problem solving examples in animals

7 of the most impressive feats of animal intelligence

by Joseph Stromberg

Animals are far smarter than we ever realized.

If we’ve learned one thing from the past few decades of animal research, it’s that many species have much more going on inside their brains than we previously thought. Experiments show that animals can solve puzzles, learn words, and communicate with each other in remarkably sophisticated ways.

Here are a few of the most impressive feats we’ve seen thus far.

1) Crows can solve puzzles as well as five-year-olds

A series of recent experiments have revealed crows’ remarkably sophisticated problem-solving skills.

In one study conducted at the University of Auckland, researchers found that when presented with tubes of water that contained a floating treat, crows figured out that dropping other objects into the tubes would cause the water level to rise, making the treat accessible. They also figured out that they could get the treats fastest if they chose tubes with higher water levels to start, and if they dropped objects that sank, rather than ones that floated.

Other research , meanwhile, has shown that crows can intentionally bend a piece of wire in order to fish a treat out of a narrow tube. On the whole, researchers put their problem-solving skills roughly on par with those of 5 to 7 year-old children.

2) Dolphins call each other by unique names

Dolphins are remarkably intelligent in all sorts of ways . In captivity, they can be quickly trained to complete tasks for treats and are known to mimic human behavior solely for the fun of it. In the wild, they're been observed putting sponges over their snouts to protect themselves from spiny fish while hunting, and killing spiny fish so they can use their spines to extract eels from crevices.

a dolphin's whistle seems to be much like its name

But one of the most striking examples of how smart they are is the fact that each dolphin seems to have a characteristic whistle that represents itself. In other words, a dolphin’s whistle seems to be much like its name.

In experiments , dolphins will swim towards a speaker emitting the whistle of a family member much more often than an unknown dolphin's, and when a mother dolphin is separated from her calf, she'll emit the calve's whistle until they're reunited. Most recently, researchers found that dolphins behave differently upon hearing the whistle of a dolphin they'd last seen 20 years earlier, compared to a stranger's — they're much more likely to approach the speaker and whistle at it repeatedly, trying to get it to whistle back.

3) Elephants can cooperate and show empathy

For years, researchers in the field have observed elephants cooperating in sophisticated ways . Families of related elephants travel together in clans, communicating via low-frequency rumbles. At times, they'll form circles around calves to protect them from predators, or carry out coordinated kidnappings of calves from competing clans in shows of dominance.

More recently, the same levels of coordination have been observed in controlled experiments. In one , pairs of elephants quickly learned to pull on a rope at the same time to get a treat — and not to pull alone, as that would have ruined the chance of getting it.

Other work seems to suggest that elephants can show genuine empathy.

In general, animals show little interest in dead members of their species — typically, they briefly sniff them before walking away or eating them. Elephants, however, show a special interest in elephant remains, lingering near them and in some cases becoming agitated around them. One study quantified this behavior: when shown an elephant skull, African elephants spent twice as long looking at it as buffalo or rhino skulls, and they investigated sticks of ivory for six times as long as pieces of wood.

Finally, field researchers have observed elephants consoling each other — something seldom seen in other species. Typically, when an elephant becomes perturbed, it'll make squeaking noises and perk its ears up. Frequently, other elephants from the same clan will come and stroke its head with their trunks, or put their trunk in its mouth.

4) Dogs can learn hundreds of words

There are many different examples of canine intelligence , but one of the most remarkable is a border collie named Chaser . A psychology researcher named John Pilley has trained Chaser to recognize the names of 1,022 different toys. When Pilley names a specific toy, Chaser is able to retrieve the correct one more than 95 percent of the time.

Recently, Pilley taught Chaser verbs , in addition to nouns: she can follow instructions to pick a toy up, put her nose on it, or put her paw on it. All this took countless hours of training — and all dogs might not be capable of it — but it's still a remarkable achievement of canine intelligence.

5) Chimps are crazy good at memory puzzles

It may not be a huge surprise that chimps are smart, given that they’re our closest relatives. But the degree of their intelligence — and, in some areas, the way it rivals human intelligence — is remarkable.

A chimp named Ayumu who lives at a research institute in Kyoto, Japan, for instance, has become world-famous for his performance on a speed and memory-based game. As part of the game, 9 numbers are shown are shown in particular spots on a screen for a fraction of a second, and the player must remember their location and reproduce it afterward. You can play a simplified version of the game here .

ayumu is better than any human who's challenged him thus far

Ayumu is not only capable of playing this game, but is better than any human who’s challenged him so far. When the numbers are shown for an extremely short amount of time (as little as 60 milliseconds), Ayumu is significantly more accurate than people, including college students and memory champions.

Scientists still don't entirely understand how he's so good, but they hypothesize he's doing something called subitizing — looking at a number of objects and immediately taking them in without sequentially counting them. Most humans can do this for up to four items, but Ayumu may be capable of doing it for many more.

6) Cockatoos can pick locks

Cockatoos, like crows, can solve difficult puzzles in order to get treats. And a 2013 study showed just how complex these puzzles can be: they required the birds to open a box (which contained a cashew) by removing a pin, unscrewing a screw, pulling out a bolt, turning a wheel, and finally sliding out a latch.

Obviously, this takes a long time for an animal that doesn’t have opposable thumbs. But one cockatoo worked at it for a full two hours, ultimately solving the puzzle and showing that the birds are capable of striving towards goals that are much more distant than the researchers had previously thought.

Other birds in the experiment, meanwhile, learned from the first bird and completed the whole puzzle much more quickly. And when the puzzle was altered so that the five steps had to be completed in a different order, the birds seemed to understand this, and attacked it accordingly instead of trying to replicate the previous solution.

7) Octopuses are weirdly intelligent in ways we don’t understand

(DeAgostini/Getty Images)

Octopus intelligence is tough to study for a few reasons: they’re aquatic, difficult to keep alive in captivity, and most live relatively deep in the ocean. Most importantly, octopuses inhabit an environment dramatically different than ours — so it stands to reason that their intelligence is directed at solving very different goals.

But some scientists believe that they're smart in ways that are qualitatively different from us and the other species on this list. One reason is that they have the largest brains of any invertebrate — but though they actually have more neurons than humans, sixty percent of these cells are in their arms, not their brains. As a result, t heir arms seem to be individually intelligent: when cut off, they can crawl away, grab food items, and lift them up to where the octopus' mouth would be if they were still connected.

Meanwhile, octopuses seem to have a keen sense of aesthetics, even though they're likely colorblind. F ield researchers have observed octopuses collect rocks of a specific color to camouflage their den, and many species can change color to blend in with their environment. The way they accomplish this, it's hypothesized, is that they actually sense color with their skin itself and respond accordingly.

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Problem Solving in Animals: Proposal for an Ontogenetic Perspective

Misha k. rowell.

1 College of Science and Engineering, James Cook University, P. O. Box 6811, Cairns, Queensland 4870, Australia; [email protected]

2 Centre for Tropical Environmental and Sustainability Sciences, James Cook University, P. O. Box 6811, Cairns, Queensland 4870, Australia

Neville Pillay

3 School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg 2000, South Africa; [email protected]

Tasmin L. Rymer

Simple summary.

Animals must be able to solve problems to access food and avoid predators. Problem solving is not a complicated process, often relying only on animals exploring their surroundings, and being able to learn and remember information. However, not all species, populations, or even individuals, can solve problems, or can solve problems in the same way. Differences in problem-solving ability could be due to differences in how animals develop and grow, including differences in their genetics, hormones, age, and/or environmental conditions. Here, we consider how an animal’s problem-solving ability could be impacted by its development, and what future work needs to be done to understand the development of problem solving. We argue that, considering how many different factors are involved, focusing on individual animals, and individual variation, is the best way to study the development of problem solving.

Problem solving, the act of overcoming an obstacle to obtain an incentive, has been studied in a wide variety of taxa, and is often based on simple strategies such as trial-and-error learning, instead of higher-order cognitive processes, such as insight. There are large variations in problem solving abilities between species, populations and individuals, and this variation could arise due to differences in development, and other intrinsic (genetic, neuroendocrine and aging) and extrinsic (environmental) factors. However, experimental studies investigating the ontogeny of problem solving are lacking. Here, we provide a comprehensive review of problem solving from an ontogenetic perspective. The focus is to highlight aspects of problem solving that have been overlooked in the current literature, and highlight why developmental influences of problem-solving ability are particularly important avenues for future investigation. We argue that the ultimate outcome of solving a problem is underpinned by interacting cognitive, physiological and behavioural components, all of which are affected by ontogenetic factors. We emphasise that, due to the large number of confounding ontogenetic influences, an individual-centric approach is important for a full understanding of the development of problem solving.

1. Introduction

Increasing concerns over human-induced rapid environmental change has led to a corresponding increase in interest in understanding how animals will cope with these challenges. Rapid and unpredictable changes may have significant effects on survival and coping ability [ 1 ]. In order to survive, animals need to gain information about the environment (e.g., relative predation risk and food availability). While this might sometimes be easily attained, such as directly observing fruit on a tree, obtaining resources or avoiding predation may require an ability to solve a problem, such as obtaining fruit that is out of reach.

Problem solving has been documented in all major vertebrate taxa, including mammals (e.g., food-baited puzzles in various mammalian carnivores, [ 2 ]), birds (e.g., food-baited puzzles given to multiple parrot and corvid species [ 3 , 4 ]), reptiles (e.g., multiple species of monitor lizards Varanus spp. are capable of solving food-baited puzzle boxes, [ 5 ]), amphibians (e.g., detour task, where the animal had to move around an obstacle in brilliant-thighed poison frogs Allobates femoralis , [ 6 ]), fishes (e.g., foraging innovation in guppies Poecilia reticulata , [ 7 ]), and some invertebrates (e.g., overcoming a physical barrier in leaf-cutting ants Atta colombica [ 8 ]).

Currently, there is no universally accepted definition of problem solving ( Table 1 ). From our literature search (see below), most definitions consider mechanical (i.e., movements required to solve problems), morphological (i.e., physical structure to manipulate objects to solve a problem) and/or cognitive (i.e., assessing, learning, storing information about problem) components as part of problem-solving ability. We consider problem solving to be the ability of an individual to integrate the information it has gained (knowledge or behaviour) to move itself, or manipulate an object, to overcome a barrier, negative state or agent, and access a desired goal or incentive, such as a resource [ 9 , 10 ]. Most reports of problem solving are based on experimental evidence where animals are presented with a feeding motivation task (e.g., a puzzle box or detour task), in which an animal manipulates an object, or moves itself around the object, to access the food. Occasionally, animals are experimentally presented with an obstacle blocking access to a location, and the animal needs to move the obstacle to access a refuge or their nest. These solutions can be achieved by innovation (the use of a new behaviour, or existing behaviour in a new context [ 11 ]) and/or by refining behaviour over repeated sessions with the stimulus (e.g., trial-and-error learning). Our literature search has also demonstrated that problem solving is sometimes assessed simply as a dichotomous skill, in which an animal either can or cannot solve a problem, but other studies have focused on how animals vary in the way they solve problems, and how efficiently they solve problems. Our definition encompasses all of these aspects.

Definitions of problem solving and innovation quoted from the literature and associated references. We highlight the drivers (i.e., whether the ability to problem solve is linked to internal (e.g., physiology, cognition) or external (e.g., environmental) factors) and the properties of the animal (mechanical/morphological abilities or cognitive abilities) that authors attribute to problem solving.

Successful problem solving has been theorised to be important for survival, as it allows animals to adjust to changing environmental conditions [ 24 ] and even invade new environments (e.g., bird species introduced to New Zealand, [ 25 ]), or to cope with harsh or extreme conditions [ 26 ]. However, the ability of animals to solve problems [ 27 ], and the specific strategy/manoeuvre that they use to solve problems [ 28 ], is highly variable, and this variation can be observed at all taxonomic levels, including between families (e.g., Columbida vs. Icteridae, [ 29 ]), genera (e.g., Molothrus vs. Quiscalus [ 30 ]), and species (jaguar Panthera onca vs. Amur tiger P. tigris , [ 2 ]). It is even possible that problem solving is phylogenetically conserved, with some groups having a greater potential to solve problems than others [ 31 ]. However, variation in problem-solving ability also occurs within species, including between populations (e.g., house finches Haemorhous mexicanus given extractive foraging tasks [ 32 ]), and individuals (e.g., meerkats Suricata suricatta given food-baited puzzle boxes [ 27 ]). Likely causes of this variation are the conditions that arise during an individual’s development. This variation could then allow problem-solving ability to be acted upon by natural selection [ 33 ], possibly impacting individual fitness. Therefore, understanding the influence of developmental factors on problem-solving ability is important.

An individual’s behaviour, physiology and morphology may change as it grows and ages due to developmental changes in life history traits [ 34 , 35 ]. Furthermore, interactions and experiences with other individuals and the immediate environment further feedback into these systems [ 36 ]. These intrinsic and extrinsic factors, either independently or synergistically, influence the individual’s ability to cope with, and respond to, environmental challenges [ 37 ], although their outcomes are likely difficult to predict because of myriad interacting factors.

Although aspects of behaviour, physiology and cognition have been studied in an ontogenetic context [ 38 , 39 ], little is currently known about how problem-solving abilities develop and change as individuals grow and age. Developmental differences between individuals could fine tune or modulate the ability to solve problems, causing individual variation in this ability. Importantly, this inter-individual variation in problem solving could have fitness consequences by influencing survival and/or reproductive success. However, untangling the relative influence of intrinsic (genetic, neuroendocrine and aging) and extrinsic (environmental) factors on the development of problem solving is challenging [ 40 , 41 ]. We propose that an integrated approach, focusing on the development of problem solving, is needed to fully appreciate the ability and propensity of animals to solve novel problems. Our aim was to review the literature on problem solving to document and then construct the links between intrinsic and extrinsic factors that influence the development of problem-solving.

We therefore conducted a literature search using Google Scholar and the Web of Science database. We included the general search terms “problem solving” “innovation” and “animal” in all searches and excluded all articles with the word “human”. This produced 6100 hits. We further refined the search by including the following as specific terms in individual searches: “development”, “ontogeny”, “heritability”, “personality”, “cognition”, “learning”, “experience”, “age”, “hormone”, “brain”, and “environment”. Articles that were repeated in subsequent searches were ignored. Articles were excluded if: (1) the researchers trained the animals to solve the problem before testing (and, therefore, tested memory rather than natural problem-solving ability); (2) the authors referred to a type of problem solving that did not meet our definition (e.g., relational problems where animals needed to extract and transfer rules between tests); and/or (3) development of problem solving was not investigated. If two papers found similar results (e.g., neophobia hinders problem solving in a bird species), we only reported on one study to avoid repetition and to reduce the overall number of citations.

Numerous studies have shown that animals can problem solve [ 42 ], and several studies have explored the fitness consequences of problem solving in animals (e.g., [ 10 ]). However, how problem solving develops is an area that has been little explored. In this paper, we first discuss how intrinsic and extrinsic factors influencing the ontogeny of individuals could affect the development of problem-solving ability. We focus on genetic (direct and indirect), neuroendocrine, and environmental (physical and social) factors, as well as age, learning and experience. Given the relative paucity of empirical studies investigating the development of problem solving in general (42 publications found of seven developmental factors), we demonstrate first how these factors impact other traits in order to create a conceptual framework for addressing problem solving. We acknowledge that limited information currently makes it challenging to separate developmental factors underlying problem-solving ability from other causal mechanisms (e.g., hormones, genetic effects). We then explore how interactions between intrinsic and extrinsic factors during an individual’s development could influence problem solving indirectly. Specifically, we focus on how personality (individual differences in behaviour) and behavioural flexibility (ability to change behaviour in response to environmental cues) contribute to differences in problem-solving ability. Finally, we briefly discuss aspects that have been overlooked in studies investigating the development of problem solving, providing hypotheses for future testing. Throughout this paper, we advocate for an individual-centric approach to study the ontogeny of problem solving, where individual variation in solving ability is considered, rather than only using simple population-level averages. Future studies should be tailored to focus on individual differences within and between tests, as well as consider a longitudinal approach to track how individuals change over their lifetimes. Analyses of these experiments should then include individual data points as a measure of individual ability and variation, and should not exclude outliers because these account for the species- or population-level variation.

2. Factors Affecting the Development of Problem Solving

Problem solving is influenced by direct [ 43 ] and indirect (epigenetic and transgenerational) genetic [ 44 ], and neuroendocrine [ 45 ] factors ( Figure 1 ). Furthermore, extrinsic factors, including both the physical and social environments, can also affect the development of problem solving ( Figure 1 ). However, the development, and ultimately expression, of problem solving is more likely impacted by complex interactions between these intrinsic and extrinsic factors ( Figure 1 ), and is also likely to change as the animal ages and experiences (i.e., learns) new situations (e.g., ravens Corvus corax [ 28 ]; North Island robins Petroica longipes , [ 46 ]). Untangling these effects is likely to be challenging.

Intrinsic (genetic, neuroendocrine, and aging), extrinsic (environment) and acquired (learning and experience) factors influencing an individual’s development directly (solid arrows) or indirectly (dashed arrows). Arrow heads indicate direction of influence.

2.1. Instrinic Factors

2.1.1. direct genetic effects.

Heritable genetic effects influence the development of phenotypic traits. For example, physiological stress (barn swallows Hirudo rustica , [ 47 ]), parental care (African striped mice Rhabdomys pumilio [ 48 ]), exploratory behaviour (great tits Parus major [ 49 ]), multiple aspects of cognition in chimpanzees Pan troglodytes [ 50 ], learning in hens Gallus gallus domesticus [ 51 ] and spatial learning ability (C57BL/6Ibg and DBA/2Ibg mice Mus musculus [ 52 ]) all have a heritable component (but see [ 53 ]).

Heritable genetic effects may also affect the development of problem solving ( Figure 1 ), although this has received little attention in the literature. Elliot and Scott [ 43 ] found that different dog Canis lupus familiaris breeds solved a complex barrier problem in different ways, and Audet et al. [ 54 ] showed that an innovative species of Darwin’s finches Loxigilla barbadensis had higher glutamate receptor expression (correlated with synaptic plasticity) than a closely related, poorly innovative species Tiaris bicolor . Tolman [ 55 ] and Heron [ 56 ] also indicated underlying genetic effects on maze-learning ability in rats, although the ability to learn a maze may not necessarily imply an ability to solve a problem (see [ 57 ]). In contrast, Quinn et al. [ 58 ] and Bókony et al. [ 59 ] found little measurable heritability of innovative problem-solving performance in great tits in a food-baited puzzle box and an obstacle-removal task, respectively. These studies suggest that the genetic architecture underlying problem solving may provide a rich area for future research.

2.1.2. Indirect Genetic Effects

Indirect genetic factors, specifically epigenetic and transgenerational effects, influence how genes are read (e.g., DNA methylation, [ 60 ]) or expressed (e.g., hormones activating genes during sexual maturation, [ 61 ]) without altering the underlying DNA. These epigenetic changes are underpinned by biochemical mechanisms that affect how easily the DNA can be transcribed [ 62 ], subsequently influencing the development of different systems. For example, the activation of thyroid receptor genes (TRα and β) in the cerebellum of 0–19 day old chicks causes hormone-dependent neuron growth and development [ 63 ]. No studies to date have explored the effects of epigenetic factors on the development of problem solving, although this relationship can be postulated ( Figure 1 ), since epigenetic factors influence the development of behaviour (e.g., maternal care, [ 64 ]), and cognition (e.g., memory, [ 44 ]). Memory is an important component of problem solving [ 65 ]. Consequently, two possible routes could be inhibited via transcriptional silencing of the memory suppressor gene protein phosphatase 1 (PP1), and demethylation and transcriptional activation of the synaptic plasticity gene reelin, both of which enhance long-term potentiation. These could lead to increased memory formation (e.g., in male Sprague Dawley rats Rattus norvegicus domesticus , [ 44 ]).

Transgenerational epigenetic effects can also influence development. These effects result from parental or grandparental responses to prevailing environmental conditions, which influence how offspring and grand offspring ultimately respond to their own environment [ 66 ]. For example, embryonic exposure to the endocrine disruptor vinclozilin in female Sprague Dawley rats resulted in epigenetic reprogramming of hippocampal and amygdala genes for at least three generations, with the resulting F3 males showing decreased, and F3 females showing increased, anxiety-like behaviour, as adults [ 67 ]. An interesting avenue for research into transgenerational effects on the development of problem solving is the NMDA (N-methyl-D-aspartate) receptor/cAMP (cyclic adenosine monophosphate)/p38 MAP kinase (P38 mitogen-activated protein kinases) signalling cascade. Exposure of newly weaned Ras-GRF1 (growth regulating factor) knockout mice to an enriched environment enables this latent signalling pathway, rescuing defective long-term potentiation and learning ability [ 68 ]. These epigenetic effects may therefore influence problem-solving ability indirectly by affecting the individual’s learning ability, or possibly directly by affecting the development of particular brain regions.

2.1.3. Neuroendocrine Effects—Brain Morphology

Many developmental processes are driven by neuroendocrine factors that are, themselves, impacted by other developmental processes [ 63 ]. While the development of many of the brain’s circuits (e.g., those located near the sensory or motor periphery), are governed by innate mechanisms [ 69 ], other parts (e.g., the basolateral nucleus of the amygdala and the cerebellar cortex [ 70 ]; the CA1 region of the mammalian hippocampus [ 71 ]; the avian hippocampus [ 72 ]) are considerably more plastic and more responsive to external stimuli, maintaining a high degree of neural plasticity throughout life. As these brain regions can be important for the expression of particular behaviours (e.g., the cerebellum is necessary for tool use, [ 73 ]), this plasticity has particular relevance for problem solving. For example, North American bird species with relatively larger forebrains were more likely to innovate when foraging than bird species with smaller forebrains [ 74 ] and New Caledonian crows Corvus moneduloides , which are renowned for their tool use and problem-solving abilities, had relatively larger brains than other bird species [ 75 ]. Similarly, C57BL/6J laboratory mice that received lesions to the hippocampus and medial prefrontal cortex initially showed impairments in solving a puzzle box task, although the mice ultimately solved the task over time, indicating the importance of experience and learning with repeated presentation of the task [ 76 ].

2.1.4. Neuroendocrine Effects—Hormones

The brain is also the central control of endocrine responses that can influence an individual’s development ( Figure 1 ). For example, the hypothalamic-pituitary-gonadal (HPG) axis activates gonadotropin-releasing hormone (GnRH), which stimulates the pituitary to produce luteinizing hormone (LH) and follicle-stimulating hormone (FSH, [ 77 ]). These hormones regulate the production of steroid hormones (testosterone and oestrogen) via the gonads [ 78 ], stimulating sexual maturity [ 79 ]. Fluctuations in steroids also influence cognitive function [ 80 , 81 ]. For example, female rats injected neonatally with testosterone show heightened learning of a Lashley III maze (contains start box, maze, and goal box; used to test learning and memory) as adults compared to non-injected females, although the underlying impacts on neural development or neuroendocrine processes were not discussed [ 82 ].

Endocrine responses can also feedback to brain morphology ( Figure 1 ), affecting neural structure and function, which can impact behaviour, cognition, and development. The hypothalamic-pituitary-adrenocortical (HPA) axis regulates the secretion of adrenocorticotropic hormone (ACTH), which in turn regulates the secretion of glucocorticoid stress hormones (e.g., corticosterone, [ 83 ]) from the adrenal glands [ 84 ]. Short-term exposure to corticosterone can improve learning, since it allows important associations to be formed, such as between threat and a behavioural response [ 85 ]. However, prolonged increased corticosterone concentrations (chronic physiological stress) reduce hippocampal neuron survival [ 86 ], which interferes with learning [ 87 , 88 ], memory retrieval [ 89 ] and problem solving. For example, house sparrows Passer domesticus with prolonged elevated corticosterone concentrations were less efficient problem solvers of puzzle boxes than birds with lower corticosterone concentrations, as stress impairs working memory and cognitive capacity [ 45 ]. Prolonged physiological stress can also cause detrimental developmental changes in morphology (e.g., chickens [ 90 , 91 ]) and behaviour (e.g., rats [ 83 ]).

In contrast to stress hormones, the mesolimbic dopaminergic system [ 92 ], which consists of the substantia nigra and ventral tegmental region [ 93 ], regulates the production of dopamine, a hormone associated with motivation and reward-seeking [ 94 ]. Motivation is a physiological process [ 94 ] that increases persistence and thereby increases the likelihood of successfully solving a problem [ 95 ]. Persistence is important for problem solving in foraging tasks in house sparrows [ 96 ], common pheasants Phasianus spp . [ 97 ] and Indian mynas Acridotheres tristis [ 98 ], and in puzzle box tasks in spotted hyenas Crocuta crocuta and lions P. leo [ 99 ]. Changes to dopamine production can also negatively impact the development of sensorimotor integration [ 100 ], disrupting approach, seeking and investigatory behaviours [ 101 ] and acquisition of spatial discrimination [ 102 ]. Disruption to dopamine production, or other circuits, may also lead to an individual persisting with an inadequate strategy if the individual lacks inhibitory control [ 103 ] and cannot recognise when to terminate the behaviour [ 104 ]. Disruptions to these behaviours and cognitive functioning therefore impact foraging and exploratory behaviours [ 87 , 104 ], which can lead to undernutrition, and consequent negative impacts on growth and physical, behavioural, and cognitive development [ 105 ].

Other hormones have also been implicated in the expression of problem solving. For example, both norepinephrine and serotonin likely impact problem solving, since they are related to cognitive flexibility (e.g., rhesus macaques Macaca mulatta [ 106 , 107 ]), with serotonin activating, and norepinephrine deactivating, the prefrontal cortex [ 108 ]. However, although some studies have investigated the role of these hormones in problem solving, these relationships are not clearly defined. For example, dietary deficiency in n-3 fatty acids during development increased serotonin receptor density and reduced dopamine receptor binding in the frontal cortex of rats, and it also altered dopamine metabolism [ 109 , 110 ]. This dietary n-3 fatty acid deficiency also impaired problem solving in a delayed matching-to-place task in the Morris water maze [ 111 ]. However, whether problem-solving ability was impacted specifically by down-regulation of dopamine receptor binding, or up-regulation of serotonin receptor binding, is unclear.

2.2. Extrinsic Factors

2.2.1. physical environmental factors.

The physical environment varies in structural complexity and quality across both spatial and temporal scales [ 112 ]. Throughout its lifetime, an individual will experience daily and/or seasonal variation in environmental conditions (e.g., rainfall, temperature, food availability, [ 113 ]), and/or when it disperses [ 114 ], migrates [ 115 ] or travels into different areas. This variability changes the likelihood of an individual encountering positive (e.g., food [ 116 ]) or negative (e.g., predator [ 117 ]) stimuli, consequently influencing its development ( Figure 1 ). For example, a higher density and abundance of aquatic snails results in the development of larger pharyngeal jaw muscles and stronger bones in predatory pumpkinseed sunfish Lepomis gibbosus [ 111 ].

Some studies have investigated the interplay between physical environmental conditions and problem-solving ability. Favourable environmental conditions can reduce stress [ 118 ], promote active and exploratory behaviours [ 119 ] and enhance cognition [ 120 ], but harsh conditions may promote problem solving. For example, mountain chickadees Poecile gambeli living in harsher high elevation montane habitats with longer winters solved novel foraging problems significantly faster than chickadees living at lower elevations, most likely because finding food in these habitats was more challenging, and survival depends on plastic responses to these challenges [ 26 ]. However, this effect on food-motivated problem-solving ability was not seen in great tits experiencing similar harsh conditions [ 40 ], suggesting that species-dependent developmental factors may be constrained by environmental effects. Urban environments may also promote the development of problem solving since they are expected to contain a higher frequency of novel problems for animals to solve. For example, house sparrows [ 121 ] and house finches [ 32 ] in urban environments were more adept food-motivated problem solvers than birds from rural areas, particularly when the problem was difficult to solve [ 96 ].

2.2.2. Social Environmental Factors

The social environment also changes throughout an individual’s lifetime, and has the capacity to influence its development ( Figure 1 ). Any positive (e.g., offspring suckling from mothers) or negative interactions (e.g., siblings fighting over food) between individuals can be considered social, and can vary over time scales (e.g., from daily interactions between individuals in a group, to shorter interactions between parents and offspring or mating partners [ 122 ]).

For mammals, females are constrained to care for their offspring through pregnancy and suckling [ 123 ]. Consequently, the mother’s physiological state and access to resources can impact offspring embryonic development prenatally through direct transfer of maternal hormones or nutrients across the placenta [ 124 ]. For example, pregnant female Sprague Dawley rats exposed to unpredictable, variable stress (e.g., restraint, food restriction) during the final week of gestation produced anxious daughters and sons with impaired cognitive function (contextual memory [ 125 ]). Furthermore, maternal care during postnatal development [ 64 ], particularly the mother’s diet quality, can also influence development. For example, protein deficiency in African striped mouse Rhabdomys dilectus chakae mothers during early postnatal development of offspring resulted in these offspring showing increased anxiety, decreased novel object recognition and increased aggression as adults compared to mice raised by mothers that did not experience nutrient deficiency [ 126 ]. Thus, detrimental developmental effects such as these may go on to impede offspring problem solving abilities.

For some species, a key developmental milestone is dispersal. Interactions with other conspecifics during this phase are often driven by dramatic developmental changes often associated with reproduction [ 114 ]. For example, male vervet monkeys Chlorocebus pygerythrus leave their natal group at sexual maturity and attempt to attain dominance in another group [ 127 ], which could lead to increased access to food resources that can be channeled further into growth and development. This process of leaving the natal territory, and any social interactions during this time, can feedback to the individual to further affect its development. For example, in many species (e.g., brown rats), dispersing juveniles undergo a period of heightened exploration and learning, allowing them to rapidly adjust to new environmental conditions [ 128 ]. However, it is unknown how dispersal and other associated events impact an individual’s problem-solving abilities.

Problem solving is most often studied in social animals [ 122 ], possibly because they are more conspicuous than solitary species. In some species, such as European starlings Sturnus vulgaris with a foraging task [ 129 ], coyotes Canis latrans with a puzzle box task [ 130 ] and rhesus macaques in an associative learning task [ 131 ], dominant individuals are better learners and problem solvers. Similarly, the presence of an alpha individual impedes problem solving success in subordinate spotted hyenas presented with a puzzle box [ 132 ] and ravens in a string-pulling task [ 28 ] due to direct interference and increased aggression from the dominant. However, in other species, such as blue tits Cyanistes caeruleus [ 133 ], adult meerkats [ 27 ] and chimpanzees [ 134 ], subdominants tend to be better solvers of puzzle boxes, since their lower competitive ability makes them more reliant on alternative methods for accessing resources [ 26 ]. Group size may also influence problem solving, although results are equivocal. For example, larger groups of house sparrows [ 121 ] and Australian magpies Gymnorhina tibicen [ 135 ] in extractive foraging tasks and zebra fish Danio rerio in an avoidance task [ 136 ] were better problem solvers than individuals in small groups, possibly because larger groups contained more reliable demonstrators. However, orange-winged amazons Amazona amazonica had similar solving success in a string-pulling task when tested in groups or in isolation [ 137 ]. Social carnivore species, such as banded mongoose Mungos mungo , were also less successful problem solvers of a puzzle box compared to solitary species, such as black bears Ursus americanus and wolverines Gulo gulo , suggesting that relative brain size may be more important for cognitive abilities than social environment [ 33 ].

Problem solving studies in solitary species are generally lacking, making it difficult to assess how social interactions may impact the development of problem solving in these species. However, it is evident that individual animals can solve problems in the absence of conspecifics. For example, black-throated monitor lizards V. albigularis albigularis [ 138 ], eastern grey squirrels Sciurus carolinensis [ 139 ], and orangutans Pongo pygmaeus [ 140 ] can individually solve puzzle boxes using flexible behaviours (i.e., switching strategies when necessary), persistence and learning. Similarly, North Island robins [ 46 ] and brilliant-thighed poison frogs [ 8 ] can solve detour problem tasks when tested in their home territories. How solitary species solve problems in the presence of conspecifics, however, is an area for future investigation.

3. Interacting Factors that Influence the Development of Problem Solving

3.1. gene × environment interactions.

Genotype × environment interactions can also have a profound effect on the development of individuals ( Figure 1 ). For example, the gene monoamine oxidase A ( MAOA ) encodes for an enzyme that impacts serotonergic activity in the central nervous system, leading to increased impulsivity and anxiety [ 141 ]. Stressful life events, or changes in social structure or status can alter the expression of this gene, leading to developmental changes during adulthood. For example, rhesus macaques raised in the absence of their parents showed increased aggression due to low MAOA enzymatic activity [ 142 ].

Although genotype × physical environment interactions have not been explored in the context of problem solving, environmental enrichment in captive bi-transgenic CK-p25 Tg laboratory mice is associated with the activation of plasticity genes, inducing chromatin modification via histone acetylation and methylation of histones 3 and 4 in the hippocampus and cortex, leading to increased numbers of dendrites and synapses [ 143 ]. This cascade of genetic and neuroendocrine processes functions to help restore learning and memory [ 143 ], both of which are important for problem solving [ 65 , 95 ].

Parents may also alter the environment (e.g., amount of parental care or food) their offspring experience [ 66 ], which could be a consequence of genetic variation between mothers [ 144 ] or a result of other factors (e.g., variability in resource availability [ 145 ]). When an offspring’s development is impacted by this nongenetic parental environment, these effects are known as parental effects [ 146 ], which are specific types of indirect genetic effects (IGEs, [ 144 ]). For example, female Long-Evans hooded rats that provided high levels of tactile stimulation (e.g., grooming and nursing [ 64 ]) to their young produced daughters that also displayed higher levels of maternal care to their own offspring [ 147 ], indicating an IGE.

Maternal care also regulates the expression of the hippocampal glucocorticoid receptor gene by changing the acetylation of histones H3-K9 and the methylation of the NGFI-A consensus sequence on the exon 17 promoter [ 148 ]. Young rats that experienced low levels of maternal tactile stimulation showed reductions in hippocampal neuron survival [ 149 ] and decreased hippocampal glucocorticoid receptor mRNA expression [ 148 ], leading to chronic corticosterone release as adults [ 150 ]. Offspring also showed decreased exploratory behaviour [ 151 ] and impairments in spatial learning and memory [ 64 ] and object recognition [ 149 , 152 ] as adults. As for genotype × physical environment interactions, how the social environment × genotype interaction affects problem solving is a promising avenue for future research.

3.2. Neuroendocrine × Environment Interactions

Habitat complexity, resource availability and social complexity can influence development via effects on neuroendocrine systems, which can also result in changes to the social environment that may then feedback to further impact development. For example, nine-spined sticklebacks Pungitius pungitius preferentially shoal together in marine environments with high predation risk and patchy food resources, but prefer to swim alone when these constraints are relaxed in freshwater ponds [ 153 ]. Marine fish with more social interactions had significantly larger olfactory bulbs and optic tecta, parts of the brain associated with sensory perception, compared to solitary fish from freshwater ponds that experienced fewer social interactions [ 154 , 155 ]. Rhesus macaques from larger social groups also had more grey matter and greater neural activity in the mid-superior temporal sulcus and rostral prefrontal cortex than macaques from smaller groups [ 156 ]. Similarly, structurally complex, changing environments improve survival of hippocampal cells and neurons by increasing the level of nerve growth factor in the hippocampus [ 112 ], which increases hippocampal volume [ 83 ], leading to increased neural plasticity [ 157 ] and a greater capacity to adjust to new environmental conditions [ 158 ]. Environmental enrichment has also been shown to enhance long-term potentiation in the hippocampus, which facilitates learning and memory [ 159 ], two important processes for problem solving [ 23 , 95 ]. Environmental enrichment has been associated with increased problem-solving ability in C57/BL6J mice in an obstruction puzzle task [ 160 ] and Labrador retrievers in puzzle box tasks [ 161 ]. This suggests causal links between the environment, the neuroendocrine system, and problem solving which are likely mediated by underlying genotype × environment interactions.

3.3. Age Effects

Separating out the effects of aging and neuroendocrine or genetic effects on development is challenging. Nevertheless, age-specific effects on development, regardless of the underlying mechanisms, are an important consideration.

The nervous system shows age-dependent decreases in neurogenesis and plasticity, particularly in the dentate gyrus of the hippocampus [ 162 ], and the subventricular zone of the lateral ventricle [ 163 ], and these age-dependent changes can alter cognitive ability and behaviour (e.g., beagles [ 164 ]). Other neuroendocrine processes also naturally change with age. For example, as brown rats age, the ACTH response increases, glucocorticoid receptor binding capacity in the hippocampus and hypothalamus decreases, corticotropin releasing hormone (CRH) mRNA expression decreases in the paraventricular nucleus, and mineralocorticoid mRNA expression in the dentate gyrus of the hippocampus is reduced [ 165 ]. These changes result in an associated attenuation of the corticosterone response to novelty [ 164 ], as well as declines in spatial learning and memory [ 166 ].

Depending on the age of the individual, changes to both the physical and social environments also impact development [ 167 ]. When raised in small cages with limited space, juvenile rats showed increased anxiety, and lower activity and exploration, whereas older rats did not [ 167 ]. Similarly, older rats reared in larger groups were more active than juveniles, mostly likely due to increased frequency of social interactions and establishment of their rank within the social hierarchy [ 167 ].

Several studies have shown that juveniles are better problem solvers than adults, although the underlying mechanisms are currently not known. For example, juvenile Chimango caracaras Milvago chimango were more successful at solving a puzzle box task than adults [ 168 ], and juvenile canaries Serinus canaria solved a vertical-string pulling task, whereas adults did not [ 169 ]. Similarly, juvenile Chacma baboons Papio ursinus solved a hidden food task more often than adults [ 170 ], and juvenile kakas N. meridionalis showed higher innovation efficiency than adults across different tasks and contexts [ 171 ]. Juveniles are often prone to higher levels of exploration [ 159 ], and are more playful [ 172 ], than adult animals, allowing juveniles to rapidly gain motor skills [ 172 ]. This could possibly improve problem solving abilities of juveniles despite their lack of experience at solving tasks. However, results are species-specific, as Indian mynas [ 173 ] and spotted hyenas [ 174 ] show no age-specific effects on problem solving in foraging tasks, while adult meerkats [ 27 ] and black-capped chickadees [ 175 ] were better innovators than juveniles in extractive foraging tasks.

3.4. Learning and Experience

As an animal ages, it encounters predators and food resources, and interacts with conspecifics. These experiences provide a rich potential for learning, which is a critical component of problem solving. However, separating out the effects of the experience itself on development from other extrinsic and intrinsic factors, or their interactions, is challenging. Nevertheless, as in aging, an animal’s development can be impacted by its experiences, particularly via learning, suggesting that experience must be considered when attempting to understand how problem solving develops.

To survive, use new resources, or avoid predators, individuals must learn to associate the experience with its significance (e.g., threat of a predator [ 176 , 177 ]). Learning enables animals to acquire information about the state of their environment [ 178 ] and learning through experience allows for adjustments in physiological and behavioural responses [ 176 ]. For example, repeated foot shock in a specific environmental location caused increases in norepinephrine and epinephrine in Sprague Dawley rats, eliciting fear and resulting in rats avoiding that location [ 179 ]. Similarly, guppies decreased their time foraging in the presence of a predatory convict cichlid Cichlasoma nigrofasciatum [ 180 ]. Animals can learn to solve problems in different ways, such as through trial and error (e.g., rooks C. frugilegus across multiple foraging extraction tasks [ 181 ]) or socially through local enhancement (e.g., common marmosets Callithrix jacchus in a foraging extraction task [ 182 ]), social facilitation (e.g., capuchin monkeys Cebus apella in a foraging extraction task [ 183 ]) or copying/imitation (e.g., laboratory rats in an extractive foraging task [ 184 ]). Learning from previous experience is also an important component for successful problem solving. For example, grey squirrels improve their ability to solve a food-baited puzzle box with repeated exposures to the problem [ 23 ]. Similarly, North Island Robins became more efficient problem solvers of new food-extraction tasks with experience [ 46 ].

3.5. Behavioural Flexibility and Personality

Although development is governed by several unifying genetic and physiological mechanisms, and these processes are impacted by age and environmental effects [ 185 ], the development of one individual differs considerably from that of another individual. Some of this variation can be attributed to the behavioural flexibility of each individual [ 29 ] and/or its personality [ 168 ], which also undergo developmental changes over the course of an individual’s lifetime [ 36 ].

Behavioural flexibility is the ability to switch behavioural responses (likely due to cognitive flexibility [ 95 ]) to adjust to new situations or states [ 186 ], and is likely governed by both genetic and non-genetic mechanisms [ 187 ]. The degree of behavioural and cognitive flexibility, and corresponding learning ability, is important for problem solving, as seen in tropical anoles ( Anolis evermanni in an obstruction task [ 188 ]; A. sagrei in a detour task [ 189 ]), spotted hyenas in a puzzle box task [ 174 ], grey squirrels in a food-extraction task [ 139 ] and keas Nestor notabilis in a foraging extraction task [ 190 ]. However, the degree of flexibility varies between species. For example, Indian mynas are more flexible, and are better innovative foraging problem solvers, than noisy miners Manorina melanocephala across a range of tasks [ 173 ]. Importantly, individual differences in behavioural and cognitive flexibility, particularly learning ability, are often attributed to physiological effects occurring during development (e.g., corticosterone exposure in nestling Florida scrub jays Aphelocoma coerulescens [ 191 ]).

An individual’s development and experiences can also affect its personality [ 192 ], defined as consistent individual differences in behaviour shown across contexts and situations, and over time [ 193 ]. Personalities are often measured along different axes (e.g., bold/shy [ 194 ]; proactive/reactive [ 195 ]), and are mediated by hormones [ 196 ]. Although personality itself is influenced by intrinsic (e.g., hunger [ 197 ]) and extrinsic (e.g., environmental quality [ 119 ]) developmental factors, personality can further feedback on an individual’s development through its effects on exploration [ 167 ]. For example, avoidant individuals may be less willing to investigate their environment than exploratory individuals, which reduces their chances of being predated, but also reduces foraging rate, which affects growth, as seen in grey treefrog tadpoles Hyla versicolor [ 198 ].

Personality can also impact problem solving [ 40 ]. Exploratory individuals have higher interaction rates with problems, increasing their likelihood of solving innovative tasks. For example, brushtail possums Trichosurus vulpecula that were exploratory, active and vigilant were more likely to solve an escape-box task during the first trial, and were capable of solving a difficult task, compared to less exploratory, less active and less vigilant individuals [ 199 ]. Similarly, exploratory fawn-footed mosaic-tailed rats Melomys cervinipes were faster problem solvers, and solved more problems, than avoidant individuals when tested with food- and escape-motivated tasks [ 200 ]. Exploratory Carib grackles were also faster learners and more likely to innovate in a foraging-extraction task than avoidant individuals [ 201 ]. However, this relationship is not always clearly defined. For example, both bold and shy chacma baboons improved their solving of a food extraction problem after watching a demonstrator [ 170 ]. Similarly, bold meerkats that approached a puzzle box first were not always the first to solve it [ 27 ], and neophobia did not significantly influence problem-solving ability in Barbary macaques Macaca sylvanus presented with puzzle boxes [ 202 ]. Although relationships between personality, behavioural flexibility and problem solving are not clearly defined, such individual variation should be taken into consideration when investigating developmental effects on problem solving.

4. Forgotten Components Limiting Our Understanding of Problem Solving and Its Development

Problem solving has been considered to rely almost exclusively on complex cognitive processes involving insightful thinking (i.e., just knowing what to do, rather than arriving at it through trial and error learning [ 181 , 203 ]), understanding of functionality [ 204 ] or causal understanding (i.e., being able to understand rules and consequences of actions [ 27 ]). Consequently, complex problem solving is often considered to be a consequence of relative brain size (e.g., birds and primates [ 169 ]). However, there is little evidence that problem solving involves complicated cognitive processes [ 28 ]. For example, introduced black rats R. rattus in Australia have caused extensive damage to macadamia Macadamia sp. orchards [ 205 ]. As rodents are evolutionarily constrained to gnaw due to the unrooted nature of their incisors [ 206 ], gnawing is an effective strategy for accessing novel food resources behind barriers or hard seed coats. To solve the problem of accessing the new food, black rats required only persistence, motivation and the appropriate mechanical apparatus rather than complex cognitive abilities. While each animal’s brain consists of a set of information-processing circuits that have evolved by natural selection to solve particular problems in their environment and increase their reproductive fitness [ 207 ], without the appropriate mechanical apparatus, the animal cannot solve the problem [ 208 ]. The ability to solve particular problems may therefore be species-specific, and morphologically constrained, specifically involving mechanical problem solving, unless animals can overcome these mechanical shortcomings (e.g., by developing tool use [ 28 ]).

Although problem solving has been studied in a wide variety of taxa, studies of the development of problem solving specifically have largely been restricted to birds [ 43 ], laboratory rats and mice [ 73 , 82 , 209 ], dogs [ 44 ], and primates that have been housed in captivity [ 131 ]. This is largely due to difficulties associated with observing free-living individuals [ 210 ] and accounting for their previous experience [ 95 ]. Consequently, studies rarely follow problem solving abilities over the development of individuals, instead comparing problem-solving ability between different age cohorts [ 168 ]. Such studies have shed light on the effects of intrinsic factors on the development of problem solving, but fail to consider individual variation in development.

Furthermore, the majority of studies on problem solving concern social species. Both solitary and social species need to problem solve, but the social environment could possibly influence how individuals develop their problem solving abilities. For example, social individuals may use social learning to problem solve, whereas solitary individuals would require persistence and motivation to achieve trial-and-error learning, or would rely on innovation because they are most likely unable to rely on social demonstrators for assistance [ 122 , 170 ], at least after weaning. Current studies therefore provide a limited view of the relevance of social conditions on problem solving development.

Finally, while the influences of environmental quality on problem-solving ability are documented, they are not well understood [ 27 , 40 ]. Animals tend to innovate under harsh conditions in times of necessity [ 24 ], yet good environmental conditions benefit problem solving by promoting neuroendocrine development [ 120 ] and reducing stress [ 118 ]. The effects of the physical or social environment tend to be studied either through manipulation studies during early development, with subsequent tests occurring later on as adults in static environments [ 165 ] or via correlative studies, where individuals from different habitats are compared [ 26 ]. Similarly, studies have investigated the impact of social rank [ 132 ], social isolation [ 211 ], group size [ 121 , 136 ], and group composition [ 2 , 27 ] on problem solving, but the majority of these studies have not explored the underlying developmental processes. To our knowledge, only one longitudinal study has tracked an individual’s problem-solving ability in response to changing physical environments. Cole et al. [ 40 ] found that individual performances in free-living great tits were consistent across time (seasonal variation). How problem-solving ability changes in response to changing social environments, such as when a subordinate changes dominance rank, has rarely been studied.

5. An Individual-Centric Focus can be Beneficial

The ability to solve a problem relies on a combination of genetic and non-genetic factors [ 44 ], physiology [ 97 ], behavioural flexibility [ 95 ], general cognitive ability [ 27 ], personality [ 129 ] and mechanical ability [ 212 ]. In addition, age and experience further influence problem-solving ability. Aging results in natural neuroendocrine system changes [ 213 ], which further affect behaviour and cognition [ 163 ]. However, every individual develops along its own unique developmental trajectory within the phylogenetic constraints of the species, and the relative contribution of these intrinsic and extrinsic factors and their interactions are likely to vary considerably between individuals. Therefore, we cannot assume that individuals from the same environment [ 214 ], or even the same clutch/litter [ 215 ], will behave or respond to the environment in the same way. We only have to look at genetic clones (e.g., identical human twins displaying linguistic differences [ 216 ]) to realise the uniqueness of individual developmental trajectories. This considerable variation argues strongly for focusing on individuals, particularly as they develop, learn and experience new things over their lifetimes in the context of problem solving. Therefore, when investigating problem solving abilities in the future, it may be beneficial to consider individual variation as an important aspect of the data analyses, and not just rejected as statistical ‘white noise’ (see [ 40 , 46 ] for examples). Using this approach may enable future research to identify key predictors, or clusters of common predictors, of problem-solving ability.

6. Conclusions

Individuals experience developmental changes over the course of their lifetimes, which impact their problem-solving abilities. The external environment, including the physical and social environments, can affect the development of problem solving via its impact on underlying genetic, non-genetic and neuroendocrine mechanisms. Problem solving has a heritable component in some species, while complex neuroendocrine processes are also involved in the development of problem solving. However, untangling the influence of these different factors on the development of problem solving is challenging, given their interdependence and complexity. Our understanding of how problem solving develops would benefit from studies of solitary species, to allow for comparisons of general causal mechanisms, since solitary species cannot rely on social learning about problems, at least after weaning. Furthermore, because environments are not static, future studies should consider the effects of changing environmental conditions over the course of an individual’s lifetime on the development of problem solving. Importantly, investigating individual variation in problem-solving ability is necessary for a full understanding of the development of problem solving, which will allow us to assess the relative contributions of different developmental factors on this ability in different individuals.

Acknowledgments

We would like to thank Ben Hirsch and Brad Congdon for providing helpful comments on the manuscript.

Author Contributions

Conceptualization, M.K.R. and T.L.R.; Writing—Original Draft Preparation, M.K.R.; Writing—Review and Editing, T.L.R. and N.P.; Supervision, T.L.R.; Project Administration, M.K.R.; Funding Acquisition, M.K.R. All authors have read and agreed to the published version of the manuscript.

We would like to thank the Australian Government for providing a Research Training Program Scholarship to MKR, and James Cook University for funding this project.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Animal Behavior

City raccoons are sophisticated problem-solvers, study suggests raccoons’ cognitive skills may help them adapt to urban living..

Posted July 31, 2024 | Reviewed by Lybi Ma

• Researchers studied wild urban raccoons using puzzle boxes with either one or multiple solutions.
• Raccoons who engaged with the boxes demonstrated flexibility and individuality in problem-solving.
• The inclusion of an easier solution enabled previous non-solver raccoons to become solvers.

Increasing urbanization has crowded out many wild animals. But raccoons appear to be thriving, living in cities across the country and even expanding their historical range.

To investigate what underlies their ability to adapt, Lauren Stanton and her Ph.D. adviser Sarah Benson-Amram combined a brand-new cognitive test for wild raccoons with modern technology. Their findings suggest that raccoons’ problem-solving skills may help them meet the challenges of urban life.

Studying Cognition in the Wild

Benson-Amram had previously used puzzle boxes, in which an animal must figure out how to open a compartment to retrieve food, to probe the problem-solving abilities of hyenas and other large carnivores. However, a new approach was needed to study the urban raccoons of Laramie, Wyoming.

“These raccoons are nocturnal and cryptic. They were not habituated to us. We weren’t going to be able to watch them live,” says Stanton. “We needed a new way to test them that had not been done before.”

Stanton, Benson-Amram, and colleagues designed a new type of puzzle box that had 24 locked compartments containing food instead of a single compartment. This allowed them to collect data from multiple individuals within the same night.

Before the start of testing, the team captured raccoons with humane traps and marked them with passive integrated transponder (PIT) tags. These were injected between the shoulder blades to facilitate individual recognition using radio frequency identification (RFID) techniques.

The researchers set up the puzzle box and surrounded it with a flexible antenna attached to an RFID reader to detect the approach of PIT-tagged raccoons. They also installed multiple infrared home security cameras around the testing area to record all interactions with the puzzle box.

Problem-Solving Skills

Stanton and colleagues began by setting out a 24-door puzzle box in which every door had the same solution.

“For this initial phase, we presented the raccoons with a new problem and asked if they could use innovation to solve it,” says Stanton. “We found that, yes, about a quarter of the raccoons that we tested learned how to open this single-solution puzzle box.”

Additionally, the team noticed an alternative strategy employed by some raccoons. Rather than solving the puzzle box, they waited nearby for another raccoon to open a door and then tried to scrounge the food reward away from them.

The second phase of the experiment involved another 24-door puzzle box, but this one had four different latch types instead of just one.

The raccoons that had previously learned to open the single-solution box quickly generalized their learning to the new types of solutions, demonstrating flexibility in problem-solving. Further analyses showed that raccoons did not open the same sequence of latches every time; instead, they transitioned among the different latch types in a flexible manner. Yet, each raccoon seemed to use an individually distinct sequence when opening compartments. Stanton thinks that sticking to strategies that differ from one another may help the raccoons mitigate competition .

Stanton and colleagues also noted that some of the non-solving raccoons who had previously scrounged off others switched and became solvers themselves when presented with the multi-solution puzzle box. This may be due to the inclusion of an easier solution on the second box. Once exposed to an easier solution that they were able to solve, the previous scroungers seemed to gain some motivation and knowledge to learn the rest of the solution types, says Stanton.

“It speaks to how learning begets learning,” she says.

Getting to Know the Neighbors

Overall, the raccoons in this study demonstrated flexibility and individuality in their problem-solving. But only about a quarter of wild raccoons engaged with the puzzle boxes at all. A similar study with captive raccoons resulted in 65 percent participation.

Stanton says that wild raccoons may be more likely to perceive the puzzle boxes as risky and avoid them altogether. It’s a sound strategy for a species often regarded as a nuisance and exposed to various forms of lethal and non-lethal management . Indeed, many of the raccoons that did interact with the puzzle boxes in this study were juveniles, who tend to be more exploratory and risk-taking than adults.

In addition to highlighting why studies with wild animals in natural environments are important, the results have implications for understanding and living alongside urban raccoons.

With urbanization and human population growth not slowing down anytime soon, Stanton believes that cities must make room for wild residents.

“There is a growing need to restore and design cities with wildlife in mind,” she says. “A better understanding of the behavior and cognition of urban wildlife can help us coexist well with raccoons and other species with which we share our cities.”

Stanton LA, Cooley-Ackermann C, Davis EC, Fanelli RE, and Benson-Amram S. 2024. Wild raccoons demonstrate flexibility and individuality in innovative problem-solving. Proceedings of the Royal Society B 291: 20240911. Doi: 10.1098/rspb.2024.0911.

Mary Bates, Ph.D. , is a science writer who specializes in neuroscience, animal behavior, psychology, and biology.

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Animal Corner

Discover the many amazing animals that live on our planet.

13 Of The Most Intelligent Animals On Earth

We humans are a pretty unique and amazing bunch. Our ability to co-operate, to use complex language to create masterful works of art and to explore deep, philosophical dilemmas like existence and consciousness really do set up apart from our animal cousins.

It’s very easy to marvel at human achievements, but when you take a pause and dive into the fascinating universe of animals, you don’t have to look too hard to find examples of incredible intelligence. They are all over the place. There are some that create incredible works of art in the sand, those that have learned to use tools, that can learn and use sign language or mimic almost any sound they hear. Can the average human do that?

Let’s take a look at some of the most intelligent animals on Earth, and get an understanding of what we determine as intelligence and how we measure this in the animal kingdom.

13 Of The Most Intelligent Animals in the World

Exploring the intelligence of animals opens up a world where we realize that we are not alone in our cognitive adventures and emotional experiences. That can be quite a comforting thought!

I’m going to start this one off with taking a look at 13 of the most intelligent animals in the world and while these are in no particular order (because different species show intelligence in very different ways), I am starting off with three different primates. These animals are our closest relatives, and the most widely researched.

Chimpanzees , our closest relatives in the animal kingdom, are not just adorable but also incredibly intelligent! They share around 98.8% of their DNA with us humans, and have considerable cognitive abilities and emotional depth. They use tools, learn sign language, and even engage in tactical deception.

Chimps have a complex social structure, in which they have to navigate through various social scenarios, understand relationships, and manage interactions. This demands a high level of social intelligence. They have been observed displaying empathy, consolation, and even mourning, indicating emotional intelligence. They can even plan for future events, showcasing a level of foresight that is rare in the animal kingdom.

They are adept learners and have shown a capacity to imitate behaviours, learn sequences, and even understand symbolic language. There have been multiple individuals that have shown their ability to learn and communicate using sign language with their researchers. A shame that such research is not more widespread.

Orangutans, the gentle giants of the Borneo and Sumatran rainforests, are not just skilled tool users but also brilliant problem solvers! They can manipulate their environment creatively to solve problems and have a remarkable memory, remembering the locations of fruit trees and the timing of when they’ll bear fruit. They also have a great capacity to learn through observation and mimicry, both in the wild and in captivity.

Despite being semi-solitary, orangutans form strong mother-offspring bonds and have social interactions that require understanding social cues and emotions. In captivity, orangutans have shown the ability to understand and respond to the emotions and needs of others, indicating emotional intelligence.

In tests and observations they have shown the capacity to plan for the future, hiding tools for later use and to innovate and overcome difficulties with new behaviours – all examples of complex thought and intelligence.

Gorillas are another primate that exhibits profound emotional intelligence. They share many of the same intelligence traits and behaviours as the other primates mentioned above, but add to that with some very profound social and emotional behaviours.

They have been observed ‘teaching’ their young ones, through behaviour modification to encourage imitation or understanding. They provide comfort, assistance and encouragement to others in their group, as well as understanding and responding to the emotional states of others too. This shows a great capacity for empathy and emotional intelligence with these Gorillas . It also reveals displays of altruism which are further identified by their tendency to selflessly share food and help injured members of their group.

Gorillas can also communicate through a variety of vocalizations and gestures, and can learn sign language, expressing emotions and desires, and thoughts.

Dolphins , especially the Bottlenose dolphins , are by all accounts, aquatic geniuses. With a brain four to five times larger than expected for their body size, they exhibit self-awareness, recognize themselves in mirrors, and comprehend complex commands. Dolphins communicate using a sophisticated system of vocalizations and even have unique ‘signature whistles’ similar to how we use human names!

They form close social groups with family and allies, and co-operative behaviours around hunting and defending the group. They are one of the few animals other than primates that have learned to use makeshift tools to perform a specific function. In this case, they use a type of sponge to protect their rostrums when they are foraging for food on the sea floor. This illustrates great problem solving and innovation.

Dolphins can learn through mimicry not just of their own species, but others too, including humans. They show capacity to actively teach their young, to remember events from far in the past, and to plan for the future. That is quite a set of skills!

Elephants, the gentle giants, are not only recognized for their impressive memory but also for their deep emotional and social connections and matriarchal families. They are one of the few species that mourn their dead so deeply. They also display deep family and social connections with great capacity for empathy. It’s not all serious and emotional with Elephants though, they engage in lots of playful activities, both baby elephants and elders alike.

They are another animal that have learned to use tools, in this case they use sticks and branches to scratch aggravating itches, and swing the leaves of these branches to swat unwelcome insects. Elephants have shown the capacity to understand human gestures, and even differentiate between languages and human voices. They are long lived animals and their memory is incredible, able to remember places, people and interactions from many years in their past.

Parrots , particularly the African Grey Parrot, are celebrated for their vocal abilities and cognitive prowess. Their greatest trait, and perhaps the most widely researched, is their incredible ability to mimic sounds. They can do this better, arguably, than any other bird. While there are many others that can mimic through simple repetition, the Parrots mimicry extends beyond this. Not only can they mimic human voices, but they also show the potential to understand and use human language contextually, and that is incredible amongst it’s avian peers.

Parrots also show the ability to solve complex problems. African Grey Parrots for example, can associate words with meanings, understand numerical concepts, and even express emotions through vocalizations! They are one of the few species of bird that have learned to use tools, particularly for the use of getting to hard-to-reach food. These birds show the capacity to understand causality and probability, especially when given the challenge of completing complex puzzles.

Corvids – Ravens & Crows

Crows and ravens , the dark-feathered intellectuals, are similar to parrots in that they exhibit remarkable problem-solving skills and the ability to use tools. They have been observed making hooks out of small pieces of wood or metal, for the purpose of getting to food. They have also been observed placing nuts in the road and waiting for traffic to run over them to crack the shell, then swooping down to get their food. This shows that they understand to some extent, cause and effect.

These birds which are both of the genus ‘ Corvus ‘ can recognize themselves in mirrors, and even plan for the future – a trait once thought to be uniquely reserved for primates!

Pigs, with their curious snouts and intelligent eyes, are both smart and emotionally rich. Research suggests they have a cognitive ability comparable to primates and the most intelligent dogs. They can manipulate objects to achieve a function or goal, they are one of the few animals that understand mirrors and are one of the few animals that have shown an ability to even play video games!

This might sound like a budget sci-fi film, but it’s true. Researchers from Purdue University in the USA ran experiments where pigs were able to move a cursor on a screen by interacting with a joystick with the aim of achieving a reward.

Pigs also have complex social structures, can learn from observation, and exhibit a range of emotions and preferences. They have shown a capacity for empathy and grief, and have shown behaviours that suggest an awareness of their own body and the impact of their actions.

Octopuses, the eight-armed wonders of the ocean, are masters of problem-solving and camouflage. They might look like little aliens, but they show a remarkable range of intelligent behaviours and emotional capacity. They are very inquisitive, though perhaps shy at first, and in captivity show an uncanny ability to recognize different humans. Some species demonstrate the ability to navigate competently through mazes and believe it or not, even unscrew the lids from jars.

These animals have a decentralized nervous system and can perform different tasks independently with each of their eight arms. This is a unique form of intelligence from any of the other species in this list. Octopus have an incredible ability to mimic the appearance of their surroundings , changing colour and blending into their rocky or coral backgrounds. Some can even mimic the appearance of other marine animals!

Rats, often underappreciated, are both clever and resourceful. They have been researched extensively, and the subject of many intelligence tests and experiments. In studies, they have been shown to be able to find shortcuts and loopholes in experiments, and have a great capacity to learn from their environment. Their spatial memory and cognitive mapping are exceptional.

They live in structured social groups and take part in social activities such as play and mutual grooming. Rats, especially young rats , are even known to play hide and seek – though they probably have a different name for it! In some observations, rats also demonstrate a capacity for metacognition, which is the ability to think about their own thinking. They can make decisions based on the certainty of their knowledge, how incredible is that!

Pigeons are not quite as smart as Parrots or Corvids, but they are up there, and have some pretty unique skills and intelligence traits. First and foremost are their incredible homing and navigation abilities which are believed to come from superior spatial memory and their capacity to remember landmarks in relation to location.

So trusted is a Pigeon’s navigation that they have been used across time by humans to deliver messages over long distances, particularly during times of war. They were even used during the World Wars for this exact purpose. While often persecuted, these birds have been a great aid to humans in our times of need.

In other examples of intelligence, Pigeons have shown a capacity to learn from and adapt to their environment. In studies they show a level of self awareness, able to recognise themselves in video footage. They perform well in reward challenges and can be trained to perform sequences of actions. Other observations reveal their capacity to differentiate between different visual stimuli, and even categorize objects, showcasing a level of abstraction in thinking. Pretty smart for an animal some of us consider to be vermin.

No intelligent animals list would be complete without mentioning mans best friend. While all dogs are smart, some are smarter than others and the Border Collie is one of the smartest around, always scoring high in tests of intelligence.

With their alert eyes and boundless energy, they are not only agile herders but also incredibly smart in many other ways. They can understand numerous words, commands, and gestures, differentiate between objects, and even exhibit problem-solving skills, making them one of the smartest dog breeds . They have great emotional intelligence and have a clear understanding of hierarchies. Their memories are also very deep and rooted into their personalities.

How Do We Research Animal Intelligence?

Exploring the depths of animal intelligence involves a blend of behavioural observations, experimental setups, and sometimes, forming unexpected friendships with our animal subjects. Scientists employ various tests (more on this later), to observe and gain insight from the cognitive world of animals. From navigating through mazes to understanding reflections in mirrors, animals are put through a series of challenges to identify the level of their intellect and emotional capacities.

What Qualifies For Intelligence?

Intelligence in the animal kingdom is identified across a blend of problem-solving, emotional understanding, social interactions, learning abilities, memory, and adaptability. It’s a measure of how animals interact with their environment, their peers, and other species, navigating through the challenges of survival and social living.

Determining How Smart Different Animals Are

Determining the intelligence of different animals is a journey that navigates through various aspects of cognitive and emotional capacities. Scientists and researchers utilize a multifaceted approach that tests subjects in the following ways:

• Problem-Solving Abilities : How well can an animal navigate through challenges and use innovative solutions?
• Learning and Adaptation : How quickly and efficiently can an animal learn new skills and adapt to changes in its environment?
• Memory : How well can an animal recall information and experiences?
• Social Intelligence : How does an animal interact, communicate, and establish relationships within its social structure?
• Emotional Intelligence : Can the animal understand and respond to emotional cues, both of its own species and others?
• Use of Tools : Can the animal figure out how to use tools to complete tasks and navigate through challenges?
• Self-awareness: Can the animal recognize itself and have a sense of its own body?

Through a combination of observational studies, experimental setups, and longitudinal research, scientists explore these facets to gauge the intelligence of various animals.

The Different Intelligence Tests Used To Determine Animal Intelligence

So we now know what it is that scientists are measuring to determine intelligence in animals, but what tests do they use? Here are some examples for the different areas of observation:

• Problem-Solving Tests

Puzzle Boxes : Animals are presented with boxes that contain food but are locked or obstructed in some way. Their ability to unlock or access the food is observed.

• Learning and Memory Tests

Maze Navigation : Animals are placed in mazes and their ability to find their way out, and remember the path in subsequent trials is tested.

Discrimination Tasks : Animals are trained to differentiate between different stimuli and are tested on their recall and application of learned knowledge.

• Social and Emotional Intelligence Tests

Mirror Test : Animals are exposed to mirrors to see if they can recognize themselves, indicating self-awareness.

Empathy Tests : Observing behaviours that indicate understanding and responding to the emotional states of conspecifics.

• Communication and Language Tests

Symbol Recognition : Animals are trained to associate symbols with objects or actions and are tested on their ability to understand and use these symbols.

Vocal Mimicry : For species that are capable, their ability to mimic and understand vocal sounds is explored.

• Tool Use and Manipulation Tests

Tool Utilization : Observing if and how animals use tools to achieve goals, such as obtaining food or providing protection.

Object Manipulation: Testing how animals interact with various objects and if they can use them innovatively to solve problems.

• Creativity and Innovation Tests

Novel Object Interaction : Introducing animals to new objects and observing their interactions and innovative uses.

Creative Problem Solving : Presenting animals with challenges that require creative thinking and observing their approaches.

• Cooperation and Altruism Tests – Observation

Cooperative Tasks: Observing if animals can work together to achieve a common goal that benefits all participants. Those that have complex social structures often do well here.

Altruistic Behaviors: Observing instances where animals help or provide for others without immediate personal gain. Some primates show a great capacity for altruism for example.

Some animals will perform better in one test than they do in the other, depending not only on the species but on the individual too.

5 Fun Intelligent Animals Facts For Kids

• Elephants can paint with their trunks and create beautiful artworks!
• Rats love to play and can learn to play a fun game of hide and seek with humans!
• Octopuses have three hearts, but did you know two of them actually stop beating when they swim?
• Pigs have such a great sense of direction that they can find their way home from huge distances!
• Pigeons were used as mail carriers and could deliver messages across long distances, even during wars!

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• Published: 03 August 2020

Innovative problem-solving in wild hyenas is reliable across time and contexts

• Lily Johnson-Ulrich 1 , 2 ,
• Kay E. Holekamp 1 , 2 &
• David Z. Hambrick 3  

Scientific Reports volume  10 , Article number:  13000 ( 2020 ) Cite this article

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• Animal behaviour
• Behavioural ecology

Individual differences in behavior are the raw material upon which natural selection acts, but despite increasing recognition of the value of considering individual differences in the behavior of wild animals to test evolutionary hypotheses, this approach has only recently become popular for testing cognitive abilities. In order for the intraspecific approach with wild animals to be useful for testing evolutionary hypotheses about cognition, researchers must provide evidence that measures of cognitive ability obtained from wild subjects reflect stable, general traits. Here, we used a multi-access box paradigm to investigate the intra-individual reliability of innovative problem-solving ability across time and contexts in wild spotted hyenas ( Crocuta crocuta ). We also asked whether estimates of reliability were affected by factors such as age-sex class, the length of the interval between tests, or the number of times subjects were tested. We found significant contextual and temporal reliability for problem-solving. However, problem-solving was not reliable for adult subjects, when trials were separated by more than 17 days, or when fewer than seven trials were conducted per subject. In general, the estimates of reliability for problem-solving were comparable to estimates from the literature for other animal behaviors, which suggests that problem-solving is a stable, general trait in wild spotted hyenas.

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Introduction.

The questions of how and why cognition evolves across the animal kingdom remain unresolved despite more than a century of intensive research. The most common approach to addressing these questions has been to compare average levels of cognitive performance among species 1 , 2 , 3 , 4 , 5 . In this interspecific approach, individual differences within species are treated as random error (or “noise”). Recently, there has been growing recognition of the value of using individual differences to test evolutionary hypotheses—the intraspecific approach 6 . Intraspecific studies of free-ranging populations are especially valuable for understanding cognitive evolution, because individual variation is the raw material on which natural selection acts. This approach allows researchers to examine the causes of cognitive variation in an ecologically valid context and also to examine the fitness consequences of this variation 7 , 8 , 9 . Despite this recognition, in the field of cognitive ecology there have been few attempts to empirically test the hypothesis that measures of cognition reflect stable, general traits, meaning traits expected to influence performance across time and across a wide range of situations 10 , 11 .

The hypothesis that a cognitive measure reflects a stable, general trait predicts that the measure should have a high degree of reliability: an animal that receives a high score on the measure at one point in time and in one context should receive a high score at later points in times and in other contexts, and the performance of animals receiving low scores on the measure should be similarly consistent . As a psychometric concept, reliability refers to the amount of error contained in a measure, as reflected in the stability of the measure across contexts and time. Although reliability is synonymous with the term ‘repeatability’, which refers to consistent individual differences in the behavior of non-human animals 12 , we use reliability because it is a well-defined psychometric term used in diverse literatures on individual differences in psychological traits, including cognitive abilities in both humans and non-human animals. It is especially important to demonstrate reliability of measures reflecting animal cognition in the wild, because there are many potential sources of error, including both external factors (e.g., weather, presence of conspecifics) and internal factors (e.g., hunger, stress) 9 , 13 , 14 , 15 .

In this study, we assessed the intra-individual reliability of innovativeness across time and context in wild spotted hyenas using a problem-solving paradigm. Defined as the ability to solve a novel problem or use a novel behavior to solve a familiar problem, innovativeness is among the most commonly measured cognitive abilities in non-human animals 16 . Although there has been a great deal of interest in the relationship between innovativeness and variables such as brain size, ability to invade new habitat, and life history traits in a diverse array of taxa, formal attempts to evaluate the reliability of problem-solving paradigms used to measure innovativeness remain very rare. In a meta-analysis, Cauchoix et al. 11 identified only six publications reporting reliability for any measure of cognitive performance,of these only two measured innovative problem-solving in wild subjects, and both were in birds. Thus, there is a pressing need to examine the reliability of innovative problem-solving in other wild animals, especially in wild mammals. Furthermore, most studies only measure cognition at two time points and across two to four different tasks 17 , 18 , 19 , 20 , and there has been very little research examining how reliability might vary based on the number of measures or the length of the interval between measures, nor how reliability might vary within species among different age-sex classes (e.g. Ref. 13 . Our ignorance here is due, in part, to the numerous logistical challenges of experimentally measuring innovativeness repeated times in the same subjects—a problem that is particularly pronounced in wild subjects where locating and enticing individuals to perform cognitive tests even once can be difficult, and tracking individuals for repeated testing may be impossible in many species. However, for the intraspecific approach in wild animals to be useful for testing evolutionary hypotheses, researchers must provide evidence that measures of innovative problem-solving reflect stable traits, and that estimates of reliability are robust against numerous sources of variation in testing environment and methodology.

The spotted hyena is well-established as a model organism for testing hypotheses about the evolution of cognition 21 and innovativeness has previously been measured in both captive and wild hyenas 22 , 23 , 24 , but the problem-solving paradigms used to measure innovativeness were never previously tested for reliability except by Johnson-Ulrich et al. 25 . Here we measured reliability (R) by calculating intraclass correlation coefficients (ICC), which are commonly used in behavioral ecology to assess the reliability of behavioral traits within individuals 26 . An ICC estimates the amount of variation in the response variable explained by random effects or grouping factors in mixed hierarchical models. Ultimately, we found significant reliability for problem-solving performance in wild spotted hyenas and demonstrate how estimates of reliability vary across tasks, trials, age-sex classes, the temporal interval between observations, and the total number of observations.

Seventy-two hyenas participated in 694 test trials with a multi-access box (MAB). MABs are problem-solving paradigms used to measure innovativeness. The MAB used in the present study was a metal box with one of four different doors on each vertical face (Fig.  1 ). Each door required a unique motor behavior to open, but all four doors opened to the same common interior from which a hyena could retrieve bait. Hyenas were given repeated trials, and after a hyena opened the same door on three of four consecutive test trials, that door was blocked, forcing the hyena to open a different door to retrieve the bait (Fig.  2 ). Testing was thus divided into four ‘phases’ in which hyenas were required to use four different doors to open the MAB (Supplementary Fig. S1 ). Overall, our sample included an adequate representation of each age and sex class with 17 adult females, seven adult males, 13 subadult females, 17 subadult males, 10 female cubs, and nine male cubs. Out of these 72 hyenas, 23 opened the MAB at least once (mean = 2.74 doors, SD = 1.39) and 11 opened each of the four doors to the MAB at least once across their trials. Because we collected data with more hyenas that never opened the MAB (N = 49 hyenas, n = 376 trials) than data with hyenas who opened the MAB at least once (N = 23 hyenas, n = 318 trials), and because including the former hyenas would lead to zero-inflation and an inflated reliability estimate, we excluded data from hyenas that never opened the MAB from our analyses. Instead, we only assessed the reliability of problem-solving for hyenas that opened the MAB at least once. Among these 23 hyenas our sample included five adult females, two adult males, five subadult females, six subadult males, one female cub, and four male cubs.

Reproduced with permission from Johnson-Ulrich et al. 23 .

The MAB used in the current study. (1) The push flap solution; (2) the sliding door solution; (3) the pull flap solution; and (4) the drawer solution. Small filled grey circles indicate the approximate number and location of holes drilled through the wall of the MAB. Large gray circle on side 3 represents the location of the doorknob. Small rectangles represent the location of door hinges.

The number of hyenas using each solution across their successful trials within Phase 1 of testing. Twenty-three hyenas were successful on at least one trial, but within these twenty-three hyenas the number of successful trials they had within Phase 1 varied. DK door knob, DR drawer, P push door, S sliding door.

Contextual reliability of problem-solving ability across different doors

Contextual reliability is typically assessed by comparing performance across different tasks that measure the same cognitive ability 11 , 27 . Because each of the four doors to the MAB required a different motor behavior, we first investigated the contextual reliability of problem-solving performance with a model examining the likelihood of solving each of the four different MAB doors (door knob, slot, push, drawer) at least once. Because innovation is defined as using a novel behavior to solve a problem 16 , hyenas only ‘innovated’ when they solved each door of the MAB for the very first time. Thus, our estimate of reliability for problem-solving across doors provides a valuable indication of the reliability of ‘innovativeness’, in the strictest sense, among hyenas. In this model our response variable was a binary variable indicating whether or not a hyena had solved each of the four doors to the MAB at least once (Table 1 : Model 1). Each hyena received four dichotomous scores for each of the four unique MAB doors, with a score of one indicating that they solved a door at least once and a score of zero indicating that they never solved that particular door despite contacting the MAB on multiple trials. We included age class as a fixed effect in this model (see “ Statistical analysis ”); hyenas in the cub (GLMM: Odds ratio = 0.05, P = 0.058) and subadult (GLMM: Odds ratio = 0.07; P = 0.055) age classes were less likely than adults to solve each door. Reliability was determined by calculating adjusted ICCs with the R package rptR 28 . Adjusted ICC values are calculated as a ratio of the random effects variance to the combined random effects and residual variance. Of the 23 hyenas that opened the MAB at least once, we found that problem-solving performance across doors was moderately but significantly reliable (Likelihood ratio test: R = 0.40, P = 0.001; Table 1 : Model 1). Thus, problem-solving ability was significantly reliable when assessed in the variable contexts of the MAB’s four doors, each of which required a unique solution.

Temporal reliability of problem-solving across trials

In addition to contextual reliability, the temporal reliability of cognitive traits is commonly assessed by comparing performance across repeated trials with the same cognitive test (e.g. problem-solving: Refs. 11 , 17 , 19 , 28 . Because problem-solving performance was moderately reliable across different doors, we next examined the reliability of problem-solving performance across each subject’s trials, regardless of the specific doors used, in order to investigate temporal reliability. We gave each hyena multiple trials with the MAB in order to give subjects the opportunity to solve its different doors and examine performance across different phases of testing. Although hyenas are not strictly ‘innovating’ when they open a MAB door that they’ve previously solved, most studies on innovative problem-solving conduct repeated trials to compare the acquisition of innovations across individuals or assess their spread through populations (e.g. Refs. 21 , 28 , 29 , 30 , 31 , 32 , so investigating the reliability of problem-solving performance across trials is relevant for future research.

In this model, and all subsequent models, our response variable was a binary variable indicating whether a hyena opened or failed to open a door of the MAB, irrespective of which specific door it was working on. On average hyenas were successful in 54.5% of trials (SD = 27.0%, N = 23 hyenas, n = 318 trials). Because temporal reliability may be influenced by learning and experience 11 we included a fixed effect of trial number in order to control for the number of previous trials in which each hyena participated. We also included age class and phase number as fixed effects (see “ Statistical analysis ”). We found that cubs (GLMM: Odds ratio = 0.31, P = 0.096) and subadults (GLMM: Odds ratio = 0.32, P = 0.046) were both less likely than adults to have successful trials with the MAB. Hyenas were also less likely to solve the MAB at later than earlier phases of testing (GLMM: Odds ratio = 0.54, P = 0.044), which probably represents the increasing difficulty across phases. Trial number had a significant positive effect on the odds of a trial being successful (GLMM: Odds ratio = 1.11, P = 0.050), which suggests that prior experience or learning with the MAB was important, but the effect of trial number on the odds of success was relatively small compared to the effects of age class and phase. Furthermore, these fixed effects only explained half as much variation in the response variable (Var F  = 0.08; Table 1 : Model 2) as that explained by hyena ID (Var R  = 0.16; Table 1 : Model 2). Among the 23 hyenas that opened the MAB at least once, problem-solving performance was significantly reliable (Likelihood ratio test: R = 0.18, P < 0.001; Table 1 : Model 2). This result suggests that hyenas’ problem-solving performance was generally consistent across trials, even after controlling for the number of previous trials, the phase of testing, and the hyena’s age class.

Reliability of innovative problem-solving within different age-sex classes

Next, we inquired whether temporal reliability varied among individuals in different age-sex classes. For example, some evidence suggests that female animals exhibit more reliable behavior than males 12 . Furthermore, it seems reasonable to expect that juveniles, which are still developing, might exhibit behavior that is less reliable than that of adults in addition to showing slightly worse performance with the MAB than adults. To compare reliability within different age and sex classes we partitioned our dataset into five different categories: females, males, adults, subadults, and cubs in order to create five different models examining reliability for each age-sex class independently. We included age class as a fixed effect in the female-only and male-only models, and we also included both trial number and phase of testing as fixed effects in all models (see “ Statistical analysis ”). We did not include sex as a fixed effect in each age class model because problem-solving did not vary with sex (Supplementary Tables S2 , S3 ). We found that most age and sex classes showed moderate levels of reliability (Likelihood ratio test: R = 0.21–0.33, P < 0.001; Table 1 : Models 3.1–3.5; Fig.  3 ) with the exception of adult hyenas, for which the reliability of problem-solving was not significantly different than zero (Likelihood ratio test: R = 0.07, P = 0.11; Table 1 : Model 3.3).

Reliability of problem-solving success within different age and sex classes. R values are calculated as adjusted repeatability ratios where the variance explained by fixed effects is not included in the denominator. Error bars show standard error. Standard error was estimated using parametric bootstrapping (N = 1,000) from the R package rptR. N indicates the number of subjects included in each model.

Reliability of innovative problem-solving across different timespans

Although most test trials within subjects (49.37%) were conducted less than 1 day apart, the average number of days between trials was 19.41 ± 56.00 days (median = 0, range = 0–301 days). We were interested in whether temporal reliability between any given trial and the trial that followed it was affected by the amount of time between trials. To do this, we created a dataset where we paired each subject’s trial with the trial that followed it and calculated the number of days elapsing between the two trials. We next partitioned this dataset into trials that occurred less than one day apart, one to three days apart, four to sixteen days apart, and more than 17 days apart. The number of bins and the date range included in each bin were chosen to distribute the number of trials across each date range as equally as possible. We then calculated reliability between pairs of trials for each of these datasets (Table 1 : Models 4.1–4.4; Fig.  4 ). We included age class, phase of testing, and trial number in these models (see “ Statistical analysis ”). We found that reliability was extremely high for trials collected on the same day (Likelihood ratio test: R = 0.93, P < 0.001; Table 1 : Model 4.1), but reliability became non-significant when trials were separated by 17 or more days (Likelihood ratio test: R = 0.10, P = 0.235; Table 1 : Model 4.4).

Reliability of problem-solving success across different timespans. R values are calculated as adjusted repeatability ratios where the variance explained by fixed effects is not included in the denominator. Error bars show standard error. Standard error was estimated using parametric bootstrapping (N = 1,000) from the R package rptR. N indicates the number of subjects included in each model.

Reliability of innovative problem-solving across different numbers of trials

Finally, we were interested in how the varying number of trials collected per hyena might affect estimates of temporal reliability. On average, hyenas received 13.8 ± 7.3 trials (median = 15 trials, range = 2–26 trials). Although we found modest levels of temporal reliability when we included every trial in Model 2 (Table 1 ), we were interested in how our estimates might have changed if we’d only sampled hyenas a set number of times. Collecting a larger number of trials per hyena could, in theory, increase the accuracy of estimates about their problem-solving ability and therefore increase reliability; however, increasing the number of trials can also strengthen learning and memory, which may ultimately reduce estimates of reliability if all individuals eventually converge at a high level of performance 11 . On the other hand, because we were testing hyenas in the wild, larger number of trials were also more likely to take place across different testing sessions, different timespans, and under variable environmental conditions which could, in theory, decrease estimates of reliability due to increased variability with increasing numbers of trials. To estimate reliability for varying numbers of trials, we calculated reliability for hyenas in nine models where we included only their first two to ten trials. We found that estimates for the reliability of problem-solving performance were not significantly greater than zero until we had sampled each hyena seven times (Likelihood ratio test: R = 0.13, P = 0.026, Table 1 : Model 5.6; Fig.  5 ). With seven or more trials estimates of reliability were modest, but nonetheless significantly greater than zero (R = 0.13–0.20; Table 1 : Models 5.6–5.9; Fig.  5 ).

Reliability of problem-solving success across different numbers of trials. R values are calculated as adjusted repeatability ratios where the variance explained by fixed effects is not included in the denominator. Error bars show standard error. Standard error was estimated using parametric bootstrapping (N = 1,000) from the R package rptR. N indicates the number of subjects included in each model.

Overall, our results suggest that innovative problem-solving ability is a stable, general trait in wild spotted hyenas. Our estimates for the reliability of problem-solving performance are comparable to the average reliability of other behaviors in wild animals 12 , and also to the average reliability of other cognitive measures in both captive and wild animals 11 . However, building on previous findings, we further present evidence that, with a few important exceptions, problem-solving performance is reliable across context, time, age-sex class, the interval between observations, and the number of observations.

We found moderate levels of reliability for problem-solving performance across the four different MAB doors. These doors represent four different motor tasks, each designed to measure innovativeness, and we found that hyenas who innovated with one door to the MAB were moderately likely to innovate with the other three doors to the MAB (Table 1 : Model 1). This result is similar to studies in wild and captive birds that have generally found consistent performance among problem-solving tasks requiring different motor actions 18 , 19 , 35 , 36 .

Next, we also evaluated the temporal reliability of problem-solving performance across all trials, irrespective of the specific door used to open the MAB. We found a modest, but significant, level of reliability for problem-solving performance across trials (Table 1 : Model 2). Because trials were conducted across a wide variety of socio-ecological conditions we were impressed to find hyenas demonstrate even this level of consistency in performance. Trial number did have a significant effect in this model, which suggests that learning may have played a role in shaping consistency across trials (Fig.  6 ); however, the amount of variation explained by subject ID in these models was twice that explained by the fixed effects, which included trial number. This result is also consistent with other cognitive studies; a meta-review of the reliability of cognitive abilities similarly found that repetition number usually had an important effect on cognitive performance 11 . However, this meta-review also found that adjusting estimates of R for repetition number usually did not increase R, so the authors concluded that repetition numbers largely had negligible effects on estimates of temporal reliability. Likewise, in most of our models our adjusted R values were only modestly larger than the total amount of variation explained by the random effects. While most studies of problem-solving performance do provide evidence that subjects improve their performance over trials, this improvement is typically gradual, which suggests that subjects do not perfectly remember the motor behaviors used to innovate during their first trial 14 , 22 , 24 , 30 , 34 . Instead, the literature suggests that behaviors such as motor diversity or flexibility may be key for successful problem-solving and that these behaviors, even though they might interact with memory, are independent from learning 34 , 37 , 38 , 39 . Ultimately, a great deal of variation in problem-solving performance was left unexplained by our models, an unsurprising result given that our subjects were wild, free-ranging hyenas tested in an uncontrolled environment. Future research investigating this remaining variation may shed light on the various individual behaviors or socio-ecological conditions that favor successful problem-solving.

The predicted probabilities of a successful trial with the MAB as a product of trial number from a binomial GLMM (Table 1 : Model 2). Error bars indicate standard error. ( a ) Shows the log odds of successfully opening the MAB as a function of test trial number. ( b ) Shows the predicted probabilities of a successful trial with the MAB as a product of trial number for hyenas with at least 10 trials.

Next, we examined the reliability of problem-solving performance within different age-sex classes. Both female and male hyenas showed similar levels of reliability for problem-solving performance (Table 1 : Models 3.2–3.3) with a slight trend towards higher reliability in females. These results are similar to results for behavior across animals more generally; a meta-review of the reliability of animal behavior found that females tend to show slightly more reliable behavior than males when mate-preference behavior is excluded 12 . When we compared the reliability of problem-solving performance across hyena age classes, we found significant reliability for problem-solving performance in subadults and cubs, but problem-solving performance was not significantly reliable for adults. This result is the opposite of what we’d expected, especially because subadults and cubs were significantly less likely to solve the MAB. A meta-review of the reliability of animal behavior found that adults and juveniles tend to show similar levels of reliability across behaviors 12 . In wild hyenas, it may be that adults must contend with a wider variety of distractions than non-reproductively active individuals that are still largely reliant on maternal support for survival 40 . However, it may also be that higher reliability among cubs and subadults compared to adults is directly related to their poorer performance with the MAB compared to adults. Cubs and subadults were successful on 45.8 ± 32.3% and 47.4 ± 27.6% of trials respectively whereas adults were successful on 72.1% ± 13.7% of trials. In adults, lower reliability here could be a result of a ceiling effect where the relatively high success rate and lower variability across trials in adults reduces the amount of variation explained by individual differences.

In general, estimates of reliability are higher for behavioral observations that are made closer together in time 12 . Here, we found remarkably high reliability for problem-solving performance within pairs of trials separated by less than a day (Table 1 : Model 4.1). We also found low to moderate reliability for trials separated by as much as 16 days (Table 1 : Model 4.2–4.3). Only when trials were separated by more than 17 days did we find no significant reliability within pairs of trials (Table 1 : Model 4.4). The lack of reliability among pairs of trials separated by 17 days or more could reflect a limitation of hyenas’ long-term memory, but research with wild spotted hyenas suggests that they are able to efficiently open a previously solved puzzle box even after delays of over a year (unpublished data). In addition to memory, both internal and external environmental conditions (e.g. hunger, social environment) are also much more likely to vary across larger than shorter time spans. That hyenas still show some consistency even with as much as two weeks separating trials is important because it can be extremely difficult to consistently locate subjects for repeated testing, especially in animals like spotted hyenas that live in fission–fusion societies occupying large territories.

In a meta-review of reliability in earlier animal behavior research, reliability estimates were generally not affected by the number of observations per individual 12 . Here, we found low to no reliability for problem-solving performance when fewer than seven trials were conducted per individual. Part of this result may be due to sample size, with just 23 hyenas that solved the MAB at least once, we were only able to include 46 trials in Model 5.1 (Table 1 ). However, part of this may also reflect high intra-individual variability in problem-solving performance for subjects in their first several trials. Although most hyenas opened the MAB on their first trial (median = 1 trial, mean ± SD = 1.96 ± 1.26 trials), the highest trial number in which any of these subjects opened the MAB for the very first time was the fourth trial. No subjects ever solved the MAB after four consecutive failures, despite having subsequent opportunities to do so. For this reason we used a conservative criterion of at least five consecutive failures to classify hyenas as non-innovative (N = 49 hyenas, n = 376 trials), though their trials were not included in our models examining reliability. The lack of reliability across our subjects’ early trials differs from the results obtained from a meta-review of reliability of animal behavior generally, and probably reflects the difficulty of getting accurate measures of animal cognition, especially in wild subjects, where many other internal and external factors may affect the way a subject interacts with a test apparatus, independent of its actual cognitive abilities. Our results suggest that, if researchers are testing problem-solving in wild subjects, they should aim to collect many trials per subject to ensure accurate estimates of their problem-solving ability, and aim to identify a minimum number of trials per subject for inclusion in analyses. In hyenas, it appears that 5–7 trials per subject may be required to observe consistent individual differences in their problem-solving ability. In total, we deployed the MAB an average of 88.5 ± 34.72 (N = 4 clans) times in each of four study groups in order to identify initial successful trials for all 23 innovative hyenas (see “ Test protocol ”).

Our study offers an important demonstration of the reliability of innovative problem-solving in a wild mammal. However, reliability does not necessarily correlate with validity. Previous research has debated the conceptual validity of problem-solving paradigms for measuring innovativeness 14 , 37 , 41 , 42 . Although this debate is not entirely settled, researchers have found that the behaviors leading to spontaneous innovations in the wild are very similar to the behaviors that underlie experimentally-observed innovations using problem-solving paradigms 37 , which strongly suggests that problem-solving paradigms are valid for measuring innovativeness. However, it is also important to consider the ecological validity of a paradigm and tasks should be designed with a species’ underlying capabilities in mind. We designed a multi-access box that required spotted hyenas to use behaviors that are part of their natural foraging repertoire to solve a novel problem for a food reward. This kind of puzzle box is sometimes called a novel extractive foraging puzzle because it requires subjects to extract food from a container. Spotted hyenas are dietary generalists and mammalian bones, which represent an important part of their diets, require a moderate degree of extractive foraging to access the marrow within. Therefore, it is not surprising that spotted hyenas were able to innovate with this kind of problem-solving paradigm. However, for animals that never use extractive foraging in the wild, problem-solving paradigms like the one used in the current study might not be ecologically valid tools for assessing innovativeness.

In conclusion, it appears that, even with the many challenges posed by testing animals in the wild, we were nevertheless able to reliably measure innovative problem-solving ability in hyenas. Overall, our results on reliability complement the literature on the validity of innovative problem-solving paradigms, and we conclude that innovative problem-solving paradigms are reliable tools for measuring individual variation in cognitive performance.

Study site and subjects

We tested innovativeness in four neighboring spotted hyena clans within the Maasai Mara National Reserve, Kenya between June 2016 and November 2017. These clans ranged in size from 30 to 55 adult hyenas. Spotted hyena clans represent distinct social groups that are made up of multiple unrelated females, their offspring, and adult immigrant males. Clans are structured by strict linear dominance hierarchies, with an alpha female and her offspring at the top, followed by lower-ranking females and their offspring, with adult immigrant males occupying the lowest rank positions. Births occur year-round and unrelated females raise their offspring together at a communal den. Female hyenas stay in their natal clan throughout their lives, whereas male hyena usually disperse to join new clans when they are 24–60 months old, after they reach sexual maturity 43 , 44 .

All subjects were identified by their unique spot patterns and ear damage. Hyenas of all age classes and both sexes were included in the study. All subjects were sexed within the first few months of life based on genital morphology 40 . Age classes were based on life history stage 45 . Cubs were defined as hyenas that were still dependent on the communal den for shelter; on average, Mara cubs become den-independent around 9–12 months of age 45 . Subadults were hyenas who were den-independent but had not yet reached sexual maturity. Adults were hyenas that had reached sexual maturity. In females, sexual maturity was determined by the observation of mating, visual evidence of first parturition, or the female reaching three years of age, whichever came first 46 . In males, sexual maturity was determined by dispersal status, males who were still present in their natal clan at testing were classified as subadults and immigrant males were classified as adults.

Multi-access box paradigm for measuring repeated innovation

We tested innovativeness in wild spotted hyenas using a multi-access box designed for use with mammalian carnivores 24 . The multi-access box (hereafter, ‘the MAB’) is a problem-solving paradigm, also known as an artificial or novel extractive foraging task, where subjects must solve a novel problem to obtain a food reward. In contrast to traditional problem-solving tasks, MAB paradigms typically offer multiple solutions to the same puzzle, each requiring its own unique behavior pattern. As a condensed battery of tasks, the MAB paradigm allows researchers to measure innovation, not just once, but multiple times across different solutions 47 . We chose to use a MAB paradigm because it allowed us to compare reliability across repeated trials within the same solution to reliability across different solutions. Reliable success with the same solution across trials may be a result of individual learning rather than a result of a stable cognitive trait. However, if individuals reliably innovate by opening multiple unique solutions to the MAB this would suggest that innovativeness is a stable cognitive trait. The MAB in the current study was a steel box, measuring 40.64 × 40.64 × 40.64 cm (length × width × height), with four unique doors, each requiring a different motor behavior, that could be used to access a common interior baited with a food reward (Fig.  1 . We used this MAB previously to test repeated innovation in captive hyenas; for more information about the design specifications see Johnson-Ulrich et al. 24 .

Test protocol

We conducted all testing between 0630 to 1000 h and 1700 to 1830 h, the daylight hours during which hyenas are most active. We deployed the MAB anytime a suitable group of hyenas was located within the territories of our study clans. A suitable group was defined as one containing five or fewer hyenas within 100 m or within visible range that were either walking or resting but not engaged in hunting, border patrol, mating, courtship, or nursing behaviors. We used our research vehicle as a mobile blind to shield the researchers from the view of hyenas while we baited and deployed the MAB on the opposite side of the vehicle from hyenas. We baited the MAB with approximately 200 g of either goat or beef muscle, skin, or organ meat. During familiarization trials we also used full cream milk powder in addition to, or in place of, meat. We deployed the MAB approximately 20 m away from the nearest hyena and after MAB deployment we drove the research vehicle to a distance of 20–50 m away from the MAB. We began videotaping immediately after we deployed the MAB and we ended videotaping when we collected the MAB.

During familiarization trials we deployed the MAB with the top removed to acclimate subjects to the MAB and allow them to learn to associate the MAB with bait. Familiarization trials began when a hyena came within 5 m of the MAB and ended upon successful food retrieval (a “feed” trial) or when the hyena moved more than 5 m away from the MAB for more than 5 min. We recorded hyenas that approached the MAB, but did not make contact, as not participating in the trial. Average duration of familiarization trials was 11.7 ± 12.3 min.

If a hyena had a “feed” familiarization trial or successful test trial, and if it had moved at least 5 m away from the MAB, we immediately rebaited the MAB for successive testing. We gave hyenas successive trials as long as the testing conditions remained suitable, as described above, or until researchers ran out of bait. We did not administer successive trials following trials where every hyena that participated was unsuccessful because unsuccessful hyenas were those that had moved beyond 5 m from the MAB for more than five minutes without opening the MAB and these hyenas were extremely unlikely to spontaneously re-approach the MAB for another trial. On average, hyenas received 1.53 ± 1.25 trials per testing session and completed testing across 6.31 ± 2.58 separate sessions (Supplementary Fig. S1 ). Most sessions were separated by a median of 1 day (mean ± SD = 24.18 ± 60.30 days, range = 0–321 days).

We divided test trials into four different phases of testing. During Phase 1, we presented the MAB to hyenas with all four doors accessible. After a hyena had reached completion criterion for Phase 1, defined by success with the same door in three out of four consecutive trials, it progressed to Phase 2. During Phase 2, we blocked the door used in Phase 1 by bolting it shut. The same criteria for progression applied to subsequent phases until a hyena reached the criteria for progression with all four doors. We gave hyenas trials until they either reached criterion for all four doors or failed five consecutive trials during any phase of testing. We did not include hyenas that participated in fewer than five trials, of which none were successful, in our analysis. On average, hyenas participated in 9.64 ± 5.61 trials. Hyenas completed Phase 1 in 7.43 ± 2.93 trials (N = 72) either by reaching the criterion for progression or by failure, Phase 2 in 3.67 ± 1.11 trials (N = 15), Phase 3 in 4.08 ± 1.32 trials (N = 13) and Phase 4 in 4.25 ± 1.96 trials (N = 12).

We aimed to give every hyena two familiarization trials prior to being given the option to participate in test trials. On average we gave hyenas the opportunity to participate in 1.60 ± 1.54 (mean ± standard deviation) familiarization trials prior to their first Phase 1 trial, but hyenas only fed from the MAB on an average of 0.94 ± 1.11 familiarization trials prior to their first Phase 1 trial.

When we presented a group of hyenas with the MAB, we configured the MAB for the hyena at the most advanced phase of testing. For example, if one hyena in the group had progressed to Phase 3, but all the others were still on Phase 1, we would configure the MAB for the hyena on Phase 3 and block the doors that hyena had used in Phases 1 and 2. Overall, there were only five trials in total in which a hyena solved the MAB in a trial during the ‘wrong’ phase of testing by joining a trial where we configured the MAB for a group mate rather than itself. The average ‘trial group size’ per hyena per trial was 3.89 ± 3.71 hyenas (median = 3, range = 1–29). We calculated trial group size as a count of all hyenas that participated in a trial by contacting the MAB at any point in time during the trial. Overall, trial group size had a positive and significant effect on hyena participation; hyenas were slightly more likely to contact the box if there were other hyenas contacting the box (Binomial GLMM: z  = 9.19, P < 0.001; Supplementary Table S1 ). We also examined the effect of ‘overall group size’ which we calculated as a count of all hyenas present within 20 m of the MAB. Overall group size had slightly negative effect on participation (Binomial GLMM: z  = -9.81, P < 0.001; Supplementary Table S1 ); hyenas were slightly less likely to contact the box if there were more hyenas present within 20 m of the MAB.

Overall, we deployed the MAB 483 independent times including both familiarization trials and test trials to 280 different hyenas for a total of 2,891 observations. The dataset used in the present analysis only includes test trials from subjects that completed testing by reaching criterion for failure or subjects who had solved the MAB at least once (N = 72 hyenas, n = 694 observations). Of these 72 hyenas, 23 opened the MAB at least once (n = 318 trials). On average, we deployed the MAB 120.75 ± 25.80 times to each of our four study clans). In order to identify the 23 solvers, we deployed the MAB an average of 88.5 ± 34.72 (N = 4) times in each of our four study clans. In other words, by the 90 th deployment on average, we had no new subjects solve the MAB that had not already solved it at least once.

Statistical analysis

All statistical analyses were performed using the statistical software R 48 . Here, R values were calculated for subject ID in generalized linear mixed models (GLMMs). The rptR package also allowed us to estimate uncertainty around each point estimate for R via parametric bootstrapping (n = 1,000), in which we estimated a standard error, a 95% confidence interval, and a P value for each estimate of R. P values were generated using likelihood ratio tests where model fit was compared to a null model with no grouping factor. Here, we report both adjusted R values, calculated as a ratio of the variance explained by subject ID over the residual variance, and conditional R values, calculated as the ratio of the variance explained by subject ID over the total variance, including fixed effects.

Before calculating R values for innovative problem-solving ability across doors, we created a global GLMM that included door, age class, sex, and rank as fixed effects and subject ID as a random effect in order to identify factors that might influence innovativeness. We used the glmmTMB package to create all global models 49 . To identify fixed effects of importance, we used the ‘dredge’ function in the R package MuMIn 50 . We built our final model including only the factors with large or significant effects on innovative problem-solving as fixed effects. Dredge identified nine top models for our global model on problem-solving success across doors (Δ AICc < 4). None of the included effects were estimated as important in all nine top models, but age class was included in the most top models (N = 5) and had a large effect that tended towards significance (Supplementary Table S2 ). Therefore, we included only age class as a fixed effect in our final model.

Likewise, before calculating R values for innovative problem-solving ability across trials, we created a global model that included age class, sex, rank, trial number, and phase of testing as fixed effects, and subject ID as a random effect. To identify fixed effects of importance we used the ‘dredge’ function in the R package MuMIn 50 . We fixed trial number and phase of testing for inclusion in all models because we wanted to control for the effects of experience and task difficulty. Dredge identified eight top models with a delta AICc of less than four (Supplementary Table S3 ). Here, both trial number and phase had significant effects. Once again, age class was only marginally significant but also had the largest effect size (Supplementary Table S3 ). Therefore we included test trial, phase of testing, and age class in all subsequent models examining problem-solving success across trials (Models 2–5.9). In Model 2, phase of testing had a significant negative effect on the likelihood of solving the MAB (GLMM: β = -0.62, SE = 0.31, z  = -2.01, P = 0.04) which suggests that later phases of testing, where solutions that hyenas used previously were blocked, were indeed more demanding for hyenas. After controlling for the effect of phase, overall test trial number had a significant positive effect on the likelihood of solving the MAB (GLMM: β = 0.11, SE = 0.06, z  = 1.96, P = 0.05), indicating that hyenas were more likely to solve the MAB in later than earlier trials (Fig.  6 A). The positive effect of trial number could indicate that hyenas were learning how to improve their performance across trials, but this effect might also be biased by some subjects reaching the criterion to end testing (five unsuccessful trials in a row) and dropping out of the subject pool. To test this possibility we created another model where we restricted our dataset to the first ten test trials only for hyenas that had at least 10 trials, and found that test trial still had a significant positive effect on the likelihood of solving the MAB (GLMM: β = 0.30, SE = 0.10, z  = 2.91, P = 0.004, n = 139 trials, N = 14 hyenas; Fig.  6 B).

Before calculating R values all models were checked for collinearity by examining variance inflation factors (VIF). Test trial number and phase of testing consistently had VIFs > 4 in most of our models, however, we chose to include both because the high collinearity here is a result of our test protocol; hyenas only progressed to Phase 4 of testing after completing a relatively large number of trials. The main concern with high VIFs is that the estimates error and P values for the collinear factors will be increased; however, both test trial and phase of testing had consistent significant effects, which suggests that this was not a problem in our models. Next, we also examined QQ plot residuals and a histogram of the residuals using the R package DHARMa to confirm that model assumptions about the normality of residuals were not violated.

Ethics statement

This work was conducted under research permit no. NACOSTI/P/16/35513/10422, issued by the Kenyan National Commission on Science, Technology and Innovation. The data collection procedure followed here was also approved by the Michigan State University Institutional Animal Care and Use Committee (IACUC): AUF #04/16-050-00. All research procedures were designed to adhere to the American Society of Mammalogists (ASM) Guidelines for the use of wild mammals in research and education 51 and to the Association for the Study of Animal Behaviour (ASAB) Ethics Committee and the Animal Behaviour Society (ABS) Animal Care Committee Guidelines for the treatment of animals in behavioural research and teaching 52 .

Data availability

R code and data tables used in this manuscript are available in the Knowledge Network for Biocomplexity (KNB) Data Repository ( https://doi.org/10.5063/F1JQ0ZC5 ).

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Acknowledgements

We thank the Kenyan National Commission for Science, Technology and Innovation, the Kenya Wildlife Service, the Narok County Government, the Mara Conservancy, Brian Heath, James Sindiyo, and the Naibosho Conservancy for permissions to conduct this research. This work was supported by NSF Grants OISE1853934 and IOS 1755089 and by an NSF graduate research fellowship to LJU.

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Contributions

L.J.U., D.Z.H. and K.E.H. conceived the experiment, L.J.U. conducted the experiments and analyzed the data with support from D.Z.H. and K.E.H. All authors wrote and reviewed the manuscript.

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Correspondence to Lily Johnson-Ulrich .

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Johnson-Ulrich, L., Holekamp, K.E. & Hambrick, D.Z. Innovative problem-solving in wild hyenas is reliable across time and contexts. Sci Rep 10 , 13000 (2020). https://doi.org/10.1038/s41598-020-69953-5

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Problem solving in animals: proposal for an ontogenetic perspective.

Simple Summary

1. introduction, 2. factors affecting the development of problem solving, 2.1. instrinic factors, 2.1.1. direct genetic effects, 2.1.2. indirect genetic effects, 2.1.3. neuroendocrine effects—brain morphology, 2.1.4. neuroendocrine effects—hormones, 2.2. extrinsic factors, 2.2.1. physical environmental factors, 2.2.2. social environmental factors, 3. interacting factors that influence the development of problem solving, 3.1. gene × environment interactions, 3.2. neuroendocrine × environment interactions, 3.3. age effects, 3.4. learning and experience, 3.5. behavioural flexibility and personality, 4. forgotten components limiting our understanding of problem solving and its development, 5. an individual-centric focus can be beneficial, 6. conclusions, author contributions, acknowledgments, conflicts of interest.

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Rowell, M.K.; Pillay, N.; Rymer, T.L. Problem Solving in Animals: Proposal for an Ontogenetic Perspective. Animals 2021 , 11 , 866. https://doi.org/10.3390/ani11030866

Rowell MK, Pillay N, Rymer TL. Problem Solving in Animals: Proposal for an Ontogenetic Perspective. Animals . 2021; 11(3):866. https://doi.org/10.3390/ani11030866

Rowell, Misha K., Neville Pillay, and Tasmin L. Rymer. 2021. "Problem Solving in Animals: Proposal for an Ontogenetic Perspective" Animals 11, no. 3: 866. https://doi.org/10.3390/ani11030866

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8 of the Animal Kingdom’s Most Clever Problem Solvers

By editorial staff | apr 9, 2014.

Who ever said Mr. Fix-it had to be human?

1. Crows Make Dining Utensils

They say humans are toolmakers, but crows may be just as handy. The birds are known to pry out grubs buried inside trees with twigs. They’ll then strip off the twig’s bark and bend the end, turning it into a hook to dig out food. (Humans are the only other animals that use hooks!)

2. Hyenas Are Brilliant Teammates

To test whether hyenas were team players, researchers built a rig with two dangling ropes. When both ropes were yanked at the same time, a trap door opened, revealing a stash of food. Not only did hyenas work together to pull the ropes, they did it without training (monkeys, on the other hand, needed lots of help from humans to pass the test). Experienced hyenas even taught rookies in their pack how to do it.

3. Bees Are Efficient Architects

Honeycombs are the most efficient structures in nature. They use the least amount of wax for their size, and the hexagonal design makes the structure amazingly strong. It took humans over 2000 years of puzzling to figure that out!

4. Cows Celebrate a Job Well Done

Research shows that cows can feel emotions like fear and anxiety (and they even worry about the future). Cows also love to fix problems. A 2004 study found that when young cows solve problems, their heart rates increase. They even jump and kick when arriving at a solution—telltale signs that cows love having Eureka moments as much as we do.

5. Clark’s Nutcrackers Are Nature’s Traveling Salesmen

Pretend it’s errand time. You have to visit the supermarket, the pharmacy, and three other stores. All five are at separate locations. What’s the most efficient way to get to each one? Mathematicians call this “the traveling salesman problem,” and it’s harder than you think—it can even stump our best computers. However, it’s a snap for Clark’s Nutcrackers. Each year, these birds collect thousands of pine nuts and bury them in small stashes. When they return to pick up the goodies, not only do they remember where everything is, they can also calculate the fastest route to get them.

6. Pigs Rock at Video Games

When scientists built a snout-controlled game in which pigs had to move a shape across a computer screen and match it with a corresponding shape, they were naturals—they even performed better than some monkeys. Pigs are so smart that European regulators require pig farmers to provide “mentally-stimulating activity” for their swine (boredom makes pigs aggressive), and researchers designed a special video game to keep European pigs busy.

7. Parrots Are Feathered Linguists

Parrots aren’t capable of language, but they are good at imitating it. A parrot named Alex actually learned 100 English words, many of which he picked up without the motivation of food. Amazingly, Alex was able to make up words, too (he called apples “Banerries”—a blend of bananas and cherries). One time, when another parrot mispronounced a word, Alex yelled, “Talk clearly!”

8. Pigeons Make For Great Game Show Contestants

When researchers mapped the brain of pigeons, they discovered the areas for long-term memory and problem solving were wired just like a human’s. Pigeons are also better at game shows than us—studies show that pigeons play Monty Hall at a significantly higher success rate than humans.

Tool Use and Problem Solving in Animals

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Associative learning ; Insight ; Instrumentation ; Thinking ; Tool behavior ; Trial-and-error learning

Problem Solving: Acquisition of knowledge or behavior to overcome an obstacle(s) to obtain some desired state or commodity, or to overcome an obstacle(s) to avoid or escape some aversive state or agent. Insightful problem solving is the sudden appearance of a correct solution to a complex problem, after a period of nonproblem-directed activity, which in turn follows a period of incorrect responding, and is often said to involve cognitive reorganization and causal understanding of problem elements.

Tool Use: The external employment of an unattached or manipulable attached environmental object (the tool); to purposively alter the form, position, or condition of another object, another organism, or the user itself; when the user holds or directly manipulates the tool during or prior to use; and when the user is responsible for the proper and effective orientation of the...

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Beck, B. B. (1980). Animal tool behavior: The use and manufacture of tools by animals . New York: Garland.

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Davidson, J. E., & Sternberg, R. J. (2003). The psychology of problem solving . Cambridge: Cambridge University Press.

Köhler, W. (1925). The mentality of apes . London: Routledge and Kegan Paul.

Tomasello, M. (1999). The cultural origins of human cognition . Cambridge: Harvard University Press.

Shumaker, R., Walkup, K., & Beck, B. (2011). Animal tool behavior; The use and manufacture of tools by animals, (2nd ed. ). Baltimore: Johns Hopkins University Press.

Visalberghi, E., & Limongelli, L. (1994). Lack of comprehension of cause-effect relations in tool-using capuchin monkeys ( Cebus apella ). Journal of Comparative Psychology, 108 (1), 15–22.

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Animal Intelligence

Animal intelligence is a fascinating topic that has captured the attention of scientists and animal enthusiasts alike.

From dolphins communicating with one another to chimpanzees using tools, animals exhibit a range of cognitive abilities that challenge our traditional notions of what it means to be intelligent.

Studying animal intelligence can help us gain a deeper understanding of animal behavior and provide insights into conservation efforts and evolutionary studies.

1. Types of Animal Intelligence

1.1. instinctual intelligence.

Instinctual intelligence is innate and hardwired into an animal’s biology. This type of intelligence is crucial for an animal’s survival in the wild, as it enables them to respond to environmental stimuli and carry out basic functions such as finding food, avoiding predators, and reproducing .

Examples of instinctual intelligence include a bird building a nest, a spider weaving a web, and a newborn lamb standing up and taking its first steps within minutes of birth.

1.2. Learned Intelligence

Learned intelligence refers to an animal’s ability to acquire new knowledge and skills through experience and observation . This type of intelligence allows animals to adapt to changes in their environment and overcome new challenges.

1.3. Social Intelligence

Social intelligence refers to an animal’s ability to interact and communicate with others of its own species. This type of intelligence is important for maintaining social bonds and hierarchies within animal groups.

Examples of social intelligence include a pack of wolves hunting together, a group of meerkats working together to keep watch for predators, and a pod of dolphins coordinating their movements to catch fish.

1.4. Problem-Solving Intelligence

Examples of problem-solving intelligence include a crow using a stick to extract insects from a crevice, a gorilla solving a puzzle to obtain food, and a dolphin using its echolocation abilities to navigate through a maze.

2. Examples of Animal Intelligence

2.1. chimpanzees and tool use.

Chimpanzees are known for their impressive ability to use tools, which is considered a hallmark of intelligence. They have been observed using sticks to extract termites from their mounds, using rocks to crack open nuts, and even fashioning spears to hunt small mammals.

2.2. Dolphins and Communication

Dolphins are highly social animals that use a complex system of communication to interact with one another. They use a variety of vocalizations , such as whistles and clicks, to convey messages and coordinate their movements when hunting or traveling.

They are also capable of understanding and responding to human commands, which has made them a popular subject of research into animal cognition and communication.

2.3. Crows and Tool Making

Crows are known for their impressive tool-making abilities, which are on par with some of the most intelligent animals, such as chimpanzees and dolphins .

They have been observed using sticks to extract insects from crevices, bending wires to create hooks for fishing, and even dropping nuts onto roads to crack them open with passing cars.

These behaviors demonstrate their problem-solving abilities, as well as their ability to plan and use tools to achieve their goals.

2.4. Elephants and Self-Awareness

They are also capable of complex social behaviors, such as mourning their dead , caring for their young, and forming long-lasting bonds with other elephants. These behaviors demonstrate their social intelligence, as well as their emotional intelligence.

3. Factors Affecting Animal Intelligence

3.1. genetics.

Genetics plays a significant role in an animal’s intelligence, as certain traits and cognitive abilities are inherited from their parents. However, the extent to which genetics influences intelligence varies between species.

3.2. Environment

Environmental factors can have a significant impact on an animal’s intelligence. Access to food, shelter, and socialization can all influence an animal’s cognitive abilities.

For example, studies have shown that rats raised in enriched environments with access to toys, tunnels, and socialization have better cognitive abilities than those raised in impoverished environments.

3.3. Socialization

Socialization is a crucial factor in an animal’s intelligence, particularly for social animals that rely on communication and cooperation with others of their own species.

Socialization can help animals develop their communication skills, problem-solving abilities , and emotional intelligence.

3.4. Training

Training can also play a significant role in enhancing an animal’s cognitive abilities. Domesticated animals, such as dogs and horses, are often trained to perform specific tasks, which can improve their problem-solving abilities and socialization skills.

Similarly, animals used in research , such as primates and rats, are often trained to perform tasks that can help researchers understand their cognitive abilities and brain function.

However, it is important to consider the ethical implications of animal training and ensure that animals are treated with respect and dignity .

4. Importance of Animal Intelligence

4.1. understanding animal behavior.

Studying animal intelligence is crucial for understanding their behavior and how they interact with their environment.

By understanding the cognitive abilities of animals, we can better understand their social structures, communication methods , and problem-solving abilities .

4.2. Conservation Efforts

Animal intelligence is also important for conservation efforts, as it can help us understand how animals respond to changing environments and human activities.

For example, studying the problem-solving abilities of primates can help us design better conservation strategies to protect them from habitat loss and hunting.

Understanding the social structures and communication methods of elephants can help us develop more effective ways to protect them from poaching and other human threats.

4.3. Evolutionary Studies

Studying animal intelligence is also crucial for understanding the evolution of intelligence and cognitive abilities.

By comparing the cognitive abilities of different species, we can better understand how intelligence evolved and how it is influenced by factors such as genetics, environment, and socialization.

5. Ethical Considerations

5.1. use of animal intelligence in research.

The use of animal intelligence in research raises ethical concerns regarding the treatment of animals and the use of their cognitive abilities for human purposes.

Animal research can involve testing on animals to understand their cognitive abilities and brain function , which can involve subjecting them to stressful and invasive procedures.

Researchers should aim to minimize the use of animals in research and ensure that they are provided with appropriate living conditions and care .

5.2. Animal Rights and Welfare

The study of animal intelligence also raises ethical concerns regarding animal rights and welfare . As we gain a better understanding of animal intelligence, it becomes increasingly clear that animals can experience emotions and have complex cognitive abilities.

Animal welfare laws should be strengthened to ensure that animals are treated with respect and provided with appropriate living conditions.

Animal rights advocates also argue that animals have the right to be free from exploitation and suffering and that their cognitive abilities should be considered when making decisions about their treatment.

6. Frequently Asked Questions about Animal Intelligence

What are the dumbest animals.

Every species has unique characteristics and abilities that have helped them survive and thrive in their respective environments. It’s important to approach all animals with respect and appreciation for the role they play in the natural world.

It’s also important to note that intelligence is a complex and multifaceted trait, and it’s difficult to make comparisons across different species.

What Animal Represents Intelligence?

No single animal represents intelligence, as intelligence is a complex and multifaceted trait that varies across different species.

Many animals exhibit impressive cognitive abilities, including problem-solving , communicatio n, and tool use, and studying animal intelligence can provide valuable insights into the evolution of cognitive abilities and the diversity of life on Earth.

What Animal Symbolizes Wisdom?

The owl is often used as a symbol of wisdom in many cultures around the world. This association likely stems from the owl’s reputation for being a wise and knowledgeable animal, as well as its sharp intellect and ability to see in the dark.

The owl is often depicted as a wise and stoic creature, and its presence is thought to bring good luck and protection.

Similarly, in Native American cultures, the owl is often seen as a messenger of wisdom and a symbol of knowledge and intuition.

While the owl may be the most well-known animal symbol for wisdom, it’s important to remember that many other animals also exhibit impressive cognitive abilities and intelligence.

What Animal Is Closest to Human Intelligence?

Chimpanzees are often considered the closest in intelligence to humans, as they share many traits with humans, including the ability to use tools, communicate through language and facial expressions , and exhibit social behavior .

Dolphins , elephants , and some species of birds, such as crows and parrots , are also known for their high levels of intelligence and cognitive abilities.

What Is Animal Intelligence?

Animal intelligence refers to the cognitive abilities and mental capacities exhibited by various animal species.

Animals exhibit a wide range of cognitive abilities, including problem-solving , communication , tool use, memory, social behavior, and self-awareness.

Some animals, such as chimpanzees , dolphins , elephants , and some species of birds , are known for their high levels of intelligence and cognitive abilities.

The factors that affect animal intelligence include genetics, environment, socialization, and training .

How Is Animal Intelligence Measured?

Measuring animal intelligence is a complex and challenging task, as it involves assessing various cognitive abilities and mental capacities that vary across different animal species.

Several methods have been developed to measure animal intelligence, including observational studies , behavioral experiments , and cognitive testing.

Behavioral experiments involve setting up controlled situations in which animals must solve problems, navigate mazes, or perform other cognitive tasks to receive a reward .

Cognitive testing involves assessing an animal’s ability to recognize patterns, remember information, and use logic to solve problems.

Other tests focus on assessing an animal’s ability to learn and remember information, such as the Wisconsin General Testing Apparatus , which measures an animal’s ability to learn and remember the location of food rewards in a maze.

It’s important to note that measuring animal intelligence is not an exact science, and different methods may produce different results depending on the animal species, the testing conditions, and other factors.

What Is the Intelligence of a Dog?

Dogs are known for their intelligence and cognitive abilities, which can vary depending on the breed and individual dog.

Dogs are highly social animals and have been domesticated for thousands of years, which has led to the development of specialized skills and abilities that are not found in their wild counterparts.

Dogs are known for their ability to communicate with humans through vocalizations, body language, and facial expressions . They are also capable of learning and performing a wide range of tasks, such as obedience training , search and rescue, and therapy work.

Recent research has also shown that dogs have an impressive memory and can remember past events and experiences, as well as learn new information through observation and social learning.

Dogs have also been shown to exhibit empathy and emotional intelligence, and are capable of forming strong bonds with their human companions.

Do Animals Have Emotional Intelligence?

Yes, animals have been shown to exhibit emotional intelligence, which refers to the ability to recognize, understand, and regulate emotions in oneself and others.

Many animal species, including primates , elephants , dolphins , dogs , and even some birds , have been observed exhibiting a range of emotional behaviors, including empathy, altruism, grief, and joy.

Elephants have also been observed displaying empathy towards others in their group and will show signs of distress and grief when a member of their group dies .

Dogs, which have been bred for thousands of years to live and work alongside humans , are also known for their emotional intelligence and ability to form strong bonds with their human companions.

While the exact nature and extent of emotional intelligence in animals are still being studied, the evidence suggests that many animal species possess complex emotional lives and can experience a wide range of emotions.

In summary, the study of animal intelligence has revealed that animals can exhibit complex cognitive abilities, such as problem-solving , communication , and tool use. The factors that affect animal intelligence include genetics, environment, socialization, and training .

However, the ethical implications of using animal intelligence in research and the need for animal rights and welfare should also be carefully considered.

By studying animal intelligence and treating animals with respect and dignity, we can better appreciate the rich and diverse cognitive abilities of the animal kingdom.

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What makes an animal clever? Research shows intelligence is not just about using tools

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Humans set themselves apart from other animals in a number of ways, including our ability to make tools . When the anthropologist Jane Goodall discovered that wild chimpanzees frequently make and use tools, her advisor Louis Leakey famously quipped that “now we must redefine tool, redefine man, or accept chimpanzees as humans”.

Numerous other species have joined chimpanzees in knocking humans off their pedestal. Boxer crabs use stinging anemones as defensive weapons. American alligators place sticks on top of their snouts to catch egrets during their nesting season, when sticks become a valuable resource. Parrots frequently use a variety of objects to scratch themselves. A jay and a crow have once been observed to use sticks as weapons to jab at each other. Elephant bulls sometimes throw young elephants at fences to create a passage.

The list goes on , and continues to grow with new research. For example, we recently discovered that New Caledonian crows use tools to transport objects and that greater vasa parrots use pebbles to grind calcium powder from seashells for ingestion.

Despite the large variation in which species use tools and how, this behaviour still has special significance. New reports of tool use in animals often feature words such as “intelligent”, “smart” or “clever”. But is this really the case or is it time to abandon tool use as a measure of intelligence?

Termites build extraordinary structures that perfectly fit their needs. Their mounds have chambers that suit specific functions, connecting tunnels that allow large crowds to pass in both directions, and air flow that keeps the nest cool during the day and warm during the night. Designing such structures out of simple materials proves difficult even for human architects, yet it appears effortless for the tiny-brained termite. This is because building behaviour in termites is genetically encoded and often follows a fixed set of rules.

The same line of reasoning can apply to tool use. Simple rules and processes can lead to complex behaviours. Egyptian vultures can’t break ostrich eggs with their beaks, so they throw stones at the eggs to crack them. Young birds are not picky in what tools they use – they also try small stones, soft wood and even dung . They quickly learn what works and what doesn’t, but this doesn’t necessarily mean that the animal understands the physical properties of objects simply because it can successfully use them as tools.

Humans don’t always reason about their tool use either. Or have you often thought about how a ballpoint pen actually works?

Flexibility is key

Finding a single measure of intelligence for species as different as fish and elephants is extremely difficult . But one place to start is by looking at how flexibly animals can solve problems or, in other words, if they can learn more general rules and use these to solve new problems. For example, if an animal usually uses a stone to crack open a nut but there are no stones around, will they choose another heavy, hard object to crack open the nut? This would suggest a more abstract understanding about the type of object needed.

In the case of the Egyptian vulture and many other species, tool use occurs in one very specific context and is relatively inflexible. On the other hand, some species use a range of different tools to solve different problems. Chimpanzees, for example, have a broad toolkit : they use stones to crack nuts, leaf stems to fish for termites, stick tools to probe for honey and leaves to soak up water for drinking.

Similarly, New Caledonian crows make and use several different tools from different materials to probe for insects, and also use tools to explore new and potentially threatening objects .

This type of flexible tool use may allow individuals to innovate new and creative solutions to difficult problems. But even so, tool-using species aren’t necessarily better at solving problems than species that don’t use tools.

Not surprisingly, New Caledonian crows excel in experiments that require them to use tools. What is surprising, however, is the performance of their close relatives that are not natural tool users. For example, researchers have shown that rooks, which do not habitually use tools in the wild, can select tools of an appropriate size and even bend a piece of wire into a hook to retrieve food in experiments when there’s a reward at stake – their problem-solving skills help them work out how to use tools. In the same way, tool-using finches and apes are not necessarily better at problem-solving tasks, whether they involve tools or not, than species of finches and apes which do not typically use tools in the wild.

In addition to using problem-solving tasks, scientists can also compare species by calculating innovation rates , or how often members of different species adapt to new challenges. For example, blue tits invented a creative way to get food by pecking open the caps of milk bottles left on porches – a behaviour which spread quickly across the population.

With continued research on animal behaviour, scientists are constantly forced to reconsider what makes humans unique. Animals continue to surprise us, leading one researcher to ask: are we even smart enough to know how smart animals are?

Humans are clearly not the only animals to use tools for a wide variety of purposes. And while tool use may not always reflect the spark of a bright mind, it still provides a fascinating glimpse into how different species interact with their environments. Case in point: wild chimpanzees use leafy sponges to obtain fermenting sap from palms. Tools to tipple – another sign of intelligence?

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20 Animals With Problem-solving Abilities

Animals have evolved to develop a wide range of problem-solving abilities, from finding food to escaping predators. These abilities can be complex and fascinating to observe, and can play a critical role in the survival and evolution of the involved species. In this article, we will take a look at 20 animals with impressive problem-solving abilities.

1. The Chimpanzee

Chimpanzees are one of the most intelligent animals on the planet, and have been observed using tools to solve problems for over 60 years. They have been observed using sticks to extract termites from mounds, using rocks to crack open nuts, and using leaves as cups to drink water. They also have been observed using problem-solving skills to obtain food, by using cooperation and planning, such as working together to extract honey from beehives.

2. The Orangutan

Orangutans are known for their impressive problem-solving abilities, particularly when it comes to finding food. They have been observed using tools to extract insects from tree bark, using leaves as gloves to protect their hands from thorns, and using branches as levers to extract fruit from hard-to-reach places. They also have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain fruit or nuts.

3. The Octopus

Octopuses are known for their intelligence and problem-solving abilities. They have been observed using tools, such as coconut shells, to hide from predators and to create shelters. They also have been observed using problem-solving skills to escape from enclosures and to obtain food. They have been observed using trial and error, such as trying different ways to open a container to get food.

4. The Crow

Crows are known for their intelligence and problem-solving abilities. They have been observed using tools, such as sticks and hooks, to obtain food. They also have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to obtain food from a vending machine.

5. The Elephant

Elephants are known for their intelligence and problem-solving abilities. They have been observed using tools, such as branches and trunks, to obtain food. They also have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to obtain food from a vending machine.

6. The Black Bear

Black bears are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to obtain food from a vending machine, and using tools, such as sticks and rocks, to obtain food.

7. The Raccoon

Raccoons are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to obtain food from a vending machine, and using tools, such as sticks and rocks, to obtain food.

8. The Gorilla

Gorillas are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to obtain food from a vending machine, and using tools, such as sticks and rocks, to obtain food.

9. The Dolphin

Dolphins are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using cooperation and teamwork to herd fish into a tighter group for hunting. They have also been observed using tools, such as using sea sponges to protect their snout while foraging for food on the ocean floor.

10. The Parrot

Parrots are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of nut or fruit. They have also been observed using tools, such as using their beaks to hold objects in order to obtain food.

11. The Pig

Pigs are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of container. They have also been observed using tools, such as using their snout to move objects in order to obtain food.

12. The Rat

Rats are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of container or to find a way through a maze. They have also been observed using tools, such as using their teeth to gnaw through obstacles in order to obtain food.

13. The Dog

Dogs are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of container or to find a way through a maze. They have also been observed using tools, such as using their mouths to hold objects in order to obtain food.

14. The Ant

Ants are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using cooperation and teamwork to carry large objects back to their colony. They have also been observed using tools, such as using their mandibles to cut and manipulate objects in order to obtain food.

15. The Squirrel

Squirrels are known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of nut or container. They have also been observed using tools, such as using their front paws to hold objects to obtain food or to store food for later. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of nut or container, and using tools, such as using their front paws to hold objects to obtain food or to store food for later.

16. The Kea

The Kea, a parrot native to New Zealand, is known for their intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to open a certain type of container or to find a way through a maze. They have also been observed using tools, such as using their beaks to hold objects in order to obtain food.

17. The Arctic Fox

The Arctic fox is known for its intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to catch lemmings under the snow or using tools, such as using their paws to dig through the snow to catch lemmings.

18. The Grizzly Bear

The Grizzly bear is known for its intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to catch fish or using tools, such as using their paws to catch fish or using sticks to catch fish in shallow water.

19. The Arctic Tern

The Arctic tern is known for its intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to catch fish or using tools, such as using their beaks to catch fish or using shells to catch fish.

20. The Beluga Whale

The Beluga whale is known for its intelligence and problem-solving abilities. They have been observed using problem-solving skills to obtain food, such as using trial and error to figure out how to catch fish or using tools, such as using their beaks to catch fish or using shells to catch fish. They have also been observed using problem-solving skills to escape from enclosures, such as using trial and error to figure out how to open a certain type of gate or door.

In conclusion, animals are incredibly diverse in their problem-solving abilities and strategies, from using tools to cooperation and teamwork. These abilities play a crucial role in their survival and evolution, and are endlessly fascinating to study and observe.

What Puzzle-Solving Crows Can Teach Us About Animal Intelligence

Grade level, life science, stem practices, analyzing and interpreting data , asking questions and defining problems, activity type:, animal behavior , engineering and design challenge , animal adaptations.

Did that crow just figure out how to get food out of that tube? Yeah, it did. What do you notice about what it did before getting the food? Why did it select specific blocks? Based on what you observed, would you call this crow intelligent? Animal behavior researchers use observations of animals in the wild and puzzles like the one above to learn more about the problem-solving skills of a particular species.

In Aesop’s Fable, The Crow and the Pitcher , a thirsty crow uses stones to gain access to water in a pitcher—as they add more stones, the water level rises, and the crow is able to drink. While conducting her PhD study at the University of Auckland, New Zealand, Sarah Jelbert recreated The Crow and the Pitcher fable, showing that New Caledonian crows had the cognitive ability to solve multi-step problems. In this test, you might notice that the crow only uses particular objects. Jelbert’s study, which included six tests, seemed to show that these crows after being given objects with different weights were able to select objects that had a greater effect on the water level!

This resource was created as part of the Science Friday Educator Collaborative . If you are implementing this in the classroom, please check out the Educator’s Guide and folder of supplementals .

There are approximately 40 different species of corvids—crows and ravens—in the world, a group including even Blue Jays. Studies have shown that some species of crows can recognize human faces, use tools, play games, and even hold funerals . New Caledonian and Hawaiian crows have even been observed to use tools! Crows have proven themselves to be excellent problem-solvers. Some say their intelligence can be comparable to that of a seven-year-old child .

In this activity, you will observe the behavior of an animal and use those observations to design a puzzle that can test its ability to solve a problem. Along the way, we will explore structures of different animals, animal behavior, the role of puzzle boxes in animal behavior research, and think about how problem-solving abilities might help different animals survive and thrive.

Animal Behaviors

No question about it, humans are fascinated by other animals. People can easily spend hours watching animals at zoos and wildlife parks or even people watching (we are animals too). Some of us can spend hours on the internet watching cat videos . Humans are not the only ones— other animals are fascinated with each other, too. Why is it that we are so drawn to other animals? It might be because we are looking at how similar and different they are from us. We wonder, do they think and feel like we do?

When you watch an insect moving around or pet playing, you are observing animal behavior , how an animal acts in response to their environment (living and nonliving). While these behaviors may seem random (seriously, why do cats chase lasers?), they all serve a purpose to help that animal and their young survive and thrive.  You can read about more basics of animal behavior in this resource from Khan Academy .

Let’s take a minute to observe the behavior of crows to get a sense of their behaviors and how complex and intelligent these creatures are.

Observe Wild Crows

As you watch the videos of wild crows, write down your observations and wonderings on your Crow Behavior Worksheet . Be sure you look at both what they do and how they do it. The first video is embedded below. While watching you will notice researchers, but focus on the parts with crows without humans.

Watch the second video,  Crows in Nature . Note additional observations on your Crow Behavior Worksheet .

Use your observations of these wild crows to answer the questions below. You can record your answers on your Crow Behavior Worksheet .

• What behaviors did you observe? (What is the crow doing?)
• What did you notice about the bodies of the crows? What structures did you see?
• How did the crows use their bodies? What movements did you notice?
• Why do you think they did that behavior? How does it help them survive?
• Based on what you saw the crows doing in the video, what else do you think they can do?

Observe Learned Behavior In Crows

Humans can influence or alter the behavior of some animals. The examples that follow describe two projects where humans developed systems that train crows to accomplish a specific task. Write down your observations of each system on your Crow B ehavior Worksheet .

Example One: In 2018, a Dutch startup called CROWded Cities invented the ‘Crowbar’, a device that releases a treat every time a crow deposits a cigarette butt into it. Using a reward system, they trained some crows to clean up cigarette butt litter. Check out the design in the image below and watch this video about the Crowbar device .

Example Two: In a similar idea, the French theme park Puy du Fou trained rooks , a species in the crow family primarily found in Europe and Asia, to pick up litter left behind by park guests in exchange for food. These rooks were not wild but raised and trained. Check out the video of the rook work at Puy du Fou .

Based on your observations of these two systems think about the questions below, which you can also find on your Crow Behavior Worksheet :

• How did the crows use their bodies to accomplish these tasks?
• Are humans using innate or learned behavior in crows when they design these systems? A combination?
• What could you do to figure out if your hypothesis is true?
• Based on what the crows learned in these two instances (Crowbar and Puy du Fou), what else do you think they can do?

Activity 1: Animal Observations

Like humans, other animals use a wide variety of behaviors to solve problems. A single animal can perform many different behaviors, sometimes at the same time as one another!

One of the tools that scientists use to keep track of animal behavior is an ethogram , a chart where they tally how often they see an animal performing particular behaviors. For example, if you were studying grooming behavior, you may observe how many times an animal grooms another animal in an allotted time period.

You are going to use a similar system. You will be observing and recording a variety of animal behaviors using live wildlife webcams. The more time you spend making observations, the better! You will choose three behaviors and then observe for a period of ten minutes. The type of animal behavior observations you make will be dependent on the animals themselves, you can find a general reference on page three of the Animal Observation Chart .

NOTE: It may take more than one observation session for your animal to cooperate (for example, if they are sleeping during your entire observation period!)

• Choose an animal to observe. You may have to check multiple webcam feeds before making your decision. Our favorite webcams are linked in the slide above.
• Write its common name and species (if known) on your Animal Observation Chart .
• Brainstorm the types of behaviors you expect to see, and discuss with a partner if you able. Think about what behaviors might be related to staying healthy, caring for young, staying safe, and practicing for the future. There is a list on page three of your chart for reference.
• Choose three behaviors and write them at the top of each of the three columns on your Animal Observation Chart .
• Record the number of times you observe each behavior using tally marks in each time block. (See examples below.)

After your webcam observations, head outside! By observing animals in their natural habitat, you may see different behaviors than those seen in captive animals. There is a lot we can learn from the animals in our neighborhoods and even in our own backyards. Be sure to follow these guidelines for maintaining space for nature to thrive.

"Crows, A Bird That’s Not Bird-Brained"

Species in the crow family—Corvidae—are considered highly intelligent animals. What are the behaviors of crows that led to this conclusion?

Kaeli Swift is a corvid researcher at the University of Washington, and her work has shown that American crows play games, hold funerals, and even recognize human faces. We are going to listen to some selections from her Science Friday interview and excerpts from an AMA she did with Science Friday on Reddit.

While listening to and reading, write down any interesting facts you learned or new questions you have about the behavior of birds in the crow family.

If you can, do this activity with other people using sticky notes so that you can collect more ideas.

• Kaeli Swift also participated in an AMA (Ask Me Anything) discussion on Reddit.com with Science Friday staff on corvids. Read some of her responses to questions in Questions About Crows? We’ve Got You Covered .

Click here for transcripts for the audio clips .

Now, group your sticky notes into themes. If you are working with other people, look at the ideas they generated. Did they find the same things interesting? Did they ask similar questions to yours? Share an idea that they didn’t have on their list.

Intelligence can be defined in many ways. You might have noticed that when Swift says crows are “intelligent,” she is referring specifically to the ability to solve certain problems with a variety of approaches. This comes from observing their behavior in the wild and in controlled settings. By designing puzzles for crows to solve, researchers can pinpoint and test the problem-solving abilities of these amazing animals.

Questions About Crows? We’ve Got You Covered

Activity 2: the games people play.

So, who could solve a puzzle faster, you or a crow? Well, that all depends on the puzzle. We use problem-solving skills constantly—to cross the street, eat a meal, or when having fun. Let’s try some puzzles designed specifically for humans. Logic puzzles are designed to be fun, but some of the puzzles will be more difficult than others to solve.

Spend a maximum of five minutes on each puzzle in the slides below. You can always go back later if you need more time.

After your puzzle time, answer the following questions on your Games People Play Sheet :

• What skills or abilities were required for each puzzle?
• How might those skills or abilities help us survive?
• Why would an animal (other than a human) have a hard time solving these puzzles?
• Some animals appear to play games. What animals have you observed playing? What were they doing? How could this behavior benefit the animal?
• Could you directly compare a human and another animal solving the same puzzle? How would you “score” them on the same puzzle?

Animals use their instincts and even learn new behaviors while engaging in play. Animal observations can tell you a lot, but often researchers use puzzles to help them understand the behaviors and instincts of the animals they are studying. Researchers may have animals work through a series of problems to gain access to food or to train them for other tasks, for example, search and rescue dogs.

When designing a puzzle, researchers consider both the physical and cognitive limitations of the organism they are researching. Thinking back to the videos and observations you made of corvids and other animals, how could their anatomy be compared to human anatomy? Can direct comparisons be made between similar anatomical features, such as wings and arms? How do humans and other animals use similar anatomical features in different and/or similar ways?

Thinking about our crow observations…

• What do you think it would look like if a crow were too frustrated to continue a puzzle?
• What types of incentives would encourage them to try a puzzle?
• What senses, skills, or body parts did you rely on to solve these puzzles? Does a crow have the same body parts?
• How would you need to modify one of the puzzles you tried to be relevant and achievable by a crow?

Activity 3: Design A Challenge

You already challenged yourself by attempting and a series of logic puzzles designed for humans; now, you will design a challenge (or puzzle) for an animal you’ve observed.

Engineering Design Process

To complete your animal challenge, you will engage in the engineering design process —a series of activities that engineers, researchers, designers, and many others use to solve a problem. Your problem: design a challenge for an animal that will help you learn more about its problem-solving abilities. Design a challenge for them to solve to gain access to a food treat. Here’s the process:

Your animal challenge design must do the following:

• Focus on one animal.
• Identify particular behaviors that inspired the design.
• Test an animal’s ability to reason/think/problem-solve/have fun.

Choose An Animal

What animal do you want to create your challenge for? Consider using one of the animals you observed during this activity, but feel free to shift to another animal you’ve observed, maybe a local animal? Crows? Pigeons? Squirrels? Coyote?

Once you’ve selected, write down your choice on your Animal Challenge Design Workbook .

Research Your Animal

Whether you are looking at an animal you observed already or have selected a new one, we still need more information before we can design our puzzles. Engineers gather research to help them create their solutions to problems, and in your case, you need information that will help you design a challenge for an animal that will help you learn more about its problem-solving abilities. Remember that researchers consider both the physical and cognitive limitations of the organism they are researching.

As you gather research, add them to the ‘Research’ section of your Animal Challenge Design Workbook .

Guiding Questions

• What natural behaviors are observed in the animal you chose? (You can use your observation notes here, but also research what other scientists have observed in your animal.)
• Not all animals have hands, so your challenge must be solvable using your animal’s available structures. What is the body of your organisms like? How does it move? Grab objects?
• Describe the niche of your animal. Where does it live? How does it interact with the living and non-living things in its surroundings? What food do they love to eat?
• Do they display problem-solving skills? If so, describe how they go about solving a problem.

Another key aspect of research is looking into challenge designs of other researchers for inspiration. Watch the following videos of animals solving challenges to help inspire your own design.

For each video, answer the following questions in your Animal Challenge Design Workbook :

• What is the animal doing?
• Are the behaviors you saw play behaviors?
• What function do the behaviors serve?
• Could this behavior help the animal survive? Why or why not?

Brainstorm & Sketch Your Design

Before you dive into drawing and creating, we should start by identifying your criteria —define what would make your puzzle box successful—and your constraints —limitations you must consider. Be sure to think about the physical and cognitive abilities of your organism when outlining your criteria and constraints in your Animal Challenge Design Workbook .

Use your research on your animal, defined criteria and constraints, and your available materials to generate possible designs for challenges.

• What ability did you want to test?
• What are some initial ideas for features of your challenge? Give yourself time to write down all the ideas you have, then read your list and chose 1-2 you want to try.
• Which of the animal’s behaviors do you think might help them solve the challenge?
• Can your animal grip something? If not, be sure to think about how the puzzle will be held steady so that it does not move around or tip over.
• Do you need to provide tools (such as sticks or stones) for the animal to use to solve the challenge? Will they be able to use those tools?

Think about and gather available materials. What materials could you use to complete your design? Are there materials that you can reuse/upcycle in your design? These are prototypes, so our goal is not a perfect and durable animal challenge, but rather something that allows us to test whether your design would work for the organism you selected.

Remember, if you don’t have access to materials you can always create a schematic —a drawing with detailed materials and dimensions that someone could use to create your design—or use a free design program like Tinkercad to create your design.

• What materials will work best in your design?
• Create a labeled design sketch that includes information about potential materials, size, and moving parts of the challenge.

Activity 4: Create Your Animal Challenge Prototype And Test It Out

Create A Working Prototype

Now it’s time to get building. Working alone or with others, create a working prototype of your animal challenge for testing (by another human) with your criteria and animal research in mind. Your prototype does not have to be perfect; that’s part of the process. Prototypes are meant to help us decide if our design can fulfill the criteria. You can develop better and better prototypes over time, and level up the materials you use.

Note: If you are working with potentially dangerous materials (e.g., wood, nails, cutting tools) remember to have an adult present to help you and to wear protective gear (e.g., safety glasses, gloves) when needed.

While you are building your prototype, be sure to keep track of challenges and alterations in your Animal Challenge Design Workbook .

If working in a group, it is important that everyone in the group works on the build. Before beginning, decide what role each team member will play and what portion they will help build. Remember that during the building process, we should help and support each other. Everyone shares responsibility for safety. Behave in a way that protects everyone’s safety and your own.

Preparing For Testing

Congratulations, you have finished building a prototype of your animal challenge! You will not be testing your prototypes on actual animals, for your safety and theirs. Instead, you will be testing them on another person!

Before another person tests your puzzle, you need to give them some information. You can add this to your Animal Challenge Design Workbook .

• Write a short statement in which you say what animal the puzzle was designed for and what you were intending to test. For example, in the photos above, the box was designed to test a corvid bird’s ability to [insert a problem-solving behavior] in order to open the box to retrieve a food treat inside.
• Provide a picture of key structures of your chosen animal, pointing out how the tester might need to modify how they engage with the puzzle.
• Describe any special considerations you had to make while designing the box. The test pictured above was designed to become more difficult each time the corvid gained access to the treat.

Testing Designs

• Give your tester time to read your short statement about the challenge and your organism.
• Tell them they will have five minutes to engage with the challenge.
• Describe any adaptations or tools you will have them use to engage with the puzzle. For example, you might provide pliers or tweezers to help them mimic a beak, or a hand rake to mimic long claws.
• Tell them that they cannot use their hands except to hold any tools you are using to mimic the structures of the animal you are imitating.
• Answer any questions they might have about engaging with the puzzle.
• As someone tries to solve your challenge, keep notes about their successes and failures in the ‘Test And Improve’ section of your Animal Challenge Design Workbook .
• Conduct a brief debrief about their experience solving your challenge. You can use some of the reflection questions below.

Reflection Questions

• Did the puzzle move when they tried to solve it?
• Tester: Would the intended animal be able to detect the treat inside to motivate it to do the puzzle?
• Tester: Was it too easy to be a good measure of intelligence?
• Tester: What did you have to figure out in order to solve the puzzle?
• Tester: What types of logical reasoning would an animal need in order to solve it?

How Might Problem-Solving Abilities Help An Animal Survive?

Use what you have learned about animals and their problem-solving abilities to answer one of the following questions:

• Crows are very intelligent animals. Write about different ways crows demonstrate their intelligence.
• Could we use the ability of animals to learn, and in some cases solve problems, to help conserve them? For example, sometimes animals face new, unfamiliar predators and they have no instinctual defenses. How might learning be important to these animals?

Want to explore more about corvids or animal cognition? Check out some of these resources!

• Corvid Cognition
• Corvid Research Blog
• Everything Worth Knowing About Animal Intelligence

Are We Smart Enough to Understand How Smart Animals Are?

Next generation science standards.

This resource works toward the following performance expectations:

• 4-LS1-1 : Construct an argument   that plants and animals have   internal and external structures that function to support   survival, growth, behavior, and reproduction.
• 3-LS4-3 : Construct an argument with evidence that   in a particular habitat some organisms   can survive well, some survive less well, and some cannot survive at all.
• 3-5-ETS1-1 : Define a simple design problem   reflecting a need or a want   that includes specified criteria for success and constraints on materials, time, or cost.
• 3-5-ETS1-2 : Generate and compare multiple   possible solutions   to a problem based on how well   each is likely to meet the criteria and constraints of the problem.
• 3-5-ETS1-3 : Plan and carry out fair tests in which variables are controlled   and failure points are considered to identify aspects of a model or prototype that can be improved.

Credits: Written by Hillary Gutierrez Edits by Shirley Campbell and Xochitl Garcia Review by: Laura Diaz, Jessica Metz, Chenille Williams, Jennifer Powers, Katie Brown, Stacey George, Marta Toran, and Michael Kosko Digital Production by Xochitl Garcia

Meet the Writer

About Hillary Gutierrez

Hillary Gutierrez teaches elementary science in Dixon, Calif., where she loves getting messy in the classroom. She believes hands-on learning activities are key to the educational experience. Hillary has a bachelor’s in anthropology from the University of California, Davis, a master’s in STEAM Education from the University of San Diego, and various teaching certificates in geology and special education.

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Köhler’s best known contribution to animal psychology arose from his studies of problem solving in a group of captive chimpanzees . Like other Gestalt psychologists, Köhler was strongly opposed to associationist interpretations of psychological phenomena, and he argued that Thorndike’s analysis of problem solving in terms of associations between stimuli and responses was wholly inadequate. The task he set his chimpanzees was usually one of obtaining a banana that was hanging from the ceiling of their cage or lying out of reach outside the cage. After much fruitless endeavour, the chimpanzees would apparently give up and sit quietly in a corner, but some minutes later they might jump up and solve the problem in an apparently novel manner—for example, by using a bamboo pole to rake in the banana from outside or, if one pole was not long enough, by fitting one pole into another to form a longer rake. Other chimpanzees reached the banana hanging from the ceiling by using a wooden box, or a series of boxes stacked precariously on top of one another, as a makeshift ladder.

Köhler believed that his chimpanzees had shown insight into the nature of the problem and the means necessary to solve it. According to Köhler’s interpretation, the solution depended on a perceptual reorganization of the chimpanzee’s world—seeing a pole as a rake, or a series of boxes as a ladder—rather than on forming any new associations. But subsequent experimental analysis has cast some doubts on Köhler’s claims. The critical observation is that the sorts of solutions that Köhler took as evidence of insight quite clearly depend on relevant prior experience. Chimpanzees will not fit two poles together to form a rake or stack boxes up to form a ladder unless they have had a great deal of prior experience with those objects. This experience may well occur during play, when the young chimpanzee discovers that using a stick can extend the reach of an arm, or that standing on a box can put one within reach of high objects. Thus, what Köhler was studying, without knowing it, was probably the transfer of earlier instrumental conditioning to new situations. As we have already seen, the ability to transfer an old solution to a new stimulus situation is an important one, relevant to a wide range of problem-solving activities. This ability is not at all well understood, but it will not necessarily be greatly illuminated by describing it as insight. Certainly it is not a process unique to the great apes: if the component tasks are sufficiently well-structured, even pigeons can put together two independently learned patterns of behaviour to solve a novel problem.

Combining information from separate sources to reach a new conclusion is one form of reasoning . The paradigm case of reasoning is the solution of syllogisms; for example, when we conclude that Socrates is mortal given the two separate premises that Socrates is a man and that all men are mortal. Employing transitive inference , we can use the premises that Adam is taller than Bertram and that Bertram is taller than Charles to conclude that Adam must be taller than Charles. Reasoning has often been regarded as a uniquely human faculty, one of the few factors, along with the possession of language, that distinguishes us from the rest of the animal kingdom.

But are humans the only animals that can reason? The unsatisfying answer must be that it depends on what is meant by reasoning. In a very general sense, most animals appear perfectly able to arrive at a conclusion based on combining information obtained on two separate occasions. A formal demonstration is provided by an experiment on instrumental conditioning discussed earlier. If rats learn that pressing a lever provides sucrose pellets and later learn that eating sucrose pellets makes them ill, they will subsequently put these two pieces of information together and refrain from pressing the lever. Monkeys and chimpanzees, however, have been trained to solve problems that appear more similar to transitive inference. They are first given discriminative training between pairs of coloured boxes, called, for example, A, B, C, D, E. Confronted with the choice between A and B, they learn that choice of A is rewarded and B is not. When B and C are the alternatives , they learn that B is correct; when C and D are the alternatives, C is correct; and so on. Although choice of A is always rewarded, and that of E never is, the remaining three boxes each are associated equally often with reward and with nonreward. Nonetheless, given a choice between B and D on a test trial, the animals choose B.

Syllogistic and transitive inference are not the only forms of reasoning: humans also reason inductively or by analogy . Indeed, analogical reasoning problems (black is to white as night is to —?) form a staple ingredient of some IQ tests. One chimpanzee, a mature female called Sarah , was tested by David Premack and his colleagues on a series of analogical reasoning tasks. Sarah previously had been extensively trained in solving matching-to-sample discriminations , to the point where she could use two plastic tokens, one meaning same , which she would place between any two objects that were the same, and another meaning different , which she would place between two different objects. For her analogical reasoning tasks, Sarah was shown four objects grouped into two pairs, with each pair symmetrically placed on either side of an empty space. If the relationship between the paired objects on the left was the same as the relationship between those on the right, her task was to place the same token in the space between the two pairs. Thus in one series of geometrical analogies , a simple problem would display a blue circle and a red circle on the left and a blue triangle and a red triangle on the right; the correct answer, of course, was same . But Sarah was equally correct on more complex problems, even when the relationships in question were functional rather than simply perceptual. For example, she correctly answered same when the two objects on the left were a tin can and a can opener and the two on the right a padlock and a key.

Solution of analogies requires one to see that the relationship between one pair of items (whether they are words, diagrams, pictures, or objects) is the same as the relationship between a different pair of items. If simple matching-to-sample requires animals to see that one comparison stimulus is the same as the sample and another is different, solving analogies requires them to match relationships between stimuli. The difficulties encountered in training pigeons to generalize simple matching-to-sample discriminations does not encourage one to believe that they would find analogies very easy.

The ability to speak was regarded by Descartes as the single most important distinction between humans and other animals , and many modern linguists, most notably Noam Chomsky , have agreed that language is a uniquely human characteristic. Once again, of course, there are problems of definition. Animals of many species undoubtedly communicate with one another. Honeybees communicate the direction and distance of a new source of nectar; a male songbird informs rival males of the location of his territory’s boundaries and lets females know of the presence of a territory-owning potential mate; vervet monkeys give different calls to signal to other members of the troop the presence of a snake, a leopard, or a bird of prey . None of these naturally occurring examples of communication, however, contains all of the most salient features of human language. In human language, the relationship between a word and its referent is a purely arbitrary and conventional one, which must be learned by anyone wishing to speak that language; many words, of course, have no obvious referent at all. Moreover, language can be used flexibly and innovatively to talk about situations that have never yet arisen in the speaker’s experience—or indeed, about situations that never could arise. Finally, the same words in a different order may mean something quite different, and the rules of syntax that dictate this change of meaning are general ones applying to an indefinite number of other sequences of words in the language.

During the first half of the 20th century, several psychologists bravely attempted to teach human language to chimpanzees. They were uniformly unsuccessful, and it is now known that the structure of the ape’s vocal tract differs in critical ways from that of a human, thus dooming these attempts to failure. Since then, however, several groups of investigators have employed the idea of teaching a nonvocal language to apes. Some have used a gestural sign language widely used by the deaf to communicate with one another; others have used plastic tokens that stand for words; still others have taught chimpanzees to press symbols on a keyboard. All have had significant success, and several apes have acquired what appears to be a vocabulary of several dozen, and in some cases 100 or 200, “words.”

Washoe , a female chimpanzee trained by Beatrice and Allan Gardner, learned to use well over 150 signs. Some apparently were used as nouns, standing for people and objects in her daily life, such as the names of her trainers, various kinds of food and drink, clothes, dolls, etc. Others she used as requests, such as please, hurry , and more ; and yet others as verbs, such as come, go, tickle , and so on. Sarah , the chimpanzee trained by Premack to use plastic tokens as words, also apparently learned to use tokens for nouns, verbs ( give, take, put ), adjectives ( red, round, large ), and prepositions ( in, under ). But do these signs or tokens really function as words? Does the ape using them, or obeying instructions from a trainer who uses them, really understand their meaning? Or is the ape simply performing various arbitrary instrumental responses in the presence of particular stimuli because she had previously been rewarded for doing so?

There can be little doubt that chimpanzees do have some understanding of what their “words” refer to. Sarah responded appropriately with her token for red if asked the question “What colour of apple?” both when an actual red apple was shown as part of the question and when only the token for an apple (which happened to be a blue triangle) was presented. To Sarah, the blue triangle surely stood for, or was associated with, the red apple. In another study, after two chimpanzees had been taught the meaning of a number of symbols for different kinds of food and different tools, they were able not only to fetch the appropriate but absent object when requested to do so, but they could also sort the symbols into two groups, one for foods and one for tools. In another series of studies, a pygmy chimpanzee named Kanzi demonstrated remarkable linguistic abilities. Unlike other apes, he learned to communicate using keyboard symbols without undergoing long training sessions involving food rewards. Even more impressive, he demonstrated an understanding of spoken English words under rigorous testing conditions in which gestural clues from his trainers were eliminated.

As noted above, human language is more than a large number of unrelated words: in accordance with certain implicitly understood syntactic rules, humans combine words to form sentences that communicate a more or less complex meaning to a listener. Can apes understand or use sentences? Undoubtedly they can put together several gestures or tokens in a row. A chimpanzee named Lana, who was trained to press symbols on a keyboard, could type out “Please machine give Lana drink”; Washoe and other chimpanzees trained in gestural sign language frequently produced strings of gestures such as “You me go out,” “Roger tickle Washoe,” and so on. Skeptical critics, however, have raised doubts about the significance of these strings of signs and symbols. They have pointed out, for example, that when Lana pressed a series of coloured symbols on her keyboard, it was humans who interpreted her actions as the production of a sentence meaning “Please machine give Lana drink.” Might it not be equally reasonable to say that she learned to perform an arbitrary sequence of responses in order to obtain a drink? Pigeons can be trained to press four coloured keys—red, white, yellow, and green—in a particular order to obtain food. Psychologists do not feel any temptation to interpret this behaviour as the production of a sentence. What is it about Lana’s behaviour that requires this richer interpretation?

In the case of apes trained to use sign language, two other doubts have been raised. First, there is some reason to believe that a disappointingly high proportion of the apes’ gestures may be direct imitations of gestures recently executed by their trainers. Second, a sequence of gestures interpreted as a single sentence is often just as readily interpreted as a number of independent gestures, each prompted, in turn, by a gesture from the trainer. Both these conclusions are based on careful examinations of video recordings of interactions between trainers and apes. Whether they will turn out to be generally true remains an open, and heatedly debated, question.

Without any explicit training, apes have nevertheless learned to produce strings of two or three signs in certain preferred orders: “more drink” or “give me,” for example, rather than “drink more” or “me give.” Do the animals understand that a string of signs in one order means something different from the same signs in a different order? The following anecdote is suggestive. A chimpanzee called Lucy was accustomed to instructing her trainer, Roger Fouts, by gesturing “Roger tickle Lucy.” One day, instead of complying with this request, Fouts signed back “No, Lucy tickle Roger.” Although at first nonplussed, after several similar exchanges Lucy eventually did as asked. A simple instance of this sort proves little or nothing, but it may suggest what is needed—namely, that Lucy should understand that changing the order of a set of signs alters their meaning in certain predictable ways. She must generalize the rule that the relationship between the meanings of the signs A-B-C and C-B-A (the same signs in reverse order) is similar to the relationship between the meanings of certain other triplets of signs in her vocabulary when their order is reversed.

The research on language in apes forcefully illustrates a conflict, or tension , that is common to many other areas of research on learning in animals. If the investigators are interested in language and communication, they can attempt to communicate as naturally and informally as possible with their apes. This approach involves treating an ape as a fellow social being, with whom one plays and interacts as far as possible as one would with a human child; it also, almost inevitably, results in a style of research where it is exceptionally difficult to control precisely the cues that the ape may be using and even hard to avoid an overly rich, anthropomorphic interpretation of the ape’s behaviour. If, on the other hand, the researchers are interested in rigorous experimental control and economical interpretation of the processes underlying the ape’s performances, they are likely to set the ape formal problems to solve, with rewards for correct responses and no rewards for errors. But such an approach, however scientific it may seem, must run the risk of missing the point. This is not language; the investigators are not communicating with the ape in the way they would communicate with a child. The very nature of the experimental problems ensures that the ape will not use its language in the way that a child does: to communicate shared interests, to attract a parent’s attention to what the child has seen or is doing, to comment on a matter of concern to both.

There is no resolution to this conflict, for both approaches have their virtues as well as their dangers, and both are therefore necessary. In just the same way, the study of a rat pressing a lever in a Skinner box or of a dog salivating to the ticking of a metronome seems to many critics a sterile and narrow approach to animal learning—one that simply misses the point that, if the ability to learn or profit from experience has evolved by natural selection , it must have done so in particular settings or environments because it paid the learner to learn something. It would be foolish to deny this obvious truism: of course it pays animals to learn. Indeed, it may pay them to learn quite particular things in specific situations, and different groups of animals may be particularly adapted to learning rather different things in similar situations. None of this should be forgotten, and the study of such questions requires the scientist to forsake the laboratory for the real world, where animals live and struggle to survive. But few sciences can afford to miss the opportunity to manipulate and experiment under laboratory conditions where this is possible, and none can afford to forget the benefits of precise observation under controlled conditions.

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5.1 Animal problem-solving: using tools

From the earliest, most primitive stick or piece of rock, to the most sophisticated supercomputer or jet aircraft of modern times, humans have been using tools to solve problems since prehistoric times.

Given the advantages of using tools, it is perhaps surprising that it's not more common for animals to use them. There are examples of tool use by other species: some otters use stones to break open shellfish; some monkeys do the same to break open nuts; and some chimpanzees ‘fish’ for termites with sticks (Emery and Clayton, 2009). But it appears to be a general pattern that all humans use tools and most other species do not. Is this because animal minds do not have the capability to use tools? Tool use does, after all, involve a number of aspects of executive function, including: working out what a tool can be used for; planning how to use it; and remembering what the tool has managed to do (and failed to do) before.

While other species may not have the same degree of neocortical development and executive function as humans, are they able to use tools to solve problems to some extent?

There is evidence that the nearest evolutionary neighbours of humans, the other great apes (gorillas, chimpanzees, bonobos and orangutans), are able to solve problems using tools. A typical laboratory experiment involves putting food into an apparatus where the animal cannot reach it using their bodies alone, e.g. if testing chimpanzees, the apparatus will prevent the chimpanzees from reaching the food with their fingers. Tools, such as sticks of varying lengths or shapes, are left near the apparatus that will, if used correctly, allow the animal to access the food. Visalberghi and colleagues (1995) showed that a variety of primate species could solve such problems, but great apes were better than other primates (monkeys) at selecting the best tools, and adapting tools to the needs of the task.

But possibly the best non-human tool users are, perhaps surprisingly, to be found in species without a neocortex: birds. Emery and Clayton (2009) and Seed and Byrne (2010) give examples of a number of bird species with impressive tool-using and problem-solving abilities, including crows, jays and finches. One of the star species, though, is the New Zealand kea (Figure 8).

This is a photograph of a kea − a type of parrot from New Zealand that has impressive problem-solving abilities. The kea has green and blue plumage and is perched on a window frame.

Keas have been shown to solve a fairly simple problem (where food is obtained by hauling up a string) on the first attempt − suggesting they had mentally worked out the solution before starting the task, rather than by trial and error (Werdenich and Huber, 2006). They have also been shown to solve ‘second-order’ tool-use tasks, where one tool must be used to acquire or adapt another, in order to then complete the task (Auersperg et al., 2010), and there is evidence that they can learn from observing other keas performing a problem-solving task (Huber et al., 2001). As well as being able to solve problems as individuals, keas have been shown to collaborate to solve problems too (Tebbich et al., 1996).

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Introduction, supplementary material.

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Innovative problem solving in nonhuman animals: the effects of group size revisited

Handling editor: Alexei Maklakov

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Andrea S. Griffin, David Guez, Innovative problem solving in nonhuman animals: the effects of group size revisited, Behavioral Ecology , Volume 26, Issue 3, May-June 2015, Pages 722–734, https://doi.org/10.1093/beheco/aru238

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Sociality is associated with a variety of costs and benefits, one of which can be to increase the likelihood of individuals solving novel problems. Several hypotheses explaining why groups show higher innovative problem-solving efficiencies than individuals alone have been proposed including the sharing of antipredator vigilance and the pool-of-competence effect, whereby larger groups containing a more diverse range of individuals are more likely to contain individuals with the skills necessary to solve the particular problem at hand. Interference between group members may cause groups to have lower problem solving abilities, however. Using a simulation approach, we model the shape of the relationship between group-level problem-solving probability and group size across a range of facilitation and inhibition scenarios, various population distributions of problem solving, and a task requiring 1 action or 2 actions to be solved. Simulations showed that both sharing of antipredator vigilance and the addition of competent individuals to an existing group lead to positive relationships between group-level problem solving and group size that reach 100% solving probability, whereas interference effects generate group-solving probabilities that rise to a maximum and decrease again, generating a group size for which problem solving is maximized. In contrast, both inhibition and facilitation scenarios generate identical patterns of individual efficiencies. Our results have important implications for our ability to understand the mechanisms that underpin group-size effects on problem solving in nonhumans.

Social systems range from simple aggregations of multiple individuals to complex societies where individuals recognize and negotiate a myriad of transient and lifelong social relationships ( Lott 1991 ; Krause and Ruxton 2002 ; Zuluaga 2013 ). Quantifying the costs and benefits of each of these increasingly more complex social arrangements is central to our understanding of the factors that drive the evolution of sociality ( Giraldeau and Caraco 2000 ; Silk 2007 ). Although the benefits of group living are most typically associated with reduced predation risk ( Elgar 1989 ; Roberts 1996 ; Janson 1998 ; Beauchamp 2010 ), sociality may also benefit individuals by allowing them access to the knowledge and skills of others ( Götmark et al. 1986 ; Galef and Giraldeau 2001 ; Griffin 2004 ). For example, animals that live in groups may use opportunistically the information produced by other, more knowledgeable individuals to detect and/or learn about novel resources ( Galef 1992 ; Reader and Laland 2000 ; Allen and Clarke 2005 ; Aplin et al. 2013 ). More coordinated social interactions may allow individuals to access larger or faster-moving prey, which are difficult to capture alone ( Creel 1995 ; Boesch 2002 ; Hayward and Kerley 2005 ; Lührs et al. 2012 ).

Accessing the skills and knowledge of others may also be beneficial when animals are faced with a novel problem ( Melis et al. 2006 ; Couzin 2009 ; Krause et al. 2010 ; Péron et al. 2011 ; Bräuer et al. 2013 ). For example, sociality may improve problem solving if individuals differ in their personalities, skills, and/or past experiences, such that they vary in their ability to solve particular problems ( Krause et al. 2011 ). Groups containing a more diverse range of individuals should be more likely to contain a problem solver with the knowledge and skills necessary to solve the problem at hand ( Hong and Page 2004 ; Burns and Dyer 2008 ). Once a knowledgeable individual has solved, its behavior becomes available to other individuals to copy. Group-size effects on problem-solving efficiency have long been known in humans ( Hastie 1986 ; Laughlin et al. 2006 ). For example, groups of 3 individuals outperform an equivalent number of single individuals attempting to solve a problem alone or in pairs ( Laughlin et al. 2006 ). Moreover, groups containing individuals with divergent skills have been found to outperform groups of high-performing individuals, suggesting that divergent humans interact synergistically to develop solutions more readily ( Hong and Page 2004 ; Laughlin et al. 2006 ).

Although research investigating the relationship between problem-solving efficiency and group size has yielded mixed results ( Liker and Bókony 2009 ; Overington et al. 2009 ; Morand-Ferron and Quinn 2011 ; Griffin et al. 2013 ), 2 studies in recent years have revealed that nonhuman animals may also show an increase in problem-solving efficiency with increased group size ( Liker and Bókony 2009 ; Morand-Ferron and Quinn 2011 ). In nonhuman animals, solving of novel problems, referred to as innovative problem solving, is most often operationalized by measuring an individual’s success in solving, or latency to solve, an extractive foraging task consisting of some kind of container that must be opened to access food ( Cole et al. 2011 ; Benson-Amram and Holekamp 2012 ; Thornton and Samson 2012 ; Griffin et al. 2013 , 2014 ; for a review, see Griffin and Guez 2014 ). Liker and Bókony (2009) measured the ability of wild-caught, captive-held house sparrows ( Passer domesticus ) to access 3.5-cm diameter wells containing food. Each well was covered with a plastic lid that needed to be removed to access the food. The proportion of individual birds that were successful in accessing a well was significantly larger in groups of 6 birds than in groups of 2. In addition, the per capita number of wells opened was significantly higher in the larger groups. In the second study, Morand-Ferron and Quinn (2011) measured the ability of free-ranging mixed species flocks of Passerines containing blue tits ( Cyanistes caeruleus ) and great tits ( Parus major ) to solve arrays of 6 lever-pulling devices. Two levers needed to be pulled sequentially, in any order, for the food to be released. Results showed that the proportion of devices solved by the group increased with increasing group size. Both studies concluded that increased group diversity offered the most likely explanation for the positive relationship between problem-solving efficiency and group size ( Liker and Bókony 2009 ; Morand-Ferron and Quinn 2011 ). Larger groups contained a more diverse range of individuals making them “more likely to contain individuals with specific skills, individual tendencies, or past experience, making them competent at solving the current problem,” a group-size effect on problem solving coined the “pool-of-competence” effect ( Morand-Ferron and Quinn 2011 ).

However, a prerequisite to establishing a pool-of-competence effect is to eliminate other potential alternative explanations for why problem-solving efficiency may increase in larger groups. First, if the presence of other individuals allows for antipredator vigilance to be shared, as has been found in the context of foraging behavior ( Elgar 1989 ; Beauchamp and Livoreil 1997; Beauchamp 1998; Lima and Bednekoff 1999; Beauchamp and Ruxton 2003; Bednekoff and Lima 2004 ), then problem-solving efficiency should increase in the presence of other individuals. Second, if the presence of other individuals socially facilitates approach and exploration, either via reduced neophobia or increased scramble competition, then individuals should also have higher solving probabilities in the presence of others (Coleman and Mellgren 1994; Visalberghi et al. 1998; Visalberghi and Addessi 2000).

Liker and Bókony (2009) ruled out that increased per capita problem-solving success in house sparrows was attributable to shared vigilance by showing that individual scan rates did not differ significantly among small and large groups. The authors also ruled out a mediating role of neophobia, exploration, and scramble competition by showing that neither individual latency to approach the problem-solving task nor the per capita attempt rates differed between 2-bird and 6-bird groups. Consequently, the possibility that larger groups were more likely to contain a problem solver and that the presence of a problem solver increased the solving rates of other members of the group remains a possible but untested explanation for the positive relationship between problem-solving efficiency and group size in this study ( Liker and Bókony 2009 ).

Morand-Ferron and Quinn (2011) proposed an alterative approach to disentangling a pool-of-competence effect from other facilitation effects. The crux of their argument was that antipredator benefits should diminish as group size becomes larger, whereas the pool-of-competence effect should lead to a linear increase of problem-solving efficiency with group size. This argument hinges on the finding that the antipredator benefits of group size in birds have been found to level off at intermediate group sizes ( Fernández-Juricic et al. 2007 ; Cresswell and Quinn 2011 ). Consequently, if antipredator benefits underpin the positive relationship between problem-solving efficiency and group size, then the relationship should also level off. In contrast, under the assumption that larger pools of individuals become increasingly more likely to contain problem solvers, a pool-of-competence effect should lead to problem-solving efficiency increasing linearly with group size.

Increases in group size may not always be associated with benefits. If problem solving is vulnerable to interference competition, from kleptoparasitism for example, then the frequency of problem solving should decrease in the presence of other individuals when compared with solitary conditions as has been found for food processing behaviors ( Overington et al. 2009 ). If problem solving is vulnerable to a “negotiation over risk,” then problem solving may also be reduced in larger groups ( Stöwe et al. 2006 ; Griffin et al. 2013 ).

Although there has been a substantial effort to model the effects of group size on foraging behavior in nonhuman animals ( Shaw et al. 1995 ; Bednekoff and Lima 1998a , 1998b ; Giraldeau and Caraco 2000 ; Bednekoff and Lima 2004 ), to our knowledge, there have been no previous attempts to model the effects of group size on innovative problem solving. Here, we use a theoretical modeling approach to simulate how the likelihood of a group solving a hypothetical 1-action extractive foraging task varies with group size under different facilitation and inhibitory scenarios. We simulated the effects of group size attributable to adding additional competent individuals to an existing group by assuming that the individual problem-solving probability remained stable as group size increased. Group-size benefits attributable to shared vigilance were modeled by increasing individual problem-solving probabilities each time an additional individual was added to the group, whereas inhibitory effects of group size on group problem solving were modeled by decreasing individual problem-solving probabilities each time an individual was added to the group. Our aim was to evaluate to what extent the relationship between problem-solving efficiency and group size varied in shape across a range of facilitation and inhibition scenarios, allowing for different processes to be identified. Current analyses of problem-solving ability have revealed both within-individual stability in problem-solving propensity and contextual variability ( Laland and Reader 1999 ; Reader 2003 ; Cole et al. 2011 ; Morand-Ferron et al. 2011 ; Griffin et al. 2013 ). Consequently, we modeled the case in which problem solving is assumed to be a stable individual trait, and populations contain both problem solvers and nonproblem solvers. We also modeled the case in which all individuals within a population have a low but equal probability of solving. Finally, we modeled the case in which individuals express specialized problem-solving abilities, and the solving skill of one individual is complementary to the solving skill of another individual.

We considered theoretical populations of animals with varying distributions of problem-solving propensity. We then simulated drawing random samples of individuals without repetition from these populations and calculated a solving probability for each sample. We varied the group size of each sample from 1 to 100 individuals, and for each group size, we calculated an average solving probability by averaging the solving probabilities of 50 independent sampling events.

Consistent with most recent studies on problem solving in nonhuman animals, which operationalize problem solving by measuring their success in opening a container to access food ( Benson-Amram and Holekamp 2012 ; Cole and Quinn 2012 ; Sol et al. 2012 ; Thornton and Samson 2012 ; Bókony et al. 2014 ), we varied the individual probability of solving a hypothetical extractive foraging task. We elected to model separately the effects of solving a 1-action task or a 2-action task. In a 1-action task, animals only need to perform 1 motor action to solve the task, whereas in the 2-action task, animals need to perform 2 motor actions. The 2-action task allowed us to model a scenario in which individuals specialized in 1 kind of motor action interact with another individual specialized a different motor action to solve a given problem. Two-action tasks have recently been proposed to provide a useful assay for measuring innovative problem-solving abilities in nonhumans ( Auersperg et al. 2012 ), so we considered it important for future research to model group-size effects on this type of task.

Theoretical populations

We considered 3 theoretical populations of 1000 individuals each. Each population had a distinct distribution of problem-solving propensity. The first theoretical population had a binomial distribution of problem-solving propensity. The solving probability of problem solvers was fixed at 0.1, whereas that of nonproblem solvers was fixed at 0.01. The frequency of problem solvers within the population was fixed at 10%. The second theoretical population had a bell-like distribution of problem-solving propensity. The population was generated by assuming a beta continuous probability distribution between 0 and 1. The parameters used to generate the population were α = 5 and β = 5. The final population is represented in Figure 1 , panel A. In our third theoretical population, we created a skewed distribution of problem-solving propensity. Once again, the population was generated by assuming a beta continuous probability distribution between 0 and 1, but this time, we assumed that problem solvers were much more rare within the population than nonproblem solvers. The parameters used to generate the population were α = 1 and β = 5. The final population is represented in Figure 1 , panel B. To explore to what extent our results were sensitive to variation in these particular population parameters, we created populations with other parameter sets and repeated our simulations (see below). The distributions of problem-solving propensity obtained using alternative parameter sets are presented in Supplementary Figures S1 and S5 .

Histogram of the distribution of problem-solving propensity within a range of different populations for a 1-action task (A and B) and a 2-action task (C and D). (A) Bell-like probability distribution of problem-solving ability for a 1-action task (beta distribution with α = 5 and β = 5). (B) Skewed distribution of problem-solving ability for a 1-action task (beta distribution with α = 1 and β = 5). (C) Bell-like probability distribution of problem-solving ability for a 2-action task (beta distribution with α = 30 and β = 30). (D) Skewed distribution of problem-solving ability for a 2-action task (beta distribution with α = 0.5 and β = 5).

Simulations

In a first series of simulations, we considered that our random sample of individuals was presented with a 1-action task that required only 1 motor action to be solved. This simulation was done under 3 different theoretical conditions. The first and simplest assumed that group size did not affect individual solving probability. In the second, we assumed that individual problem-solving probability increased with group size uniformly by a small amount. Under natural conditions, this effect would arise if individuals in groups shared antipredator vigilance and could allocate more attention to solving the task at hand. The third theoretical condition assumed that individual problem-solving abilities decreased uniformly by a small amount with group size. An example of a natural correlate of decreasing individual problem-solving probability would be that associated with an increased risk of interference competition (e.g., kleptoparasitism; intragroup aggression) with increasing group size. Increased density of conspecifics is known to be associated with reduced expression of behaviors prone to kleptoparasitism (i.e., food dunking; Morand-Ferron et al. 2004 ). Hence, we considered it reasonable to assume that individual solving probabilities may decrease with increasing group size because individuals would similarly withhold from problem solving.

In a second series of simulations, we considered that our random sample of individuals was presented with a 2-action task that could only be solved using 2 independent motor actions in any order ( Auersperg et al. 2012 ). In these simulations, we considered that each individual within our theoretical population had a different probability of performing each action. Because 2-action tasks involve 2 different actions, and not a repeat of the same action, and that empirical work has demonstrated that solving rates can vary across different kinds of tasks (e.g., Bókony et al. 2014 ), we considered it more realistic to assume that the probabilities of solving would be different for different actions. The probability distribution of the first action conformed to that described for the 1-action task described above. The probability distribution of the second motor action among the theoretical population with a bell-like distribution of problem-solving propensity is depicted in Figure 1 , panel C (beta distribution parameter α = 30 and β = 30), whereas the probability distribution of the second motor action within the theoretical population with skewed distribution of problem-solving propensity is depicted in Figure 1 , panel D (beta distribution parameter α = 0.5 and β = 5). In the case of the theoretical population with a binomial distribution of problem-solving propensity, the frequency of problem solvers within the population was still 10%, but problem solvers had a solving probability of 0.05, whereas nonproblem solvers had a solving probability of 0.005. Under these conditions, a given individual could have a high or low probability of solving via the first motor action, whereas having a high or low probability of solving via the second motor action. In other words, there was no link between the probability of using one action and the probability of using the other action. In the case of the 1-action task, the probability of the group solving was calculated by summing individual solving probabilities because the task could be solved by one group member or another. In the case of a 2-action task, the probability of the group solving was calculated as the product of the sum of the individual probabilities of solving each task. This is because solving the task required that both actions be performed in any order; however, either individual could perform either action. So, if Pa1 and Pa2 are the probabilities of solving using action A of individuals 1 and 2, respectively, and Pb1 and Pb2 are the probabilities of solving using action B for individuals 1 and 2, respectively, then the probability of solving the 2-action task can be calculated as P = (Pa1 + Pa2) × (Pb1 + Pb2).

In addition to the 3 theoretical conditions described above (stable, increasing, and decreasing individual problem-solving probabilities), we modeled group-level problem-solving probability under scenarios where the benefits (or costs) gained by (or imposed on) each additional individual changed exponentially (rather than being a uniform increase or a uniform decrease). First, we assumed that the problem-solving benefit decreased exponentially with each additional individual. Under natural conditions, this would arise if individuals in groups share vigilance, but these individual-level benefits level off beyond certain group sizes ( Elgar 1989 ; Beauchamp and Livoreil 1997; Beauchamp 1998; Lima and Bednekoff 1999; Beauchamp and Ruxton 2003; Bednekoff and Lima 2004 ). Second, we assumed that the problem-solving cost increased exponentially with each individual added to the group, as would arise if the probability of interference competition, such as kleptoparasitism, intragroup aggression, or vigilance toward competitors, increased with group size. Increasing penalties could arise because the probability of a thief being present becomes higher or because the number of individuals available to steal from becomes higher. For the sake of completeness, we also modeled an exponentially increasing benefit and an exponentially decreasing cost, even though we do not think that these conditions have any biological equivalent. As these simulations did not change our general conclusions, we provide the outcomes of these simulations in the supplementary materials (for exponentially decreasing benefits and costs, see Supplementary Figures S9–S11 ; for exponentially increasing benefits and costs, see Supplementary Figures S12–S14 ).

Finally, past empirical work has quantified group-size effects on problem solving either by measuring performance of increasingly large groups (e.g., proportion of devices solved by the group; Morand-Ferron and Quinn 2011 ) or by calculating a per capita solving performance by dividing each group’s performance by the number of group members (e.g., Liker and Bókony 2009 ; Griffin et al. 2013 ). Per capita solving measures (e.g., number of tasks solved per individual or number of tasks solved per individual per unit time) allow for the solving performances of groups of different sizes to be compared and are therefore taken to provide a measure of group efficiency ( Morand-Ferron and Quinn 2011 ). Given that they are calculated at the individual level, however, we refer to them here as “individual efficiencies.” In order to ensure that the outcomes of our models could be compared with empirical data sets, we modeled the effects of increasing group size not only on group-level solving probability but also on individual efficiency for a subset of our theoretical populations and simulation scenarios. Per capita solving performances were calculated for simulations involving binomial, bell-like, and skewed populations and uniformly increasing and decreasing individual solving probabilities, exponentially decreasing costs and benefits, and exponentially increasing costs and benefits for both a 1-action task and a 2-action task. Consistent with the literature where group efficiencies are calculated by dividing a group-level performance measure by the number of individuals in the group, per capita solving performances were calculated by dividing group-level solving probabilities by the number of individuals within the group at each stepwise increase in group size.

All simulations were run using Scilab 5.4.0 software for numerical computation available at www.scilab.org .

One-action task

In this series of simulations, we evaluated the solving probability of an extractive foraging task assuming 3 possible theoretical populations differing in the distribution of their problem-solving propensities (binomial; bell-like, Figure 1A ; skewed, Figure 1B ). We simulated the effect of group size on the probability of solving assuming 1) no variation of individual solving probabilities as a function of group size, 2) a uniformly increased probability of individual solving propensity with group size, and 3) a uniformly decreased probability of individual solving propensity with group size.

Assuming no change in individual solving probability as a function of group size, the probability of solving a 1-action task increased steadily with group size and reached 1 regardless of the distribution of problem-solving propensity within the population ( Figure 2 , left column). This pattern of results did not change when we assumed that the probability of individual problem solving increased with increasing group size, as would be the case if individuals shared antipredator vigilance. Specifically, in a scenario where individuals became slightly more likely to solve each time group size increased, the probability of solving a 1-action task increased steadily with group size and reached 1 regardless of the distribution of problem-solving propensity within the population ( Figure 2 , ✭ symbols). The larger the individual gain associated by increased group size (0.01, 0.005, or 0.0005 for the binomial distribution; 0.01, 0.005, or 0.015 for the bell-like or skewed distributions), the faster the positive relationship between group solving probability and group size increased and reached 1 ( Figure 2 ).

Average solving probability of a hypothetical 1-action problem-solving task as a function of group size in scenarios where individual solving probabilities remained constant (▼ symbols in left column), increased (✭; e.g., via shared antipredator vigilance), or decreased (○ symbols; e.g., via interference competition) with increasing group size. Solving probabilities were increased or decreased by a factor of 0.01, 0.005, and 0.0005 (binomial) or 0.01, 0.005, and 0.015 (bell-like and skewed). For each group size, averages were calculated across 50 independent samples drawn with no repetition from populations with a binomial Figure 1 panel (A), bell “like” Figure 1 panel (B), or skewed Figure 1 panel (C) distributions of problem-solving propensities.

Calculating individual efficiencies at each stepwise increase in group size assuming that individual problem-solving propensity remained stable (i.e., pool-of-competence effect) or increased (e.g., shared vigilance) with increased group size revealed that per capita solving performance consistently increased to a maximum and decreased again as group size increased, and this regardless of the distribution of problem solving within the original population ( Figure 3 , different rows), but also regardless of whether individual solving probability remained stable ( Figure 3 , left column) or increased steadily with group size by any given amount ( Figure 3 , ✭ symbols, different columns).

Individual solving efficiency of a hypothetical 1-action problem-solving task as a function of group size in scenarios where individual solving probabilities remained constant (▼ symbols in left column, e.g., via a pool-of-competence effect), increased (✭ symbols; e.g., via shared antipredator vigilance), or decreased (○ symbols; e.g., via interference competition) with increasing group size. Solving probabilities were increased or decreased by a factor of 0.01, 0.005, and 0.0005 (binomial) or 0.01, 0.005, and 0.015 (bell-like and skewed). For each group size, averages were calculated across 50 independent samples drawn with no repetition from populations with a binomial Figure 1 panel (A), bell-like Figure 1 panel (B), or skewed Figure 1 panel (C) distributions of problem-solving propensities in.

The shape of the relationship between group-solving probability and group size completely changed when we assumed that individual problem-solving propensity decreased with increased group size, as would be the case if the probability of interference competition increased with increasing group size. Under this assumption, group-solving probability increased and then decreased again either side of a group size whereby group-solving probability was maximized ( Figure 2 , ○ symbols). Considered with regard to problem solving, this “optimal” group size varied from 3 to around 40 individuals, depending on the distribution of individual problem-solving probabilities within the population and the amplitude of the reduction in individual problem-solving probability with increasing group size ( Figure 2 , ○ symbols).

In a case where individual problem-solving probability followed a bell-like distribution, group-solving probability reached 100% rapidly with increased group size ( Figure 2 , middle row, ○ symbols) before decreasing sharply when group size increased further. In contrast, in cases where the distribution of individual problem-solving propensity followed a binomial or skewed distribution within the population, maximum group-solving probability was clearly maximized for an optimal group size, but never reached 100%. Assuming a binomial distribution of individual solving probability within the population, and a 0.01 penalty for each additional individual in the group, the group size for which problem solving was maximized was 3–4 and the maximum solving probability reached approximately 5% ( Figure 2 , first row, second column). With a smaller penalty of 0.005 for each additional individual in the group, optimal group size increased to 4 and maximum solving probability to approximately 6% ( Figure 2 , first row, third column). Finally, with an even smaller penalty of 0.0005 for each additional individual in the group, optimal group size increased to between 35 and 40 and maximum solving probability to approximately 40% ( Figure 2 , first row, fourth column).

Assuming that the distribution of problem-solving propensity was skewed within the population, and a 0.005 penalty for each additional individual within the group, the optimal group size was around 18 individuals, and the maximum solving probability fell just short of 100% ( Figure 2 , third row, third column). With a larger penalty of 0.01 per individual added to the group ( Figure 2 , first row, second column), optimal group size decreased to around 12, and the maximum solving probability decreased to approximately 85%. With an even larger penalty of 0.015 ( Figure 2 , third row, fourth column), optimal group size decreased even further to approximately 9, and the maximum solving probability decreased to around 75%.

In sum, within the range of group sizes explored here, the simulations with decreasing individual problem-solving probabilities with increasing group sizes showed that both optimum group size and maximum solving probability changed when the penalty on individual problem solving changed, whether the distribution of problem-solving propensity was binomial ( Figure 2 , first row) or skewed ( Figure 2 , third row) within the population. In contrast, when the distribution of problem-solving propensity within the population followed a bell-like distribution ( Figure 2 , second row), only the optimal group size decreased with increasing penalties for adding additional individuals to the group.

Calculating individual efficiencies at each stepwise increase in group size in scenarios where we assumed that individual problem-solving propensity decreased with increased group size (i.e., interference competition) revealed that per capita solving performance consistently increased to a maximum and decreased again, and this regardless of the distribution of problem solving within the original population ( Figure 3 , different rows) and regardless of the amplitude of the individual penalty associated with increased group size ( Figure 3 , ○ symbols, different columns).

The patterns of group solving probability and individual efficiencies described above remained unchanged when we used alternative parameter sets to describe the distribution of problem solving within the initial populations (see Supplementary Figures S2 and S3 and S6 and S7 ). Our conclusions also remained unchanged when we repeated the simulations assuming that the benefit (or cost) associated with each additional individual decreased (or increased) exponentially as group size increased, as would be the case if individual antipredator vigilance benefits leveled off or if the probability of interference competition (e.g., kleptoparasitism; intraspecific aggression) became gradually higher, with increasing group sizes. These simulations are presented in Supplementary Figures S9–S14 .

Two-action task

The simulations considering that individuals were presented with a 2-action task revealed patterns of problem-solving probabilities that were strikingly similar to those revealed when considering that individuals were presented with a 1-action task. Regardless of the distribution of problem-solving propensity within the population, the probability of group-solving increased with increasing group size, and reached one, whether we assumed that individual problem-solving probability remained constant as additional individuals were added to the group ( Figure 4 , first column) or that individual problem-solving probability increased as additional individuals were added to the group ( Figure 4 , ✭ blue symbols). The only effect of increasing individual problem-solving probabilities as group size increased rather than maintaining them stable was to make the already positive relationship steeper. This effect was most visible when the distribution of problem-solving propensity within the population followed a binomial distribution ( Figure 4 , first row).

Average solving probability of a hypothetical 2-action problem-solving task as a function of group size in scenarios where individual solving probabilities remained constant (black ○ symbols in left column; e.g., via a pool-of-competence effect), increased (✭ blue symbols; e.g., via shared antipredator vigilance), or decreased (○ magenta symbols; e.g., via interference competition) with increasing group size. Solving probabilities were increased or decreased by a factor of 0.01, 0.005, and 0.0005 (binomial) or 0.01, 0.005, and 0.015 (bell-like and skewed). For each group size, averages were calculated as indicated in the legend of Figure 3 .

As for the 1-action task, when we assumed that individual problem-solving propensity decreased with additional individuals added to the group, the positive relationship between group problem solving and group size changed from a positive relationship to one with an optimal group size, for which group problem solving was maximized ( Figure 4 , ○ magenta symbols). The effects of decreasing individual solving probabilities with increasing group size were particularly dramatic if individual problem-solving propensity within the population for a 2-action task followed a binomial distribution. In this case, group-solving probability only rose substantially above 0 if the penalty of adding additional individuals was extremely low. For example, assuming a penalty of 0.0005, the maximal group solving probability plateaued at 0.025, with an optimal group size of around 23 individuals ( Figure 4 , fourth column).

In contrast, assuming that the distribution of individual problem-solving ability within the population followed a bell-like distribution, the requirement of a second motor action to solving generated relationships between solving probabilities and group size that are very similar to those generated using a 1-action task ( Figure 4 , second row, columns 2–4 vs. Figure 2 , second row).

Finally, in the case where the distribution of individual problem-solving ability followed a skewed distribution, introducing the requirement of a second motor action to solve the extractive foraging task decreased not only the optimum group size but also the maximum group-solving probabilities for each group size ( Figure 4 , third row, columns 2–4 vs. Figure 2 , third row).

Calculating individual efficiencies at each stepwise increase in group size when we assumed that individual problem-solving propensity for a 2-action task remained stable, increased uniformly, or decreased uniformly showed the same pattern as for a 1-action task. Per capita solving performance consistently rose to a peak and then decreased again, and this regardless of the distribution of problem solving within the original population ( Supplementary Figure S15 , different rows) and regardless of the amplitude of the individual penalty ( Supplementary Figure S15 , magenta symbols) or gain ( Supplementary Figure S15 , blue symbols) associated with increased group size ( Supplementary Figure S15 , different columns). Assuming that penalties/gains associated with increased group size increased/decreased exponentially, rather than uniformly, as a function of group size for populations with a skewed distribution of problem solving for a 1-action task did not change the shape of the relationship between individual efficiency and group size ( Supplementary Figure S16 ).

Using alternative parameter sets to describe the distribution of problem solving for 2-action tasks within the initial populations did not change the overall pattern of our results ( Supplementary Figures S4 and S7 ), nor did modeling group problem solving probability assuming exponentially increasing and decreasing costs and benefits ( Supplementary Figures S9–S14 ).

It has been suggested that increasing problem-solving efficiencies with increasing group sizes in nonhuman animals is mediated by a pool-of-competence effect. This is the idea that as group size increases, and with it, the diversity of individuals within the group, the presence of a problem solver with the skills suited to solving the particular task at hand becomes more likely, such that larger groups have a higher probability of problem solving than smaller groups ( Liker and Bókony 2009 ; Morand-Ferron and Quinn 2011 ). An alternative reason why performance may increase with increasing numbers of individuals is that each member is able to allocate less time to antipredator vigilance and hence more time to solving the task. In order to determine whether these mechanisms could be disentangled, we modeled the pool-of-competence effect by drawing individuals from a pool of competent and noncompetent individuals with varying distributions of problem-solving probability and adding them to a group without changing their individual problem-solving probabilities. We modeled antipredator benefits by drawing individuals from a pool of competent and noncompetent individuals once again, but this time increasing their problem-solving probabilities as they were added to the group. We found that regardless of the theoretical distribution of problem-solving propensity within the population, and regardless of whether individual problem-solving ability was maintained constant or increased as groups became larger, the relationship between group size and group problem–solving probability was consistently positive and rose to 1, with the only difference that increasing individual problem-solving probabilities at each stepwise increase in group size caused a steeper positive relationship. In addition, calculating per capita solving performance based on group solving probabilities revealed that regardless of the distribution of problem-solving ability within the population and regardless of whether individual problem-solving ability was maintained constant or increased as groups became larger, the relationship between group size and individual efficiency rose to a maximum and then dropped off again as group performance reached 1. These findings did not change when we considered an extractive foraging task that required 2 motor actions to be solved and in which individuals with different sets of skills could cooperate to solve the problem ( Péron et al. 2011 ). To examine what form the relationship between group size and solving probability would take in a case where group members interfered with each other as the group became larger, we simulated scenarios in which adding more individuals to a group decreased the probability of each individual solving. In this case, the likelihood of solving by the group increased to an optimal group size and then decreased again. These findings indicate that the shape of the relationship between group size and group problem solving can be used to distinguish competitive interference from group size–associated benefits. However, within the latter, a pool-of-competence effect cannot be disambiguated from shared antipredator vigilance benefit.

One might argue that individuals could benefit at first from being with others, but that those benefits may plateau as group size continues to increase, as has been found in the foraging context ( Fernández-Juricic et al. 2007 ; Cresswell and Quinn 2011 ). However, explicitly modeling this particular scenario by decreasing the antipredator benefit exponentially each time an individual was added to the group did not change the general pattern of our results. The relationship between group size and group problem–solving probability remained positive, gradually increasing to 1 ( Supplementary Figures S9–S14 ). Intuitively, this consistent increase occurs because even though individual benefits become gradually smaller, adding additional, potentially competent, individuals to the group continues to increase the likelihood of the group solving. In an alternative scenario, increased group size may be beneficial at first, but then become detrimental. For example, in humans, problem-solving performance increases up to groups with 3 members to above those levels exhibited by an equivalent number of individuals alone, and then stabilizes for groups of 4 and 5, an effect attributed to interference between group members ( Laughlin et al. 2006 ). The present results lead to the prediction that switching from benefits to costs at a specific group size would yield a group solving probability that increases at first and then decreases again. It is also important to note that an assumption of all our simulations was that all individuals in a group, independent of its size, had access to the problem to be solved. Failure to meet this assumption would be equivalent to drawing individuals solely from the pool of nonsolvers once the problem became inaccessible. This is the only scenario that produces a leveling off of group problem solving probability as group size increases beyond a certain upper limit.

Past empirical work measuring problem-solving performance of groups has quantified group-size effects using 2 distinct approaches ( Figure 5 ). The first quantifies performance of groups with increasing numbers of members (e.g., proportion of devices solved by the group; Morand-Ferron and Quinn 2011 ), whereas the second quantifies group performance and then calculates a per capita solving performance (e.g., Liker and Bókony 2009 ; Griffin et al. 2013 ). Per capita solving rates, which might be considered a measure of “group efficiency,” are expressed either as a number (or percentage) of problems solved per individual (e.g., Liker and Bókony 2009 ; Griffin et al. 2013 ) or a number (or percentage) of problems solved per individual per unit time ( Laughlin et al. 2006 ) and allow for the performance of groups of different sizes to be compared. Systematically varying individual solving probabilities to model a pool of competency effect, shared antipredator vigilance and interference competition, simulating group-level solving performance, and then back calculating per capita solving performances at each stepwise increase in group size revealed that per capita solving performances rose to a peak and then decreased again regardless of the theoretical distribution of problem-solving propensity within the population and regardless of whether individual problem-solving ability remained constant or changed exponentially as groups became larger. These results indicate that per capita solving performances calculated from measured group-level performance data do not allow for mechanisms underpinning group-size effects to be disentangled ( Figure 5 ). This contrasts with analyses of group level performance relative to group size, which, as discussed above, can be used to distinguish group size–associated costs (e.g., competitive interference) from group size–associated benefits, but within the later, cannot disambiguate pool-of-competence from a shared antipredator vigilance effects ( Figure 5 ).

Conceptual relations between empirical and simulated approaches to studying group-size effects on problem-solving performance. Empirical group performance measures and simulated group solving probabilities are equivalent. In contrast, per capita solving performances (at times used loosely to refer to the efficiency of a group in the literature) and individual solving probabilities are only equivalent if individual solving efficiencies are measured (not shown; e.g., Overington et al. 2009 ) rather than calculated on the basis of group level performance (shown; e.g., Morand-Ferron and Quinn 2011 ). Our simulations show that calculated individual efficiencies cannot be used to disentangle any type of group size–mediating mechanism, whereas group level measures allow for group size–associated costs (i.e., interference competition) to be distinguished from group size–associated benefits, but without identifying a benefit mechanism (i.e., pool of competence vs. shared antipredator vigilance).

The findings from this theoretical analysis are at odds with the prediction that group-size benefits attributable to an increasing number of problem solvers with a diverse range of skills can be distinguished from group-size benefits attributable to shared vigilance by examining the shape of the relationship between problem-solving efficiency and group size ( Morand-Ferron and Quinn 2011 ). It has been suggested that a pool-of-competence effect on problem solving should yield a linear increase in problem-solving efficiency as group size increases, whereas a shared antipredator vigilance should result in a relationship that increases at first and then levels off because the importance of antipredator benefits decrease as group size increases ( Morand-Ferron and Quinn 2011 ). Whereas the empirical approach involves measuring group performance and then calculating group efficiencies to disentangle mechanisms underpinning group-size effects (e.g., shared vigilance), here, we used a modeling approach in which we fixed individual solving probabilities assuming specific underpinning mechanisms and modeled their effect on group performance and individual efficiencies ( Figure 5 ). Modeled at the group level, simulations of both the pool-of-competence effect and shared antipredator vigilance both produced a positive relationship between group problem solving and group size, which reached a solving probability of 100%. Considered at the individual level, a pool-of-competence effect, that is, the increasing likelihood that a group will contain a competent individual as group size increases ( Morand-Ferron and Quinn 2011 ), will be reflected by individual solving rates that remain stable as group size increases. If competent individuals facilitate problem solving in other members of the group, then the individuals that learn will show an improved solving probability, leading, at the group level, to an even steeper increase of solving probability as group size increases compared with the case where no learning occurred. In contrast, at the individual level, a shared antipredator vigilance benefit will be reflected by increasing individual solving rates as groups become larger. If this benefit is maximum for a given group size, individual solving probability will plateau at this group size. However, our simulation shows that none of these distinct patterns of individual-level effects can be detected by calculating individual efficiencies based on group-level performance. This is because both the pool-of-competence effect and shared antipredator vigilance produce calculated individual efficiencies that rise to a peak and then decrease again. In order to demonstrate an antipredator vigilance effect, excluding a pool-of-competence effect, one would need to show that individual solving performances increase in the presence of increased numbers of conspecifics that cannot interact with the problem-solving task ( Overington et al. 2009 ). That is to say that individual solving performance variables need to be measured rather than calculated on the basis of group-level performance data ( Figure 5 ).

Overall, the outcomes of the simulations presented here are consistent with the suggestion that group diversity may underpin a positive relationship between group size and group performance in nonhumans but highlight the need for detailed measurement of individual solving performance in the presence of other individuals that cannot interact with the task ( Overington et al. 2009 ) rather than calculation of per capita solving performances based on group performance to establish with certainty the role of this mechanism. Individual specializations in foraging behavior are well documented across a broad range of vertebrates and can emerge as a consequence of the formation of search images, chance events, and individual learning biases. Skill pool effects have been predicted to maintain individual specializations and therefore diversity in foraging behaviors within species ( Giraldeau 1984 ). The mere addition of individuals with different foraging strategies to a group increases the availability of demonstrators within a group and opportunities for individuals to copy the behavior of others and thereby increase their own foraging efficiencies ( Galef and Giraldeau 2001 ). Liker and Bókony (2009) found that larger groups of sparrows had neither higher proportions of birds trying to solve nor higher attempt rates per capita. However, larger groups contained significantly larger proportions of individuals that succeeded among birds that were trying, indicating a higher conversion rate from trying to succeeding. Hence, with larger numbers of competent individuals available in the group, the pool-of-competence effect, individuals motivated to try presumably copied some aspects of the solving behavior of successful individuals. Social learning of this kind has been demonstrated in pigeons ( Columba livia ) ( Palameta and Lefebvre 1985 ) and in various species of tits ( Parus sp.) and thrushes ( Turdus sp.) ( Sasvari 1985 ; Aplin et al. 2013 ).

Our simulations were based on populations with either a binomial, bell-like, or skewed distribution of problem-solving propensity. Recent analyses indicate that innovative problem-solving ability is stable across time ( Cole et al. 2011 ), tasks ( Griffin and Diquelou 2015 ), and some ( Griffin et al. 2013 ), but not other ( Cole et al. 2011 ; Sol et al. 2012 ), contexts, suggesting that innovative problem solving should be regarded as a personality trait and hence underpinned by a specific genetic makeup. On the other hand, innovative problem solving is influenced by state-dependent variables ( Laland and Reader 1999 ) and motivational factors ( Benson-Amram and Holekamp 2012 ; Thornton and Samson 2012 ; Griffin et al. 2014 ), which most likely interact with personality-dependent expression biases to determine the final probability of problem solving. Although there are currently no descriptions of the distributions of innovation propensity within natural populations, these considerations together with the general view that inventing a solution to a new problem is a rare event within wild populations ( Reader and Laland 2003 ) suggest that either a skewed or a discrete binomial distribution of the type presented here is most likely to be representative of innovative problem-solving propensity within natural populations. For example, if problem-solving propensity was determined by a single gene with 2 alleles, one of which is common and associated with low innovation propensity, the other of which is rare and associated with high innovation tendency, then one would expect innovation propensity to follow a skewed binomial distribution. If, instead, we assume that this single gene has multiple alleles each associated with a discrete problem-solving propensity, with the rare variants being associated with higher innovation propensities, one would expect that innovation propensity would be distributed following a multinomial distribution skewed toward 0. Another possibility is to consider that innovation propensity is the result of the interaction of multiple genes. In this case, that innovation propensity would be best described by a continuous skewed distribution, similar to our skewed distribution of problem solving, with each individual problem-solving propensity the result of the interaction of these genes. Regardless of which of these distributions is appropriate in a given species, the fact that inventing a solution to a new problem appears to be a rare event, which is best described at the population level by a skewed distribution of innovation propensity, suggests that there is a fitness cost to high innovation propensity. These costs may be a consequence of exposure to the risks inherent to innovating ( Greenberg 2003 ). Alternatively, the costs may be of pleiotropic origin where one or more of the genes inducing improved innovation have a detrimental effect on seemingly unrelated phenotypic traits. Detailed studies combining behavioral and population genetic approaches will be useful for future work on mechanisms of innovative problem solving and the causes of group-size effects on this behavior.

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Particularly Exciting Experiments in Psychology

March 12, 2015

Learning to Problem-Solve in Dogs and Humans

Interestingly, using dogs as subjects, Duranton and colleagues (2015, Journal of Comparative Psychology ) (PDF, 54KB) found evidence that the ability to solve problems may be distinct from the ability to learn or remember how to solve problems.

Dogs smelled a food reward, and then watched as the experimenter put the food inside a wooden box with a plastic lid. The dog was then unleashed and had a limited amount of time to solve the problem of how to open the box.

On the first trial, male dogs were significantly more successful at opening the box than female dogs. However, when only dogs that were successful on the first trial were retested, female dogs were significantly more successful than male dogs. This suggests that while male dogs were better at initially solving the problem, female dogs were better at remembering the successful strategy.

These results parallel human studies suggesting that men are better at spatial problem solving, but women are better at remembering precise object features. Future work is required to determine whether these sex differences are due to different evolutionary pressures, environmental differences during ontogeny, or hormone levels.

Unlike the dogs in Duranton et al. who were repeatedly tested with the exact same problem, when we encounter similar problems multiple times the specifics are unlikely to be identical. Therefore, learning how to solve particular kinds of problems may be more useful than learning one specific solution.

Patrick and colleagues (2015 first, Journal of Experimental Psychology: Learning, Memory, and Cognition ) (PDF, 273KB) tested a procedure for improving performance on insight problems . These problems are challenging because the initial, automatic representation of the problem is based on an incorrect assumption. Getting "stuck" on this incorrect representation prevents solvers from reaching the solution.

Participants in the training group were made aware of how habitual interpretations can block solutions to insight problems. Then, they were trained to use an inconsistency identification procedure that involves selecting one part of the problem, trying to identify inconsistencies between the problem and their interpretation and, if none are found, selecting and scrutinizing another part of the problem. After training, they were tested on a new set of problems.

Participants trained in inconsistency identification solved significantly more problems than non-trained participants who solved additional practice problems or were made aware of the reason that insight problems are difficult. This solving advantage in trained participants was still evident when testing took place 48 hours after training. Moreover, when participants were instructed to "think aloud" while solving test problems, trained participants engaged in more re-reading or paraphrasing, and questioning aspects of the problem statement than untrained participants.

Thus, training problem solvers to identify inconsistencies between the problem itself and their assumptions was successful in improving insight problem solving.

Other Interesting Reading

• Sensitivity to interference predicts difficulty memorizing arithmetic facts ( De Visscher & Noël, 2014, Journal of Experimental Psychology: General ).
• Practice does not generalize between simple non-zero addition problems, suggesting that simple addition is solved using declarative memory, not procedure-based processing ( Campbell & Beech, 2014, Journal of Experimental Psychology: Learning, Memory, and Cognition ).

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Particularly Exciting Experiments in Psychology™ (PeePs) is a free summary of ongoing research trends common to six APA journals that focus on experimental psychology.

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Journal of Experimental Psychology: Human Perception and Performance

Journal of Experimental Psychology: Learning, Memory, and Cognition

Journal of Comparative Psychology

Journal of Experimental Psychology: General

Journal of Experimental Psychology: Animal Learning and Cognition

Behavioral Neuroscience

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