the absence of rewards, a result that is difficult to explain using then-dominant simple stimulus-response learning theories.
Latent Learning (Definition + Examples)
Tolman's experiment provided significant insights into 'latent learning.' Even without immediate rewards, the rats in the third group had been learning and forming a cognitive map of the maze.
Latent learning, cognitive maps, and curiosity
Tolman and his graduate students placed rats in mazes like this one and found that they would explore the maze unrewarded and would demonstrably learn the features of the structure of the maze in the absence of rewards, a result that is difficult to explain using then-dominant simple stimulus-response learning theories.
Psych: Tolman'S Rats, Latent Learning, & Cognitive Maps
This video dives into Tolman's rat experiment, which helped him development the concepts of latent learning and cognitive maps.
Latent learning
One significant example of latent learning in rats subconsciously creating mental maps and using that information to be able to find a biological stimulus such as food faster later on when there is a reward. [ 3] These rats already knew the map of the maze, even though there was no motivation to learn the maze before the food was introduced.
Latent Learning
Latent learning is to learning that is not reinforced and not demonstrated until there is motivation to do so. Tolman argued that the rats had formed a "cognitive map" of the maze but did not demonstrate this knowledge until they received reinforcement.
Latent Learning: A Simple Explanation With Examples & More
Latent learning is linked to cognitive maps as it suggests that organisms, like rats in Tolman's experiments, can mentally construct maps of their environment through passive observation, enabling them to navigate effectively when motivation arises.
Of Rats And Men: Edward C. Tolman
The work of Edward C. Tolman broadened our understanding of humanity and paved the way for modern cognitive science. Commentator Tania Lombrozo waves the flag for the man and his ideas.
Latent Learning
Tolman started with discussing the concept of latent learning in rats in a maze. Fig. 1.6 shows the maze in which rats were trained to find food in a goal location, starting from another place in the maze. One group of animals was always rewarded by food and learned quickly given the same start and goal location every day. Two other groups of rats were also included in the experiment. One ...
Latent Learning
Latent learning is an acquisition of neutral information in the absence of external reinforcement or punishment. In latent learning, the acquisition of information does not lead to an immediate change in behavior until the subject is given an incentive to demonstrate the knowledge.
Narrative Summary of Cognitive Maps in Rats and Men
Latent Learning: I describe experiments demonstrating that rats learn without immediate rewards, highlighting the concept of "latent learning" and the building of internal maps that guide behavior later on.
Narrative Summary of The Concept of the Habit-Family Hierarchy and Maze
This summary explores Hull's theory of habit-family hierarchies, using maze learning as an example to explain how animals learn and transfer skills in novel situations.

Latent Learning In Psychology and How It Works

Cognitive maps as an example of latent learning in rats

Critical Evaluation

What is the difference between latent learning and observational learning?

What is the difference between implicit and latent learning?

How Latent Learning Works According to Psychology

Why Latent Learning Matters

Discovery of Latent Learning

Latent Learning Observations

Highly Complex Decision Makers

Latent Learning

Everyday Connection: This Place Is Like a Maze

Link to Learning

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Tolman’s Rat Experiments

Tolman’s Rat Experiments: Understanding Cognitive Maps

Key Definition:

History of Tolman’s Experiments

The Experiments

Psychological Concepts

How Tolman’s Discovery Challenges Behaviorism

A Narrative Example

Latent Learning in Action

Cognitive Processes Beyond the Maze

Mental Models

A Few Words by Psychology Fanatic

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