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Animal Cognition
Animal Cognition L Castro and E A Wasserman, University of Iowa, Iowa City, IA, USA ã 2012 Elsevier Inc. All rights reserved.
Glossary Associative learning Making the psychological connection between contiguous events in the environment. Categorization Placing different objects or events into separate classes or categories. Conceptualization The process of forming an abstract or generic idea from particular instances. Discrimination Responding differently to two or more stimuli differing in one or more respects. Generalization Transferring a learned response from one stimulus to another. Matching-to-sample Experimental procedure for studying memory in which reward is given for responding to a stimulus if it is the same as a prior sample stimulus.
The Science of Animal Cognition For centuries, it was believed that a clear discontinuity held between the cognitive capabilities of humans and animals. Philosophers and other thinkers tended to be quite skeptical of animals’ mental abilities. For example, the great French philosopher Rene´ Descartes deemed animals to be mere machines whose actions could be explained without invoking any cognitive processes whatsoever. In his Letter to the Marquess of Newcastle, Descartes proposed that “the reason why animals do not speak as we do is not that they lack the organs but that they have no thoughts.” Approximately 50 years later, the great British philosopher John Locke, in An Essay Concerning Human Understanding, joined Descartes in disparaging the cognitive capacities of animals: “the having of general ideas is that which puts a perfect distinction betwixt man and brutes, and is an excellency which the faculties of brutes do by no means attain to.” All that changed some 200 years later when Charles Darwin advanced his revolutionary ideas about the evolution of species. In The Descent of Man, Darwin went beyond suggesting the evolution of physical traits to proposing the evolution of the mind as well. For Darwin, there was “no fundamental difference between man and the higher mammals in their mental faculties” (p. 66). Human and animal intelligence differ only in degree, not in kind, Darwin argued; so, there should be no sharp schism between human and animal mind. The human mind might be the final step in the evolution of intellectual functions, but the roots of human mental processes should be observable in animals as well. Darwin’s unsettling ideas had actually been presaged by other philosophers and psychologists. In his Principles of Psychology, Herbert Spencer suggested that the mind could be understood only by examining how it had evolved. According to Spencer, mental capacities could be represented along a continuum with no great gaps: a continuous progression
Memory Control of current behavior by past stimulation. Metacognition Thinking about one’s own cognitive states and processes. Numerical processing Set of abilities related to understanding and manipulating numbers, such as: using symbols to denote quantities; discriminating, ordering, and comparing different quantities; and, combining quantities in order to perform arithmetic operations. Reinforcer Any consequence of a behavior that increases the probability that the organism will repeat that behavior. Time-out A brief period of time during which there is no stimulation; time-out is a form of punishment that is used to decrease the probability that the organism will repeat a behavior.
from simple associative learning to complex forms of abstraction and reasoning. The ideas of Darwin and Spencer on the evolution of the mind were staunchly defended by Thomas Huxley, who wrote in 1874 that: “the doctrine of continuity is too well established for it to be permissible to suppose that any complex natural phenomenon comes into existence suddenly and without being preceded by simpler modifications; and very strong arguments would be needed to show that such complex phenomena as those of consciousness first made their appearance in man.” These principles of evolutionary biology clearly clashed with Cartesian philosophy. The gradual evolution of cognition was a direct consequence of this new biological vantage point. Critically, within that new paradigm, it was entirely plausible to believe that mental capabilities could be observed in organisms other than humans. Once the hypothesis of mental continuity was enunciated, evidence to support it was needed. Darwin himself amassed a large collection of supportive anecdotes: stories told by naturalists, zookeepers, and pet owners attesting to how smart animals really were. For example: Dr. Hayes, in his work on The Open Polar Sea, repeatedly remarks that his dogs, instead of continuing to draw the sledges in a compact body, diverged and separated when they came to thin ice, so that their weight might be more evenly distributed. This was often the first warning which the travelers received that the ice was becoming thin and dangerous.
Darwin was alert to the hazards of relying on anecdotal evidence alone; such observations lacked the information needed to pinpoint the mechanisms responsible for the animals’ behavior. Relevant to the prior example, Darwin wondered about the origins of the dogs’ behavior: “now, did the dogs act thus from the experience of each individual, or from the example of the older and wiser dogs, or from an inherited habit, that is from instinct?”
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Any bona fide scientific analysis of animal cognition requires careful recording of the relevant behaviors as well as precise knowledge and control of the variables influencing or determining that behavior. Darwin’s disciple, George Romanes, advocated for an objective analysis of the mind in his book Animal Intelligence: “We can only infer the existence and the nature of thoughts and feelings from the activities of the organisms which appear to exhibit them.” (p. 1). Yet, despite his advocacy for and his interest in documenting mental continuity across species, Romanes’ work too was simply a compilation, albeit extensive and thorough, of still more informal observations and anecdotes. The true transition to the scientific study of animal cognition began with C. Lloyd Morgan. In his Introduction to Comparative Psychology, Morgan emphasized the importance of distinguishing between an animal’s behavior and the interpretation of that behavior. In order to properly interpret an organism’s behavior, high standards of objectivity are needed. As well, systematic experimental studies are required in order to reach incisive conclusions as to the origins of behavior, which he himself undertook with baby chicks whose developmental history could be known and carefully controlled. Finally, circumspection was to be one of Morgan’s enduring contributions. Morgan’s famous canon urged researchers to be cautious in advocating advanced cognitive interpretations when the behavior in question might be more parsimoniously explained by simpler behavioral processes. These then were the forebears of the science of comparative cognition. Following the methods of natural science, research in comparative cognition today seeks to discover the extent to which our mental capacities are similar to those of other animals and to answer many longstanding questions. Are animals intelligent? Is human intelligence superior to animal intelligence? What are the cognitive and neurobiological mechanisms of intelligent action? In this article, we will consider a few particularly prominent and promising areas of research in animal cognition: numerical processing, memory and planning, conceptualization, and metacognition. This sampling of topics, issues, methods, and findings should provide readers with a good taste of the current state of the science of animal cognition. A broader compilation of animal cognition studies can be found in Comparative Cognition edited by Wasserman and Zentall.
Numerical Processing Number is an important property of objects in the external world. Critically, numerical processing can be considered to be mediated by an abstract cognitive process, because behavioral control by number must be made irrespective of the specific physical features of the items to be evaluated; 2, 5, or 8 items are 2, 5, or 8 regardless of whether the items are apples, pebbles, or geckos. Traditionally, it has been believed that linguistic competence is necessary for numerical processing. Although it is the case that a verbal system does mediate several numerical and mathematical operations, considerable evidence has been obtained documenting numerical processing by animals and infants lacking language; this evidence suggests
that common preverbal processes underlie numerical and mathematical competencies in humans and animals. Basic numerical abilities comprise: learning and using symbols to denote specific quantities; discriminating, ordering, and comparing different quantities; and, combining quantities so that basic arithmetic operations, such as addition and subtraction, can be accomplished. The Japanese primatologist Tetsuro Matsuzawa taught a 5-year-old chimpanzee, Ai, to use Arabic numerals to denote the number of items in a display. Ai was first presented with either 1 or 2 items in a display window and then required to choose either the ‘1’ or the ‘2’ key on a numeric keyboard. As Ai mastered the lower numbers, higher numbers of items were progressively introduced, up to a maximum of 6. Regardless of the items being colors, objects, or symbols, Ai’s accuracy exceeded 90%. Nevertheless, Ai had greater difficulty reporting the numbers when they were close to one another (e.g., it was easier for Ai to distinguish 2 from 5 than to distinguish 2 from 3) and when the numbers were at the higher end of the scale (e.g., Ai was more accurate at identifying 2 and 3 than at identifying 5 and 6, even when the disparity between them was in both cases 1 item). It seems that, as quantities increase, the discrepancies between them become less obvious and, in turn, the discrimination between them becomes more challenging. Interestingly, Ai’s behavior is not an isolated curiosity. Humans too find it easier to distinguish between 2 and 14 items than between 4 and 5 items. This finding is called the numerical distance effect. As well, humans can rapidly distinguish between 2 and 3 items, but we cannot so rapidly say if there are 14 or 15 items, in this case showing what is called the numerical magnitude effect. The numerical magnitude effect has also been found in monkeys, rats, and pigeons. Recent research has found that rhesus monkeys can learn to choose the smaller of two simultaneously presented sets of items when such nonnumerical factors as area and density are controlled. Monkeys can also choose the larger of two quantities even when the items are sequentially presented, as when the items fall one by one into an opaque container. In addition, Brannon and her colleagues have found that monkeys’ accuracy rises as the disparity between the amounts to be evaluated increases. Indeed, the monkeys’ performance is affected by the ratio of the values being compared. As predicted by Weber’s law, the larger the amounts, the greater the difference between them must be in order to be distinguishable. This relationship was true for human college students as well. These and other observations have led researchers to propose that humans and animals share a system that calculates inexact, but approximate representations of quantity. For example, when having to choose among different waiting lines, we tend to select the shortest. We rarely do so by precisely counting the people in line, but rather do so by a process of rough estimation; we use an approximate preverbal number system. It is not difficult to imagine that animals in the wild benefit from the ability to distinguish between trees containing 5 and 15 fruits or predators attacking in groups of 2 or 4. Small or large quantities can surely make a difference and numerosity judgments in these situations seem to be similarly performed by verbal and nonverbal organisms.
Animal Cognition
Estimating and representing numerical values appears to be necessary for the next step in numerical reasoning: interrelating quantities so that operations like adding, subtracting, multiplying, and dividing can be accomplished. Can animals perform these basic arithmetic operations? Studies using a preferential looking paradigm have suggested that preverbal human infants and monkeys can understand simple arithmetical operations, such as adding and subtracting a small number of visually presented objects. For example, when monkeys watched as two eggplants were placed behind a screen, they looked longer when the screen was removed and only one eggplant was present than when two eggplants were present. Monkeys may have spent more time looking at the incorrect outcome because it surprisingly violated the rules of arithmetic. But, the results of such studies may be explained in terms of the infants’ and monkeys’ understanding of object permanence: organisms can keep track of occluded objects and when one object unexpectedly appears or disappears, they are surprised; no real arithmetic ability is required in that case. More compelling evidence of addition in monkeys has been found by Brannon and Cantlon. They presented animals with two sets of dots on two sequential screens. Then, on a third screen, the monkeys had to choose from two sets: one containing the number of dots equal to the sum of the two sets and a second containing a different number of dots (see Figure 1). Monkeys learned to choose the array that roughly corresponded to the arithmetic sum of the two sets of dots and they readily transferred this behavior to novel combinations. Some problems proved to be more difficult than others. For example, monkeys were faster and more accurate when, after being presented with a set of 2 dots and a second set of 6 dots, they had to choose between 8 and 14 dots compared to when they had to choose between 8 and 10 dots. The closer the two possible answers, the more difficult the task. When humans were given the same nonverbal task, their pattern of performance was strikingly similar. Although humans were generally more proficient than monkeys, both
Figure 1 A monkey performing the addition task designed by Brannon and Cantlon. The monkey had to choose from two sets of dots: one set containing the same number of dots as the sum of two previously presented sets and a second set containing a different number of dots. Photograph courtesy of Elizabeth Brannon, Duke University.
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species’ reaction times and accuracies were affected by the ratio between the numerical values of the choice arrays: the larger the numerical difference between the correct and the incorrect choices, the better the performance. These results further suggest that humans and animals share an approximate calculation system that does not involve verbal processing. Research in numerical processing thus shows that animals possess the evolutionary precursors to advanced mathematical abilities: animals can identify and name quantities, compare and discriminate different quantities, and even manipulate those quantities to perform simple additive operations.
Memory There is no question that animals can learn new information. Pavlov’s classical conditioning experiments showed that, after presenting dogs with a sound followed by food, the mere presentation of the sound alone would make the dogs salivate, a response that was initially elicited only by food. We can infer that the dogs learned that the sound predicted food. And, if animals can learn, then that implies they remember the events they experienced in the past. So, the fact that the dogs came to salivate to the sound suggests that they remembered the pairing of sounds with food. Unlike humans, animals cannot verbally express their recollections; but, there are other ways we can study their memory. One popular procedure is delayed matching-to-sample. In the basic task, a sample stimulus (a colored light or a picture) is presented for a few seconds. Then, the sample is removed and a delay ensues. After this delay (which can range from seconds to minutes), the sample is again presented along with one or more comparison stimuli. The animal’s task is to pick the choice stimulus that matches the sample. Monkeys and pigeons successfully perform this task. Moreover, their accuracy generally decreases as the delay increases, which is true for humans as well. Further, animals can perform this matching task even when new sample and comparison stimuli are presented to them, showing effective transfer to a novel situation. Human memory studies have traditionally involved learning and retrieval of lists of items. People are given lists of items and they must later recall as many items as possible. In this situation, the first and the last items in the list tend to be better recalled – primacy and recency, respectively. Animals too show these serial position effects. The memory of pigeons, monkeys, and humans for lists of visual items has been directly compared after different delays between the last item and the recognition test. All three species show the same pattern: strong recency, but no primacy at the shortest delays; the classic U-shaped serial position function at intermediate delays; and, strong primacy, but no recency at the longest delays. Thus, the same mechanisms may mediate the serial memory performance of all three species. Although similarities between species abound, there are disparities too. Sometimes, animals can be even more proficient than humans! One noteworthy example comes from Tetsuro Matsuzawa’s laboratory in Inuyama, Japan. Chimpanzees were first taught to contact each of the numerals from 1 to 9 in order when they appeared in random locations on a computer touchscreen. Even when the sequence lacked some
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Figure 2 A chimpanzee performing the memory task designed by Matsuzawa and his colleagues. On the left, the chimpanzee is touching in sequential order the numerals from 1 to 9 located randomly on the screen. On the right, after brief presentation, the numerals were covered by white squares; here too, the chimpanzee has to touch each of the squares in the correct sequence. This procedure is detailed in Inoue S and Matsuzawa T (2007) Working memory of numerals in chimpanzees. Current Biology 17: R1004–R1005. Photographs courtesy of Tetsuro Matsuzawa, Primate Research Institute, Kyoto University.
of the numbers (e.g., 1-3-4-6-8-9), the chimpanzees could still respond in sequential order from the smallest to the largest. Humans could also do this, of course. But, a critical variation was later introduced. The numerals were presented for a very short duration (from 650 to 210 ms) and then they were replaced by white squares (see Figure 2). Now, subjects had to remember which numeral appeared in which location and then touch each of them in the correct sequence. Humans’ performance plummeted, but chimpanzees’ performance remained high. The chimpanzees could retain an accurate image of the very brief visual scene, an ability that surpasses our own perceptual memory capacity. This eidetic (or photographic) memory can sometimes be seen in young children, but very rarely in adults. This striking developmental disparity was also seen in chimpanzees; the young animals were the best performers in this task. Despite these remarkable illustrations of animal memory, some memory capacities have been deemed to be uniquely human. Episodic memory refers to the ability to remember specific events in time and space, thereby involving: the content of an experience (what), its place of occurrence (where), and its time of occurrence (when). Many mammals and birds are food-hoarding animals; they cache food in specific places for future use. Food hoarding implies that: (1) food has to be stored and concealed and (2) actual consumption is deferred for hours, days, weeks, or months. Successful hording appears to require animals to use past spatial and temporal information to retrieve food later. In a series of experiments with scrub jays, Nicola Clayton and her colleagues allowed animals to store and recover worms and peanuts. Fresh worms are the scrub jays’ preferred food. But, the worms do not last very long; after a few days, they degrade and become unpalatable. Peanuts are less appetizing, but they are nonperishable; so, they can be consumed at any time. The jays were trained in the laboratory to cache worms on one side of a distinctive tray and to cache peanuts on the other side (Figure 3 shows a scrub jay caching food from a tray). Critically, the jays were allowed to retrieve their caches either 4 or 124 h later. After 4 h, the birds tended to inspect the side of the tray where the worms should be; but, after 124 h, the birds tended to inspect the side where the peanuts should be. No olfactory or visual food cues were available; the jays had to rely on their memory at the time of cache recovery.
Figure 3 A scrub jay caching worms and nuts in the compartments of a sand-filled ice-cube tray. Photograph courtesy of Nicola S. Clayton, University of Cambridge.
Evidently, the jays can remember: (1) whether peanuts or worms had been cached – what; (2) on which side of the tray each of the food items had been stored – where; and (3), whether a short or a long time had elapsed since the food items had been cached – when. Thus, the cache and retrieval behaviors of the scrub jays meet the criteria for episodic memory. Many researchers believe that such successful and elaborate food caching suggests that animals plan for the future. However, in order to speak of planning, current behavior must be based on its future consequences and must be independent of the animal’s prevailing motivation. Clayton and her colleagues asked if scrub jays could anticipate their future need states and act accordingly. The researchers placed the jays into two different compartments on alternate mornings for 6 days. In one compartment, food was always there in the morning; in the other compartment, the bowl was empty. Following 6 days of exposure to the two compartments, the jays were unexpectedly given a bowl of cacheable pine nuts after the evening meal; they were also given free access to the two different compartments in which the caching trays had been placed. The jays could either eat the pine nuts or store them in one or the other of the two compartments. The birds opted to store the food, but not randomly; they presumably anticipated their future needs and cached more food in the compartment in which food was not going to be available in the morning. Thus, it seems that scrub jays can plan for a future motivational state.
Animal Cognition
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Conceptualization Flower
Cat
Chair
Car
It is generally admitted that the higher animals possess memory, attention, association, and even some imagination and reason. If these powers are capable of improvement, there seems no great improbability in more complex faculties, such as the higher forms of abstraction, . . . having been evolved through the development and combination of the simpler ones.
Darwin’s speculation that higher-order cognitive capabilities are the evolutionary outgrowth of more primitive cognitive capacities, has bolstered the search for evidence of abstract concept learning in animals. Over the last quarter century, the extent to which animals, especially those phylogenetically distant from humans, can learn abstract concepts and, particularly, understand the relations of sameness and differentness, has become a focal concern of this work. Animals must possess at least some primitive sense of sameness and differentness. Animals are comfortable with members of their own species, but they flee from members of alien species. Plus, animals behave consistently in familiar situations, but they change their actions when something unusual transpires. For example, when uninformative stimuli repeatedly occur, a wide range of animals cease responding to them. This phenomenon of habituation requires the animal to perceive a particular stimulus as the same as one presented before. By contrast, when a second novel stimulus is introduced along with the habituated stimulus, the organism starts responding again – dishabituation. Now, the organism detects something different in the situation and its behavior changes accordingly. We also know that behaviors which are associated with some stimuli in some contexts generalize to other stimuli in other contexts. Indeed, generalization and its counterpart, discrimination, are the foundations of categorization, another familiar instance of conceptual behavior. When an organism makes the same response ‘car’ to discriminably different cars, it is generalizing among all cars; when an organism makes the same response ‘flower’ to discriminably different flowers, it is generalizing among all flowers; at the same time, it is discriminating flowers from cars. Categorization thus involves generalization within a class, but discrimination between classes. If sameness within a category and differentness between categories are not perceived, then each event would have to be treated as entirely unique; animals would need to learn the appropriate response each time that each event is encountered, an excruciatingly demanding and inefficient chore. Pigeons can learn to respond to photographs that contain a human being and not to respond to similar photographs in which human beings are absent. Even when people in the pictures differ in age, size, race, sex, clothing, and posture, pigeons learn to respond to the photographs when they portray people with a high level of accuracy. Pigeons can also learn categorization tasks which are even more similar to the real-world situations that confront humans. Wasserman and his colleagues wanted to see if pigeons could report which stimuli belonged to which of four different categories of objects: cats, flowers, cars, and chairs. One image at a time from each category was presented on a viewing screen. Four report keys were also available, each
Figure 4 Illustration of one trial of the categorization task designed by Wasserman and his colleagues. In this case, a stimulus from the flower category is presented on the screen. The four report keys correspond to each of the four experimental categories; cats, flowers, cars, and chairs. For each of the categories, the pigeons had to choose the appropriate key. Text labels are included for explanatory purposes; they did not appear on the screen.
corresponding to a different category; these four different keys effectively served as four different ‘words’ for the pigeons (see Figure 4). Pigeons learned to select the appropriate key for the different categories. After learning, the birds were given novel items from the training categories to see if they would exhibit reliable transfer from the training stimuli, the critical test to document concept learning. The pigeons passed the test; they successfully used the four report keys to classify the novel items from the four trained categories. It should be noted that items belonging to such categories as cats, flowers, cars, or chairs share a number of physical properties; so, it is generally accepted that perceptual similarity guides classification learning in animals, as it does in humans. A higher level of conceptualization is represented by relational concepts, which do not depend on perceptual similarity, but require learning about the relations between or among two or more stimuli. Here, the absolute properties of the stimuli must be transcended and knowledge of universal applicability must be extracted. In order to speak of a true same–different concept, relational learning and generalization to novel situations must be demonstrated. Habituation and categorization suggest that abstract conceptualization may be within animals’ capabilities, but more explicit evidence is required. Initial efforts to teach pigeons to report whether 2 items – the smallest number that is possible to make a same–different discrimination – are the same as or different from one another were not successful. But, perhaps it would be easier for pigeons to learn same– different discriminations with displays of items containing more than two stimuli. Therefore, Wasserman and his colleagues trained pigeons to peck one button when a stimulus array comprised 16 identical icons and to peck a second button when a stimulus array comprised 16 nonidentical icons (Figure 5, top). Now, pigeons readily learned the discrimination to high levels of accuracy and, critically, they later transferred the discrimination with little decrement to both identical and nonidentical arrays constructed from novel visual items.
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Animal Cognition
Same
Different
Same
Different
Figure 5 Examples of 16-item same and different arrays used in Wasserman and colleagues’ research on same–different discrimination. The top panel shows orderly arrays, whereas the bottom panel shows disorderly arrays. Pigeons are highly adept at discriminating both orderly and disorderly arrays.
One characteristic of the same and different arrays depicted in Figure 5 is that they differed in their spatial orderliness; the same arrays entailed clear horizontal and vertical regularities that the different arrays lacked. Pigeons could have been performing a perceptual rather than a conceptual discrimination. Remember Morgan’s canon of parsimony? Nonetheless, when pigeons were trained to discriminate 16-icon Same from 16-icon Different arrays in which the items were randomly placed on the screen, the birds readily acquired the discrimination and showed excellent transfer to new arrays (Figure 5, bottom). Moreover, randomly rotating the items in an array did not adversely affect pigeons’ discrimination behavior. Finally, when pigeons were shown successive lists of same and different items on a one-at-a-time basis – thereby making spatial orderliness an unusable cue for solving the discrimination – the pigeons still exhibited robust discrimination learning and transfer performance. This pattern of results discredits simple perceptual accounts and strongly suggests that pigeons in these studies had acquired a same–different concept. Even Morgan would have been convinced. To discriminate collections of same and different items involves understanding what are called first-order relations. Can animals go to the next level and learn higher-order relational concepts? Can they understand, not only that several identical apples are the same and that several identical balls are the same, but also that the relation between the apples and the balls is sameness? Judging relations between relations is basic to analogical reasoning; many authors have proposed that analogical competence is the very essence of human intelligence. In an analogical or relational matching-to-sample task, the animal is given a sample stimulus set (either two or more
identical items on some trials and two or more nonidentical items on other trials), and two choice stimulus sets (one containing two or more identical items and the other containing two or more nonidentical items). Critically, none of the items in the sample is presented in the choice sets; so, if the sample is AA, then the choices can be BB or CD. To be successful, the animal must select the set of choice alternatives that instantiates the same relation as the sample set. Given that there is no overlap between the sample and choice items, only attention to the matching relations (same sample to same choice and different sample to different choice) can yield successful performance. When baboons were given a relational matching-to-sample task in which the sample and choice arrays contained 16 items, they successfully learned to choose the 16-item choice array instantiating the same relation as sample array. Accuracy was high when sample arrays containing novel items were presented, thereby attesting to the generality of the relational matching concept; but, when the number of items in the sample arrays was reduced from 16 to 12 to 8 to 4 to 2, baboons’ accuracy systematically fell to chance level. For the baboons, the task proved too taxing when too little pictorial information was available. Similar results have been reported with pigeons. In contrast to baboons and pigeons, chimpanzees solve relational matching-to-sample problems even when only 2 items are presented in the sample and the choice arrays. The first chimpanzee to exhibit a variety of analogical behaviors was Sarah, who could evaluate, complete, and even create analogies. Sarah initially learned to use one plastic token for the concept ‘same’ and another plastic token for the concept ‘different.’ In a set of later experiments, Sarah was given four geometric forms on a display board in a 2 2 format. The 2 items on the left represented one relation and the 2 items on the right represented another relation. Sarah had to choose the correct plastic token (one for ‘same’ and one for ‘different’) and place it in the middle of the board to indicate whether the relations between the set on the left and the set on the right were same (thus representing an analogy) or different. Sarah chose correctly about 80% of the time. In other experiments, Sarah was given 2 items on the left side and only 1 item on the right side. The token for ‘same’ was placed in the middle of the board and Sarah now had to choose, from two alternatives, the item that completed the analogy. Sarah chose the correct option most of the time. She even did so when items representing functional relations were presented. For example, when shown a lock and a key on the left side, and a can on the right side, Sarah would choose the can opener to complete the analogy. Another set of studies explored whether Sarah could also construct analogies. She was given an empty board and 4 or 5 items that could be used to create a valid analogy. This task was especially challenging because Sarah had to find unspecified relations among the items and arrange them on the board so that they would represent a proper analogical relationship. When only 4 items were available, Sarah created a valid analogy 76% of the times. Her performance dropped when 5 items were available, but it was still above chance level. Premack and his colleagues have contended that language training and/or prior experience with arbitrary symbols for the
Animal Cognition
abstract concepts of same and different are needed for animals to exhibit analogical reasoning. Such training may have allowed Sarah to display analogical abilities that had been believed to be uniquely human. Language or symbol systems may facilitate relational and analogical behavior because they provide a way for animals to represent abstract relations so that these relations can be encoded and manipulated. However, the research described above with baboons and pigeons suggests that language or symbol training may not be necessary for disclosing this cognitive capacity. Perhaps critically, both baboons and pigeons had been trained to discriminate same from different collections of items before training on the relational matching-to-sample task. That prior learning of first-order relations may have provided the scaffolding required to process second-order relations. We conclude from all of this research that animals either have a rudimentary capacity for analogical reasoning or they at least possess the basic mechanisms that evolved into this capacity. These observations have important evolutionary implications. Higher-level cognition was once believed to be the unique province of human beings; but, we now know that chimpanzees, baboons, and pigeons show similar intellectual abilities, at least in their basic form. The roots of abstract thought may thus lie deep in our animal ancestry.
Metacognition Demonstrations of numerical and basic mathematical abilities, different types of memory, as well as abstraction and analogical reasoning clearly document that animals possess a broad range of cognitive abilities. But, do animals know what they know? This question is not a tricky word play, but the core matter of research in the growing field of metacognition. Metacognition in humans is said to be associated with conscious awareness of one’s own cognitive states. People know whether they can retrieve a specific memory, whether they can ascertain if they have enough information to make a decision, and whether they can assess the amount of knowledge they have about a certain topic; in short, people can think about their own cognitive states and processes. In the last decade, several researchers have studied metacognition in animals as well. Metacognition in animals is plausible. Imagine this common scene: you are walking through a park and you encounter a woman walking her dog; the dog sees you and then it starts looking back and forth to his owner, as if deciding ‘should I stay or should I go?’ Or we see a hesitant squirrel at the base of a wall apparently deciding if the wall is low enough for her to jump up and land on it safely. When animals do not know what to do, they might defer their actions and seek help or information. These behaviors may be the result of a metacognitive process; but, do they really require metacognition? Do animals have access to their own cognitive states and can they use those states to control their behavior? First attempts to address animal metacognition used what has been dubbed the uncertainty paradigm, in which animals must learn to discriminate between categories of stimuli, for example, between high-pitch and low-pitch sounds or between pixel-dense and pixel-sparse visual images by choosing one
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of two different responses for each of the categories. Animals receive food reward for a correct response and a time-out period for an incorrect response. When the stimuli are near the extreme values, the task is easy; but, the task becomes increasingly difficult the closer values are to the middle of the continuum. In addition to the two category responses, animals are also given a third option – the uncertainty response – that avoids the target discrimination altogether and takes the animal to another easier task and a smaller amount of food than if they had chosen the correct response for the training categories. If animals can monitor their knowledge, then they might choose the uncertainty response when the values to be discriminated are highly similar and failure is likely. And, so they do. Dolphins and monkeys choose the two category responses when the task is easy, but they choose the third uncertainty response when the task is more difficult. Nevertheless, the uncertainty response paradigm has raised several concerns, because alternative explanations based on simple associative learning can explain animals’ apparently metacognitive behavior. For example, animals may have learned to select the uncertainty response for a particular range of stimuli (the difficult ones near the middle of the continuum) because of the reinforcement history with those stimuli (animals are consistently rewarded if they choose the uncertainty response, but they are inconsistently rewarded if they choose the category responses), not because of a subjective feeling of uncertainty. In order to avoid this problem, other paradigms have been devised. As we saw earlier, animals have excellent memories for rich and varied information; but, as in the case of humans, these memories may fade or become difficult to retrieve over time. One interesting possibility is to see if animals can report their having good or poor memory for an event that happened some time ago. Hampton trained rhesus monkeys on a matching-tosample task in which a delay was introduced between offset of the sample image and the testing stimuli, with the sample presented along with three distractors. On some trials, an intermediate choice was introduced at the end of the delay interval which allowed the monkeys to either accept the memory test and receive a preferred reward if they were successful or to decline the memory test and receive a guaranteed, but less desirable reward (see method in Figure 6). On other trials, at the end of the delay interval, only the option to take the test was given, so that the monkeys had to take the memory test. If monkeys have metamemory, then when given the option to accept or to decline the test, they should accept the test if their memory is strong, but they should decline the test if their memory is weak. As a consequence, the monkeys should be more accurate on those trials in which they are given the choice. They should accept the test on choice-test trials when they know that their memory is good. But, the forced-test trials will also include cases in which the monkeys’ memory is poor, thereby, lowering their overall accuracy. Monkeys’ performance accorded with this prediction; they were more accurate on trials in which they accepted the test than on trials in which taking test was the only option, suggesting that the monkeys could distinguish between their different memory states. Similar results have been found with rats.
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Study phase
Delay Delay interval 1/3 of trials
2/3 of trials
Choice phase
Test phase or small reward
associations between the presented stimuli and the alleged metacognitive responses. Animals can certainly exhibit complex behaviors in these metacognitive tasks; nevertheless, some researchers believe that it is premature to conclude that those behaviors are the result of access to and evaluation of internal cognitive states. Metamemory tasks, the gambling paradigm, and other studies do suggest that animals can adaptively regulate their behavior under conditions of uncertainty and respond in accord with the knowledge they posses. But, that behavioral regulation may be achieved by means other than metacognitive processes. For example, animals may use their latency to respond as a cue for subsequent behavior (humans also take into account their speed of coming up with an answer to decide whether or not they really know). Although metacognition is a plausible mechanism for the animals’ behavioral regulation, it is not yet clear whether it is the only possible mechanism. Again, Morgan’s Canon comes into play.
Final Comments Preferred peanut if correct
Primate pellet
Figure 6 Method for assessing monkeys’ metamemory designed by Hampton. Colored panels represent what the monkeys saw on the computer monitor. At the start of each trial, monkeys studied a randomly selected image. After a delay period, on two-thirds of trials, monkeys could choose between taking a memory test (right, left-hand stimulus) or declining the test (right, right-hand stimulus). On the remaining third of the trials, monkeys were forced to take the test (left). From Hampton RR (2001) Rhesus monkeys know when they remember. Proceedings of the National Academy of Sciences 98: 5359–5362. Work in the US public domain.
Monkeys’ confidence in their memory has also been evaluated by allowing them to gamble. Monkeys viewed a series of six pictures, one by one; after the last picture, nine pictures simultaneously appeared on the screen, only one of which had been presented in the prior series. The monkeys’ task was to select this picture. But now, before feedback was provided, the monkeys were given a choice of 2 icons, representing a highrisk option and a low-risk option. A high-risk choice resulted in the gain of three tokens (that could be later exchanged for food) if the monkeys’ response in the picture memory test had been correct, but a loss of three tokens if the monkeys’ response had been incorrect. A low-risk choice resulted in a sure gain of one token. The rationale was that a monkey showing metacognitive capabilities should make a high-risk bet when confident about its prior response, but it should make a low-risk bet when unsure about its prior response. In fact, monkeys chose the high-risk button more often on correct trials than on incorrect trials, suggesting that they knew whether they had responded correctly before the presentation of any feedback. Moreover, monkeys generalized the use of the high- and low-risk options to a variety of different perceptual discrimination and memory tasks. This flexibility further helps to discount any specific
Considerable recent research documents many animals’ ability to remember the past, to respond effectively in the present, and to plan for the future. Research also suggests that animals may be able to take into account their current state of knowledge to control their own behavior in an adaptive way. Finally, animals can master numerical and abstract concepts, perform basic arithmetic operations, and even exhibit behaviors which suggest that they possess the roots of analogical reasoning. Dumb beasts? Hardly! Animals of many different species are sensitive to the rich mosaic of events and relationships that are woven into the causal fabric of the environment. How could it be otherwise? Animals evolved under most of the same constraints and contingencies as the human species. To study animal cognition is to study the mechanisms and functions of cognition without the complexities of language or the biases of anthropomorphism. Doing so not only enriches our understanding of cognition in animals, but it also places human cognition into a more complete evolutionary perspective.
See also: Analogical Reasoning; Associative Learning; Memory; Primate Cognition.
Further Reading Bekoff M, Allen C, and Burghardt GM (eds.) (2002) The Cognitive Animal: Empirical and Theoretical Perspectives on Animal Cognition. Cambridge, MA: MIT Press. Beran MJ (2007) Rhesus monkeys (Macaca mulatta) enumerate sequentially presented sets of items using analog numerical representations. Journal of Experimental Psychology: Animal Behavior Processes 33: 42–54. Beran MJ (2008) The evolutionary and developmental foundations of mathematics. PLoS Biology 6: e19. Boakes R (1984) From Darwin to Behaviorism. Cambridge: Cambridge University Press. Cantlon JF and Brannon EM (2006) Shared system for ordering small and large numbers in monkeys and humans. Psychological Science 17: 401–406. Cantlon JF and Brannon EM (2007) Basic math in monkeys and college students. PLoS Biology 5(12): e328.
Animal Cognition
Clayton NS and Dickinson A (1998) Episodic-like memory during cache recovery by scrub jays. Nature 395: 272–278. Clayton NS and Russell J (2009) Looking for episodic cognition in animals and young children: Prospects for a new minimalism. Neuropsychologia 47: 2330–2340. Darwin C (1897) The Descent of Man, and Selection in Relation to Sex, 2nd edn. New York: Appleton (Original work published 1871). Descartes R (1970) Descartes’s Philosophical Letters. Oxford, UK: Clarendon (Original work published 1646). Fagot J, Wasserman EA, and Young ME (2001) Discriminating the relation between relations: The role of entropy in abstract conceptualization by baboons and humans. Journal of Experimental Psychology: Animal Behavior Processes 27: 316–328. Gillan DD, Premack D, and Woodruff G (1981) Reasoning in the chimpanzee: I. Analogical reasoning. Journal of Experimental Psychology: Animal Behavior Processes 7: 1–17. Hampton RR (2001) Rhesus monkeys know when they remember. Proceedings of the National Academy of Sciences 98: 5359–5362. Hampton RR (2009) Multiple demonstrations of metacognition in nonhumans: Converging evidence or multiple mechanisms? Special Issue on Metacognition. Comparative Cognition & Behavior Reviews 4: 17–28. Hauser M, MacNeilage P, and Ware M (1996) Numerical representations in primates. Proceedings of the National Academy of Sciences 93: 1514–1517. Huxley TH (1874) On the hypothesis that animals are automata, and its history. Reprinted in Huxley TH (1898) Method and Results, Essays. New York, Appleton. Inoue S and Matsuzawa T (2007) Working memory of numerals in chimpanzees. Current Biology 17: R1004–R1005. Kornell N, Son LK, and Terrace HS (2007) Transfer of metacognitive skills and hint seeking in monkeys. Psychological Science 18: 64–71. Locke J (1849) An Essay Concerning Human Understanding. Philadelphia: Kay & Troutman (Original work published 1690). Matsuzawa T (1985) Use of numbers by a chimpanzee. Nature 315: 57–59. Matsuzawa T (2009) Symbolic representation of number in chimpanzees. Current Opinion in Neurobiology 19: 92–98.
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Matsuzawa T, Tomonaga M, and Tanaka M (eds.) (2006) Cognitive Development in Chimpanzees. Tokyo: Springer-Verlag. Morgan CL (1896) An Introduction to Comparative Psychology. London: Walter Scott. Oden DL, Thompson RKR, and Premack D (2001) Can an ape reason analogically? Comprehension and production of analogical problems by Sarah, a Chimpanzee (Pan troglodytes). In: Gentner D, Holyoak KJ, and Kokinov BN (eds.) Analogy: Theory and Phenomena, pp. 472–497. Cambridge, MA: MIT. Pearce JM (2008) Animal Learning and Cognition. New York: Psychology Press. Premack D (1983) The codes of man and beast. Behavioral and Brain Sciences 6: 125–137. Raby CR, Alexis DM, Dickinson A, and Clayton NS (2007) Planning for the future by western scrub-jays. Nature 445: 919–921. Romanes GJ (1892) Animal Intelligence. New York: Appleton (Original work published 1882). Shettleworth SJ (2010) Cognition, Evolution, and Behavior, 2nd edn. New York: Oxford University Press. Smith JD, Shields WE, Schull J, and Washburn DA (1997) The uncertain response in humans and animals. Cognition 62: 75–97. Spencer H (1855) The Principles of Psychology. London: Longman, Brown, Green and Longmans. Thomas RK (1980) Evolution of intelligence: An approach to its assessment. Brain, Behavior, and Evolution 17: 454–472. Wasserman EA and Young ME (2010) Same-different discrimination: The keel and backbone of thought and reasoning. Journal of Experimental Psychology: Animal Behavior Processes 36: 3–22. Wasserman EA and Zentall TR (2006) Comparative Cognition: Experimental Explorations of Animal Intelligence. New York: Oxford University Press. Wright AA, Santiago HC, Sands SF, Kendrick DF, and Cook RG (1985) Memory processing of serial lists by pigeons, monkeys, and people. Science 229: 287–289. Zentall TR, Wasserman EA, Lazareva OF, Thompson RKR, and Rattermann MJ (2008) Concept learning in animals. Comparative Cognition & Behavior Reviews 3: 13–45.
Glossary Associative learning Making the psychological connection between contiguous events in the environment. Categorization Placing different objects or events into separate classes or categories. Conceptualization The process of forming an abstract or generic idea from particular instances. Discrimination Responding differently to two or more stimuli differing in one or more respects. Generalization Transferring a learned response from one stimulus to another. Matching-to-sample Experimental procedure for studying memory in which reward is given for responding to a stimulus if it is the same as a prior sample stimulus.
The Science of Animal Cognition For centuries, it was believed that a clear discontinuity held between the cognitive capabilities of humans and animals. Philosophers and other thinkers tended to be quite skeptical of animals’ mental abilities. For example, the great French philosopher Rene´ Descartes deemed animals to be mere machines whose actions could be explained without invoking any cognitive processes whatsoever. In his Letter to the Marquess of Newcastle, Descartes proposed that “the reason why animals do not speak as we do is not that they lack the organs but that they have no thoughts.” Approximately 50 years later, the great British philosopher John Locke, in An Essay Concerning Human Understanding, joined Descartes in disparaging the cognitive capacities of animals: “the having of general ideas is that which puts a perfect distinction betwixt man and brutes, and is an excellency which the faculties of brutes do by no means attain to.” All that changed some 200 years later when Charles Darwin advanced his revolutionary ideas about the evolution of species. In The Descent of Man, Darwin went beyond suggesting the evolution of physical traits to proposing the evolution of the mind as well. For Darwin, there was “no fundamental difference between man and the higher mammals in their mental faculties” (p. 66). Human and animal intelligence differ only in degree, not in kind, Darwin argued; so, there should be no sharp schism between human and animal mind. The human mind might be the final step in the evolution of intellectual functions, but the roots of human mental processes should be observable in animals as well. Darwin’s unsettling ideas had actually been presaged by other philosophers and psychologists. In his Principles of Psychology, Herbert Spencer suggested that the mind could be understood only by examining how it had evolved. According to Spencer, mental capacities could be represented along a continuum with no great gaps: a continuous progression
Memory Control of current behavior by past stimulation. Metacognition Thinking about one’s own cognitive states and processes. Numerical processing Set of abilities related to understanding and manipulating numbers, such as: using symbols to denote quantities; discriminating, ordering, and comparing different quantities; and, combining quantities in order to perform arithmetic operations. Reinforcer Any consequence of a behavior that increases the probability that the organism will repeat that behavior. Time-out A brief period of time during which there is no stimulation; time-out is a form of punishment that is used to decrease the probability that the organism will repeat a behavior.
from simple associative learning to complex forms of abstraction and reasoning. The ideas of Darwin and Spencer on the evolution of the mind were staunchly defended by Thomas Huxley, who wrote in 1874 that: “the doctrine of continuity is too well established for it to be permissible to suppose that any complex natural phenomenon comes into existence suddenly and without being preceded by simpler modifications; and very strong arguments would be needed to show that such complex phenomena as those of consciousness first made their appearance in man.” These principles of evolutionary biology clearly clashed with Cartesian philosophy. The gradual evolution of cognition was a direct consequence of this new biological vantage point. Critically, within that new paradigm, it was entirely plausible to believe that mental capabilities could be observed in organisms other than humans. Once the hypothesis of mental continuity was enunciated, evidence to support it was needed. Darwin himself amassed a large collection of supportive anecdotes: stories told by naturalists, zookeepers, and pet owners attesting to how smart animals really were. For example: Dr. Hayes, in his work on The Open Polar Sea, repeatedly remarks that his dogs, instead of continuing to draw the sledges in a compact body, diverged and separated when they came to thin ice, so that their weight might be more evenly distributed. This was often the first warning which the travelers received that the ice was becoming thin and dangerous.
Darwin was alert to the hazards of relying on anecdotal evidence alone; such observations lacked the information needed to pinpoint the mechanisms responsible for the animals’ behavior. Relevant to the prior example, Darwin wondered about the origins of the dogs’ behavior: “now, did the dogs act thus from the experience of each individual, or from the example of the older and wiser dogs, or from an inherited habit, that is from instinct?”
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Any bona fide scientific analysis of animal cognition requires careful recording of the relevant behaviors as well as precise knowledge and control of the variables influencing or determining that behavior. Darwin’s disciple, George Romanes, advocated for an objective analysis of the mind in his book Animal Intelligence: “We can only infer the existence and the nature of thoughts and feelings from the activities of the organisms which appear to exhibit them.” (p. 1). Yet, despite his advocacy for and his interest in documenting mental continuity across species, Romanes’ work too was simply a compilation, albeit extensive and thorough, of still more informal observations and anecdotes. The true transition to the scientific study of animal cognition began with C. Lloyd Morgan. In his Introduction to Comparative Psychology, Morgan emphasized the importance of distinguishing between an animal’s behavior and the interpretation of that behavior. In order to properly interpret an organism’s behavior, high standards of objectivity are needed. As well, systematic experimental studies are required in order to reach incisive conclusions as to the origins of behavior, which he himself undertook with baby chicks whose developmental history could be known and carefully controlled. Finally, circumspection was to be one of Morgan’s enduring contributions. Morgan’s famous canon urged researchers to be cautious in advocating advanced cognitive interpretations when the behavior in question might be more parsimoniously explained by simpler behavioral processes. These then were the forebears of the science of comparative cognition. Following the methods of natural science, research in comparative cognition today seeks to discover the extent to which our mental capacities are similar to those of other animals and to answer many longstanding questions. Are animals intelligent? Is human intelligence superior to animal intelligence? What are the cognitive and neurobiological mechanisms of intelligent action? In this article, we will consider a few particularly prominent and promising areas of research in animal cognition: numerical processing, memory and planning, conceptualization, and metacognition. This sampling of topics, issues, methods, and findings should provide readers with a good taste of the current state of the science of animal cognition. A broader compilation of animal cognition studies can be found in Comparative Cognition edited by Wasserman and Zentall.
Numerical Processing Number is an important property of objects in the external world. Critically, numerical processing can be considered to be mediated by an abstract cognitive process, because behavioral control by number must be made irrespective of the specific physical features of the items to be evaluated; 2, 5, or 8 items are 2, 5, or 8 regardless of whether the items are apples, pebbles, or geckos. Traditionally, it has been believed that linguistic competence is necessary for numerical processing. Although it is the case that a verbal system does mediate several numerical and mathematical operations, considerable evidence has been obtained documenting numerical processing by animals and infants lacking language; this evidence suggests
that common preverbal processes underlie numerical and mathematical competencies in humans and animals. Basic numerical abilities comprise: learning and using symbols to denote specific quantities; discriminating, ordering, and comparing different quantities; and, combining quantities so that basic arithmetic operations, such as addition and subtraction, can be accomplished. The Japanese primatologist Tetsuro Matsuzawa taught a 5-year-old chimpanzee, Ai, to use Arabic numerals to denote the number of items in a display. Ai was first presented with either 1 or 2 items in a display window and then required to choose either the ‘1’ or the ‘2’ key on a numeric keyboard. As Ai mastered the lower numbers, higher numbers of items were progressively introduced, up to a maximum of 6. Regardless of the items being colors, objects, or symbols, Ai’s accuracy exceeded 90%. Nevertheless, Ai had greater difficulty reporting the numbers when they were close to one another (e.g., it was easier for Ai to distinguish 2 from 5 than to distinguish 2 from 3) and when the numbers were at the higher end of the scale (e.g., Ai was more accurate at identifying 2 and 3 than at identifying 5 and 6, even when the disparity between them was in both cases 1 item). It seems that, as quantities increase, the discrepancies between them become less obvious and, in turn, the discrimination between them becomes more challenging. Interestingly, Ai’s behavior is not an isolated curiosity. Humans too find it easier to distinguish between 2 and 14 items than between 4 and 5 items. This finding is called the numerical distance effect. As well, humans can rapidly distinguish between 2 and 3 items, but we cannot so rapidly say if there are 14 or 15 items, in this case showing what is called the numerical magnitude effect. The numerical magnitude effect has also been found in monkeys, rats, and pigeons. Recent research has found that rhesus monkeys can learn to choose the smaller of two simultaneously presented sets of items when such nonnumerical factors as area and density are controlled. Monkeys can also choose the larger of two quantities even when the items are sequentially presented, as when the items fall one by one into an opaque container. In addition, Brannon and her colleagues have found that monkeys’ accuracy rises as the disparity between the amounts to be evaluated increases. Indeed, the monkeys’ performance is affected by the ratio of the values being compared. As predicted by Weber’s law, the larger the amounts, the greater the difference between them must be in order to be distinguishable. This relationship was true for human college students as well. These and other observations have led researchers to propose that humans and animals share a system that calculates inexact, but approximate representations of quantity. For example, when having to choose among different waiting lines, we tend to select the shortest. We rarely do so by precisely counting the people in line, but rather do so by a process of rough estimation; we use an approximate preverbal number system. It is not difficult to imagine that animals in the wild benefit from the ability to distinguish between trees containing 5 and 15 fruits or predators attacking in groups of 2 or 4. Small or large quantities can surely make a difference and numerosity judgments in these situations seem to be similarly performed by verbal and nonverbal organisms.
Animal Cognition
Estimating and representing numerical values appears to be necessary for the next step in numerical reasoning: interrelating quantities so that operations like adding, subtracting, multiplying, and dividing can be accomplished. Can animals perform these basic arithmetic operations? Studies using a preferential looking paradigm have suggested that preverbal human infants and monkeys can understand simple arithmetical operations, such as adding and subtracting a small number of visually presented objects. For example, when monkeys watched as two eggplants were placed behind a screen, they looked longer when the screen was removed and only one eggplant was present than when two eggplants were present. Monkeys may have spent more time looking at the incorrect outcome because it surprisingly violated the rules of arithmetic. But, the results of such studies may be explained in terms of the infants’ and monkeys’ understanding of object permanence: organisms can keep track of occluded objects and when one object unexpectedly appears or disappears, they are surprised; no real arithmetic ability is required in that case. More compelling evidence of addition in monkeys has been found by Brannon and Cantlon. They presented animals with two sets of dots on two sequential screens. Then, on a third screen, the monkeys had to choose from two sets: one containing the number of dots equal to the sum of the two sets and a second containing a different number of dots (see Figure 1). Monkeys learned to choose the array that roughly corresponded to the arithmetic sum of the two sets of dots and they readily transferred this behavior to novel combinations. Some problems proved to be more difficult than others. For example, monkeys were faster and more accurate when, after being presented with a set of 2 dots and a second set of 6 dots, they had to choose between 8 and 14 dots compared to when they had to choose between 8 and 10 dots. The closer the two possible answers, the more difficult the task. When humans were given the same nonverbal task, their pattern of performance was strikingly similar. Although humans were generally more proficient than monkeys, both
Figure 1 A monkey performing the addition task designed by Brannon and Cantlon. The monkey had to choose from two sets of dots: one set containing the same number of dots as the sum of two previously presented sets and a second set containing a different number of dots. Photograph courtesy of Elizabeth Brannon, Duke University.
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species’ reaction times and accuracies were affected by the ratio between the numerical values of the choice arrays: the larger the numerical difference between the correct and the incorrect choices, the better the performance. These results further suggest that humans and animals share an approximate calculation system that does not involve verbal processing. Research in numerical processing thus shows that animals possess the evolutionary precursors to advanced mathematical abilities: animals can identify and name quantities, compare and discriminate different quantities, and even manipulate those quantities to perform simple additive operations.
Memory There is no question that animals can learn new information. Pavlov’s classical conditioning experiments showed that, after presenting dogs with a sound followed by food, the mere presentation of the sound alone would make the dogs salivate, a response that was initially elicited only by food. We can infer that the dogs learned that the sound predicted food. And, if animals can learn, then that implies they remember the events they experienced in the past. So, the fact that the dogs came to salivate to the sound suggests that they remembered the pairing of sounds with food. Unlike humans, animals cannot verbally express their recollections; but, there are other ways we can study their memory. One popular procedure is delayed matching-to-sample. In the basic task, a sample stimulus (a colored light or a picture) is presented for a few seconds. Then, the sample is removed and a delay ensues. After this delay (which can range from seconds to minutes), the sample is again presented along with one or more comparison stimuli. The animal’s task is to pick the choice stimulus that matches the sample. Monkeys and pigeons successfully perform this task. Moreover, their accuracy generally decreases as the delay increases, which is true for humans as well. Further, animals can perform this matching task even when new sample and comparison stimuli are presented to them, showing effective transfer to a novel situation. Human memory studies have traditionally involved learning and retrieval of lists of items. People are given lists of items and they must later recall as many items as possible. In this situation, the first and the last items in the list tend to be better recalled – primacy and recency, respectively. Animals too show these serial position effects. The memory of pigeons, monkeys, and humans for lists of visual items has been directly compared after different delays between the last item and the recognition test. All three species show the same pattern: strong recency, but no primacy at the shortest delays; the classic U-shaped serial position function at intermediate delays; and, strong primacy, but no recency at the longest delays. Thus, the same mechanisms may mediate the serial memory performance of all three species. Although similarities between species abound, there are disparities too. Sometimes, animals can be even more proficient than humans! One noteworthy example comes from Tetsuro Matsuzawa’s laboratory in Inuyama, Japan. Chimpanzees were first taught to contact each of the numerals from 1 to 9 in order when they appeared in random locations on a computer touchscreen. Even when the sequence lacked some
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Figure 2 A chimpanzee performing the memory task designed by Matsuzawa and his colleagues. On the left, the chimpanzee is touching in sequential order the numerals from 1 to 9 located randomly on the screen. On the right, after brief presentation, the numerals were covered by white squares; here too, the chimpanzee has to touch each of the squares in the correct sequence. This procedure is detailed in Inoue S and Matsuzawa T (2007) Working memory of numerals in chimpanzees. Current Biology 17: R1004–R1005. Photographs courtesy of Tetsuro Matsuzawa, Primate Research Institute, Kyoto University.
of the numbers (e.g., 1-3-4-6-8-9), the chimpanzees could still respond in sequential order from the smallest to the largest. Humans could also do this, of course. But, a critical variation was later introduced. The numerals were presented for a very short duration (from 650 to 210 ms) and then they were replaced by white squares (see Figure 2). Now, subjects had to remember which numeral appeared in which location and then touch each of them in the correct sequence. Humans’ performance plummeted, but chimpanzees’ performance remained high. The chimpanzees could retain an accurate image of the very brief visual scene, an ability that surpasses our own perceptual memory capacity. This eidetic (or photographic) memory can sometimes be seen in young children, but very rarely in adults. This striking developmental disparity was also seen in chimpanzees; the young animals were the best performers in this task. Despite these remarkable illustrations of animal memory, some memory capacities have been deemed to be uniquely human. Episodic memory refers to the ability to remember specific events in time and space, thereby involving: the content of an experience (what), its place of occurrence (where), and its time of occurrence (when). Many mammals and birds are food-hoarding animals; they cache food in specific places for future use. Food hoarding implies that: (1) food has to be stored and concealed and (2) actual consumption is deferred for hours, days, weeks, or months. Successful hording appears to require animals to use past spatial and temporal information to retrieve food later. In a series of experiments with scrub jays, Nicola Clayton and her colleagues allowed animals to store and recover worms and peanuts. Fresh worms are the scrub jays’ preferred food. But, the worms do not last very long; after a few days, they degrade and become unpalatable. Peanuts are less appetizing, but they are nonperishable; so, they can be consumed at any time. The jays were trained in the laboratory to cache worms on one side of a distinctive tray and to cache peanuts on the other side (Figure 3 shows a scrub jay caching food from a tray). Critically, the jays were allowed to retrieve their caches either 4 or 124 h later. After 4 h, the birds tended to inspect the side of the tray where the worms should be; but, after 124 h, the birds tended to inspect the side where the peanuts should be. No olfactory or visual food cues were available; the jays had to rely on their memory at the time of cache recovery.
Figure 3 A scrub jay caching worms and nuts in the compartments of a sand-filled ice-cube tray. Photograph courtesy of Nicola S. Clayton, University of Cambridge.
Evidently, the jays can remember: (1) whether peanuts or worms had been cached – what; (2) on which side of the tray each of the food items had been stored – where; and (3), whether a short or a long time had elapsed since the food items had been cached – when. Thus, the cache and retrieval behaviors of the scrub jays meet the criteria for episodic memory. Many researchers believe that such successful and elaborate food caching suggests that animals plan for the future. However, in order to speak of planning, current behavior must be based on its future consequences and must be independent of the animal’s prevailing motivation. Clayton and her colleagues asked if scrub jays could anticipate their future need states and act accordingly. The researchers placed the jays into two different compartments on alternate mornings for 6 days. In one compartment, food was always there in the morning; in the other compartment, the bowl was empty. Following 6 days of exposure to the two compartments, the jays were unexpectedly given a bowl of cacheable pine nuts after the evening meal; they were also given free access to the two different compartments in which the caching trays had been placed. The jays could either eat the pine nuts or store them in one or the other of the two compartments. The birds opted to store the food, but not randomly; they presumably anticipated their future needs and cached more food in the compartment in which food was not going to be available in the morning. Thus, it seems that scrub jays can plan for a future motivational state.
Animal Cognition
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Conceptualization Flower
Cat
Chair
Car
It is generally admitted that the higher animals possess memory, attention, association, and even some imagination and reason. If these powers are capable of improvement, there seems no great improbability in more complex faculties, such as the higher forms of abstraction, . . . having been evolved through the development and combination of the simpler ones.
Darwin’s speculation that higher-order cognitive capabilities are the evolutionary outgrowth of more primitive cognitive capacities, has bolstered the search for evidence of abstract concept learning in animals. Over the last quarter century, the extent to which animals, especially those phylogenetically distant from humans, can learn abstract concepts and, particularly, understand the relations of sameness and differentness, has become a focal concern of this work. Animals must possess at least some primitive sense of sameness and differentness. Animals are comfortable with members of their own species, but they flee from members of alien species. Plus, animals behave consistently in familiar situations, but they change their actions when something unusual transpires. For example, when uninformative stimuli repeatedly occur, a wide range of animals cease responding to them. This phenomenon of habituation requires the animal to perceive a particular stimulus as the same as one presented before. By contrast, when a second novel stimulus is introduced along with the habituated stimulus, the organism starts responding again – dishabituation. Now, the organism detects something different in the situation and its behavior changes accordingly. We also know that behaviors which are associated with some stimuli in some contexts generalize to other stimuli in other contexts. Indeed, generalization and its counterpart, discrimination, are the foundations of categorization, another familiar instance of conceptual behavior. When an organism makes the same response ‘car’ to discriminably different cars, it is generalizing among all cars; when an organism makes the same response ‘flower’ to discriminably different flowers, it is generalizing among all flowers; at the same time, it is discriminating flowers from cars. Categorization thus involves generalization within a class, but discrimination between classes. If sameness within a category and differentness between categories are not perceived, then each event would have to be treated as entirely unique; animals would need to learn the appropriate response each time that each event is encountered, an excruciatingly demanding and inefficient chore. Pigeons can learn to respond to photographs that contain a human being and not to respond to similar photographs in which human beings are absent. Even when people in the pictures differ in age, size, race, sex, clothing, and posture, pigeons learn to respond to the photographs when they portray people with a high level of accuracy. Pigeons can also learn categorization tasks which are even more similar to the real-world situations that confront humans. Wasserman and his colleagues wanted to see if pigeons could report which stimuli belonged to which of four different categories of objects: cats, flowers, cars, and chairs. One image at a time from each category was presented on a viewing screen. Four report keys were also available, each
Figure 4 Illustration of one trial of the categorization task designed by Wasserman and his colleagues. In this case, a stimulus from the flower category is presented on the screen. The four report keys correspond to each of the four experimental categories; cats, flowers, cars, and chairs. For each of the categories, the pigeons had to choose the appropriate key. Text labels are included for explanatory purposes; they did not appear on the screen.
corresponding to a different category; these four different keys effectively served as four different ‘words’ for the pigeons (see Figure 4). Pigeons learned to select the appropriate key for the different categories. After learning, the birds were given novel items from the training categories to see if they would exhibit reliable transfer from the training stimuli, the critical test to document concept learning. The pigeons passed the test; they successfully used the four report keys to classify the novel items from the four trained categories. It should be noted that items belonging to such categories as cats, flowers, cars, or chairs share a number of physical properties; so, it is generally accepted that perceptual similarity guides classification learning in animals, as it does in humans. A higher level of conceptualization is represented by relational concepts, which do not depend on perceptual similarity, but require learning about the relations between or among two or more stimuli. Here, the absolute properties of the stimuli must be transcended and knowledge of universal applicability must be extracted. In order to speak of a true same–different concept, relational learning and generalization to novel situations must be demonstrated. Habituation and categorization suggest that abstract conceptualization may be within animals’ capabilities, but more explicit evidence is required. Initial efforts to teach pigeons to report whether 2 items – the smallest number that is possible to make a same–different discrimination – are the same as or different from one another were not successful. But, perhaps it would be easier for pigeons to learn same– different discriminations with displays of items containing more than two stimuli. Therefore, Wasserman and his colleagues trained pigeons to peck one button when a stimulus array comprised 16 identical icons and to peck a second button when a stimulus array comprised 16 nonidentical icons (Figure 5, top). Now, pigeons readily learned the discrimination to high levels of accuracy and, critically, they later transferred the discrimination with little decrement to both identical and nonidentical arrays constructed from novel visual items.
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Same
Different
Same
Different
Figure 5 Examples of 16-item same and different arrays used in Wasserman and colleagues’ research on same–different discrimination. The top panel shows orderly arrays, whereas the bottom panel shows disorderly arrays. Pigeons are highly adept at discriminating both orderly and disorderly arrays.
One characteristic of the same and different arrays depicted in Figure 5 is that they differed in their spatial orderliness; the same arrays entailed clear horizontal and vertical regularities that the different arrays lacked. Pigeons could have been performing a perceptual rather than a conceptual discrimination. Remember Morgan’s canon of parsimony? Nonetheless, when pigeons were trained to discriminate 16-icon Same from 16-icon Different arrays in which the items were randomly placed on the screen, the birds readily acquired the discrimination and showed excellent transfer to new arrays (Figure 5, bottom). Moreover, randomly rotating the items in an array did not adversely affect pigeons’ discrimination behavior. Finally, when pigeons were shown successive lists of same and different items on a one-at-a-time basis – thereby making spatial orderliness an unusable cue for solving the discrimination – the pigeons still exhibited robust discrimination learning and transfer performance. This pattern of results discredits simple perceptual accounts and strongly suggests that pigeons in these studies had acquired a same–different concept. Even Morgan would have been convinced. To discriminate collections of same and different items involves understanding what are called first-order relations. Can animals go to the next level and learn higher-order relational concepts? Can they understand, not only that several identical apples are the same and that several identical balls are the same, but also that the relation between the apples and the balls is sameness? Judging relations between relations is basic to analogical reasoning; many authors have proposed that analogical competence is the very essence of human intelligence. In an analogical or relational matching-to-sample task, the animal is given a sample stimulus set (either two or more
identical items on some trials and two or more nonidentical items on other trials), and two choice stimulus sets (one containing two or more identical items and the other containing two or more nonidentical items). Critically, none of the items in the sample is presented in the choice sets; so, if the sample is AA, then the choices can be BB or CD. To be successful, the animal must select the set of choice alternatives that instantiates the same relation as the sample set. Given that there is no overlap between the sample and choice items, only attention to the matching relations (same sample to same choice and different sample to different choice) can yield successful performance. When baboons were given a relational matching-to-sample task in which the sample and choice arrays contained 16 items, they successfully learned to choose the 16-item choice array instantiating the same relation as sample array. Accuracy was high when sample arrays containing novel items were presented, thereby attesting to the generality of the relational matching concept; but, when the number of items in the sample arrays was reduced from 16 to 12 to 8 to 4 to 2, baboons’ accuracy systematically fell to chance level. For the baboons, the task proved too taxing when too little pictorial information was available. Similar results have been reported with pigeons. In contrast to baboons and pigeons, chimpanzees solve relational matching-to-sample problems even when only 2 items are presented in the sample and the choice arrays. The first chimpanzee to exhibit a variety of analogical behaviors was Sarah, who could evaluate, complete, and even create analogies. Sarah initially learned to use one plastic token for the concept ‘same’ and another plastic token for the concept ‘different.’ In a set of later experiments, Sarah was given four geometric forms on a display board in a 2 2 format. The 2 items on the left represented one relation and the 2 items on the right represented another relation. Sarah had to choose the correct plastic token (one for ‘same’ and one for ‘different’) and place it in the middle of the board to indicate whether the relations between the set on the left and the set on the right were same (thus representing an analogy) or different. Sarah chose correctly about 80% of the time. In other experiments, Sarah was given 2 items on the left side and only 1 item on the right side. The token for ‘same’ was placed in the middle of the board and Sarah now had to choose, from two alternatives, the item that completed the analogy. Sarah chose the correct option most of the time. She even did so when items representing functional relations were presented. For example, when shown a lock and a key on the left side, and a can on the right side, Sarah would choose the can opener to complete the analogy. Another set of studies explored whether Sarah could also construct analogies. She was given an empty board and 4 or 5 items that could be used to create a valid analogy. This task was especially challenging because Sarah had to find unspecified relations among the items and arrange them on the board so that they would represent a proper analogical relationship. When only 4 items were available, Sarah created a valid analogy 76% of the times. Her performance dropped when 5 items were available, but it was still above chance level. Premack and his colleagues have contended that language training and/or prior experience with arbitrary symbols for the
Animal Cognition
abstract concepts of same and different are needed for animals to exhibit analogical reasoning. Such training may have allowed Sarah to display analogical abilities that had been believed to be uniquely human. Language or symbol systems may facilitate relational and analogical behavior because they provide a way for animals to represent abstract relations so that these relations can be encoded and manipulated. However, the research described above with baboons and pigeons suggests that language or symbol training may not be necessary for disclosing this cognitive capacity. Perhaps critically, both baboons and pigeons had been trained to discriminate same from different collections of items before training on the relational matching-to-sample task. That prior learning of first-order relations may have provided the scaffolding required to process second-order relations. We conclude from all of this research that animals either have a rudimentary capacity for analogical reasoning or they at least possess the basic mechanisms that evolved into this capacity. These observations have important evolutionary implications. Higher-level cognition was once believed to be the unique province of human beings; but, we now know that chimpanzees, baboons, and pigeons show similar intellectual abilities, at least in their basic form. The roots of abstract thought may thus lie deep in our animal ancestry.
Metacognition Demonstrations of numerical and basic mathematical abilities, different types of memory, as well as abstraction and analogical reasoning clearly document that animals possess a broad range of cognitive abilities. But, do animals know what they know? This question is not a tricky word play, but the core matter of research in the growing field of metacognition. Metacognition in humans is said to be associated with conscious awareness of one’s own cognitive states. People know whether they can retrieve a specific memory, whether they can ascertain if they have enough information to make a decision, and whether they can assess the amount of knowledge they have about a certain topic; in short, people can think about their own cognitive states and processes. In the last decade, several researchers have studied metacognition in animals as well. Metacognition in animals is plausible. Imagine this common scene: you are walking through a park and you encounter a woman walking her dog; the dog sees you and then it starts looking back and forth to his owner, as if deciding ‘should I stay or should I go?’ Or we see a hesitant squirrel at the base of a wall apparently deciding if the wall is low enough for her to jump up and land on it safely. When animals do not know what to do, they might defer their actions and seek help or information. These behaviors may be the result of a metacognitive process; but, do they really require metacognition? Do animals have access to their own cognitive states and can they use those states to control their behavior? First attempts to address animal metacognition used what has been dubbed the uncertainty paradigm, in which animals must learn to discriminate between categories of stimuli, for example, between high-pitch and low-pitch sounds or between pixel-dense and pixel-sparse visual images by choosing one
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of two different responses for each of the categories. Animals receive food reward for a correct response and a time-out period for an incorrect response. When the stimuli are near the extreme values, the task is easy; but, the task becomes increasingly difficult the closer values are to the middle of the continuum. In addition to the two category responses, animals are also given a third option – the uncertainty response – that avoids the target discrimination altogether and takes the animal to another easier task and a smaller amount of food than if they had chosen the correct response for the training categories. If animals can monitor their knowledge, then they might choose the uncertainty response when the values to be discriminated are highly similar and failure is likely. And, so they do. Dolphins and monkeys choose the two category responses when the task is easy, but they choose the third uncertainty response when the task is more difficult. Nevertheless, the uncertainty response paradigm has raised several concerns, because alternative explanations based on simple associative learning can explain animals’ apparently metacognitive behavior. For example, animals may have learned to select the uncertainty response for a particular range of stimuli (the difficult ones near the middle of the continuum) because of the reinforcement history with those stimuli (animals are consistently rewarded if they choose the uncertainty response, but they are inconsistently rewarded if they choose the category responses), not because of a subjective feeling of uncertainty. In order to avoid this problem, other paradigms have been devised. As we saw earlier, animals have excellent memories for rich and varied information; but, as in the case of humans, these memories may fade or become difficult to retrieve over time. One interesting possibility is to see if animals can report their having good or poor memory for an event that happened some time ago. Hampton trained rhesus monkeys on a matching-tosample task in which a delay was introduced between offset of the sample image and the testing stimuli, with the sample presented along with three distractors. On some trials, an intermediate choice was introduced at the end of the delay interval which allowed the monkeys to either accept the memory test and receive a preferred reward if they were successful or to decline the memory test and receive a guaranteed, but less desirable reward (see method in Figure 6). On other trials, at the end of the delay interval, only the option to take the test was given, so that the monkeys had to take the memory test. If monkeys have metamemory, then when given the option to accept or to decline the test, they should accept the test if their memory is strong, but they should decline the test if their memory is weak. As a consequence, the monkeys should be more accurate on those trials in which they are given the choice. They should accept the test on choice-test trials when they know that their memory is good. But, the forced-test trials will also include cases in which the monkeys’ memory is poor, thereby, lowering their overall accuracy. Monkeys’ performance accorded with this prediction; they were more accurate on trials in which they accepted the test than on trials in which taking test was the only option, suggesting that the monkeys could distinguish between their different memory states. Similar results have been found with rats.
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Study phase
Delay Delay interval 1/3 of trials
2/3 of trials
Choice phase
Test phase or small reward
associations between the presented stimuli and the alleged metacognitive responses. Animals can certainly exhibit complex behaviors in these metacognitive tasks; nevertheless, some researchers believe that it is premature to conclude that those behaviors are the result of access to and evaluation of internal cognitive states. Metamemory tasks, the gambling paradigm, and other studies do suggest that animals can adaptively regulate their behavior under conditions of uncertainty and respond in accord with the knowledge they posses. But, that behavioral regulation may be achieved by means other than metacognitive processes. For example, animals may use their latency to respond as a cue for subsequent behavior (humans also take into account their speed of coming up with an answer to decide whether or not they really know). Although metacognition is a plausible mechanism for the animals’ behavioral regulation, it is not yet clear whether it is the only possible mechanism. Again, Morgan’s Canon comes into play.
Final Comments Preferred peanut if correct
Primate pellet
Figure 6 Method for assessing monkeys’ metamemory designed by Hampton. Colored panels represent what the monkeys saw on the computer monitor. At the start of each trial, monkeys studied a randomly selected image. After a delay period, on two-thirds of trials, monkeys could choose between taking a memory test (right, left-hand stimulus) or declining the test (right, right-hand stimulus). On the remaining third of the trials, monkeys were forced to take the test (left). From Hampton RR (2001) Rhesus monkeys know when they remember. Proceedings of the National Academy of Sciences 98: 5359–5362. Work in the US public domain.
Monkeys’ confidence in their memory has also been evaluated by allowing them to gamble. Monkeys viewed a series of six pictures, one by one; after the last picture, nine pictures simultaneously appeared on the screen, only one of which had been presented in the prior series. The monkeys’ task was to select this picture. But now, before feedback was provided, the monkeys were given a choice of 2 icons, representing a highrisk option and a low-risk option. A high-risk choice resulted in the gain of three tokens (that could be later exchanged for food) if the monkeys’ response in the picture memory test had been correct, but a loss of three tokens if the monkeys’ response had been incorrect. A low-risk choice resulted in a sure gain of one token. The rationale was that a monkey showing metacognitive capabilities should make a high-risk bet when confident about its prior response, but it should make a low-risk bet when unsure about its prior response. In fact, monkeys chose the high-risk button more often on correct trials than on incorrect trials, suggesting that they knew whether they had responded correctly before the presentation of any feedback. Moreover, monkeys generalized the use of the high- and low-risk options to a variety of different perceptual discrimination and memory tasks. This flexibility further helps to discount any specific
Considerable recent research documents many animals’ ability to remember the past, to respond effectively in the present, and to plan for the future. Research also suggests that animals may be able to take into account their current state of knowledge to control their own behavior in an adaptive way. Finally, animals can master numerical and abstract concepts, perform basic arithmetic operations, and even exhibit behaviors which suggest that they possess the roots of analogical reasoning. Dumb beasts? Hardly! Animals of many different species are sensitive to the rich mosaic of events and relationships that are woven into the causal fabric of the environment. How could it be otherwise? Animals evolved under most of the same constraints and contingencies as the human species. To study animal cognition is to study the mechanisms and functions of cognition without the complexities of language or the biases of anthropomorphism. Doing so not only enriches our understanding of cognition in animals, but it also places human cognition into a more complete evolutionary perspective.
See also: Analogical Reasoning; Associative Learning; Memory; Primate Cognition.
Further Reading Bekoff M, Allen C, and Burghardt GM (eds.) (2002) The Cognitive Animal: Empirical and Theoretical Perspectives on Animal Cognition. Cambridge, MA: MIT Press. Beran MJ (2007) Rhesus monkeys (Macaca mulatta) enumerate sequentially presented sets of items using analog numerical representations. Journal of Experimental Psychology: Animal Behavior Processes 33: 42–54. Beran MJ (2008) The evolutionary and developmental foundations of mathematics. PLoS Biology 6: e19. Boakes R (1984) From Darwin to Behaviorism. Cambridge: Cambridge University Press. Cantlon JF and Brannon EM (2006) Shared system for ordering small and large numbers in monkeys and humans. Psychological Science 17: 401–406. Cantlon JF and Brannon EM (2007) Basic math in monkeys and college students. PLoS Biology 5(12): e328.
Animal Cognition
Clayton NS and Dickinson A (1998) Episodic-like memory during cache recovery by scrub jays. Nature 395: 272–278. Clayton NS and Russell J (2009) Looking for episodic cognition in animals and young children: Prospects for a new minimalism. Neuropsychologia 47: 2330–2340. Darwin C (1897) The Descent of Man, and Selection in Relation to Sex, 2nd edn. New York: Appleton (Original work published 1871). Descartes R (1970) Descartes’s Philosophical Letters. Oxford, UK: Clarendon (Original work published 1646). Fagot J, Wasserman EA, and Young ME (2001) Discriminating the relation between relations: The role of entropy in abstract conceptualization by baboons and humans. Journal of Experimental Psychology: Animal Behavior Processes 27: 316–328. Gillan DD, Premack D, and Woodruff G (1981) Reasoning in the chimpanzee: I. Analogical reasoning. Journal of Experimental Psychology: Animal Behavior Processes 7: 1–17. Hampton RR (2001) Rhesus monkeys know when they remember. Proceedings of the National Academy of Sciences 98: 5359–5362. Hampton RR (2009) Multiple demonstrations of metacognition in nonhumans: Converging evidence or multiple mechanisms? Special Issue on Metacognition. Comparative Cognition & Behavior Reviews 4: 17–28. Hauser M, MacNeilage P, and Ware M (1996) Numerical representations in primates. Proceedings of the National Academy of Sciences 93: 1514–1517. Huxley TH (1874) On the hypothesis that animals are automata, and its history. Reprinted in Huxley TH (1898) Method and Results, Essays. New York, Appleton. Inoue S and Matsuzawa T (2007) Working memory of numerals in chimpanzees. Current Biology 17: R1004–R1005. Kornell N, Son LK, and Terrace HS (2007) Transfer of metacognitive skills and hint seeking in monkeys. Psychological Science 18: 64–71. Locke J (1849) An Essay Concerning Human Understanding. Philadelphia: Kay & Troutman (Original work published 1690). Matsuzawa T (1985) Use of numbers by a chimpanzee. Nature 315: 57–59. Matsuzawa T (2009) Symbolic representation of number in chimpanzees. Current Opinion in Neurobiology 19: 92–98.
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Matsuzawa T, Tomonaga M, and Tanaka M (eds.) (2006) Cognitive Development in Chimpanzees. Tokyo: Springer-Verlag. Morgan CL (1896) An Introduction to Comparative Psychology. London: Walter Scott. Oden DL, Thompson RKR, and Premack D (2001) Can an ape reason analogically? Comprehension and production of analogical problems by Sarah, a Chimpanzee (Pan troglodytes). In: Gentner D, Holyoak KJ, and Kokinov BN (eds.) Analogy: Theory and Phenomena, pp. 472–497. Cambridge, MA: MIT. Pearce JM (2008) Animal Learning and Cognition. New York: Psychology Press. Premack D (1983) The codes of man and beast. Behavioral and Brain Sciences 6: 125–137. Raby CR, Alexis DM, Dickinson A, and Clayton NS (2007) Planning for the future by western scrub-jays. Nature 445: 919–921. Romanes GJ (1892) Animal Intelligence. New York: Appleton (Original work published 1882). Shettleworth SJ (2010) Cognition, Evolution, and Behavior, 2nd edn. New York: Oxford University Press. Smith JD, Shields WE, Schull J, and Washburn DA (1997) The uncertain response in humans and animals. Cognition 62: 75–97. Spencer H (1855) The Principles of Psychology. London: Longman, Brown, Green and Longmans. Thomas RK (1980) Evolution of intelligence: An approach to its assessment. Brain, Behavior, and Evolution 17: 454–472. Wasserman EA and Young ME (2010) Same-different discrimination: The keel and backbone of thought and reasoning. Journal of Experimental Psychology: Animal Behavior Processes 36: 3–22. Wasserman EA and Zentall TR (2006) Comparative Cognition: Experimental Explorations of Animal Intelligence. New York: Oxford University Press. Wright AA, Santiago HC, Sands SF, Kendrick DF, and Cook RG (1985) Memory processing of serial lists by pigeons, monkeys, and people. Science 229: 287–289. Zentall TR, Wasserman EA, Lazareva OF, Thompson RKR, and Rattermann MJ (2008) Concept learning in animals. Comparative Cognition & Behavior Reviews 3: 13–45.