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date: 18 February 2020

Animal Learning and Cognition

Summary and Keywords

Comparative psychology is the study of behavior and cognition across species. In recent decades, much of this research has focused on cognitive capacities that are well studied in humans. This approach provides comparative perspectives on the evolution of these cognitive capacities. Although in many areas humans shows distinct aspects of various cognitive processes, it is clear that for most major topics in human cognition, important and illustrative data are available from studies with other animals. Moreover, these areas of investigation increasingly show continuities between the behavior of other species and human behavior. Several of these cognitive processes, including concept and category learning, numerical cognition, memory, mental time travel and prospective cognition, metacognition, and language learning, highlight these continuities and demonstrate the richness of mental lives in other animals. Nonhuman animals can discriminate between categories of perceptual and conceptual classes, they can form concepts, and they can use those concepts to guide decision making and choice behavior. Other species can engage in rudimentary numerical cognition, and more importantly share with humans certain core quantitative abilities for the approximate representation of magnitude and number. Nonhuman animals share many phenomena of memory that are well-recognized in humans, and in some cases may even share the capacity to mentally re-experience the past and to anticipate and plan for the future. In some cases, some species may even reflect on their own knowledge states, memory accessibility, and perceptual acuity as they make metacognitive judgments. And, studies of animal communication provided the basis for intensive assessments of language-like behavior in certain species. Taken together, these results argue much more for continuity than discontinuity. This should not be seen as a challenge to the uniqueness of human minds, but rather as a way to better understand how we became the species we are through the process of evolution.

Keywords: animal cognition, comparative psychology, comparative cognition, metacognition, memory, mental time travel, numerical cognition, categories and concepts, language

Introduction

Despite being the title of this article, Animal Learning and Cognition is simply not a topic that can be adequately covered in a single article such as this one. The study of animal learning began well before psychology was recognized as a field of scientific inquiry, and questions about the nature of animal behavior and the possible existence of nonhuman animal minds have been being asked nearly as long as they have been about human behavior and human minds. These initially more philosophical questions eventually became one of the central areas of study in the formal new field of comparative psychology (see Dewsbury, 1984, 2013; Innis, 1998; Takasuna, 2010; Wasserman, 1993), with some of the world’s leading theorists writing some of the earliest papers in experimental psychology about animal learning and behavior (e.g., Mills, 1899; Morgan, 1894; Romanes, 1883a, 1883b; Small, 1900, 1901; Thorndike, 1898, 1911; Washburn, 1908; Yerkes, 1907). At that time, the first big debate was between those who saw animals as being much like humans in their ability to represent, and reason, and engage in cognitive faculties (e.g., Romanes, 1883a, 1883b), and those who saw animals as largely constrained in their behavior to producing responses to environmental stimuli based solely on the consequences of such previous stimulus–response pairings (e.g., Thorndike, 1898, 1911).

At the turn of the 20th century, animals were viewed by some scientists and the public as being intelligent in ways matching human intelligence. However, a strong backlash emerged against this perspective because of some high-profile instances of seemingly intelligent animal behaviors being instead the result of simpler explanations (e.g., the Clever Hans controversy, Pfungst, 1911). This led to a long period in which the dominant position on animal behavior was that of behaviorism and its focus on understanding the role of stimulus control and contingencies on responding as being the major determinants of animal behavior (Catania & Harnad, 1988; Skinner, 1938, 1969, 1974). During this time, however, there were “hints” that there was more to the behavior of animals than their classically and operantly conditioned responses. For example, the work of Tolman with rats and other species suggested that animals may engage in “purposive” behavior that is goal-directed and that involves some form of expectations (Tolman, 1932, 1948). Furthermore, examples of frustration at violated expectations in monkeys (Tinklepaugh, 1928) also suggest that there is more to animal minds than simply stored stimulus–response–outcome associations. In addition, the research of Kohler, Yerkes, and others with the great apes indicated the real possibility that some animals engage in planning behavior, experience insightful revelations, and have sophisticated forms of memory (e.g., Kohler, 1925; Tinklepaugh, 1932; Yerkes & Yerkes, 1928). These and other reports set the stage for the emergence of what is now called comparative cognition, or comparative cognitive science (Honig & Fetterman, 1992; Hulse, Fowler, & Honig, 1978; Roitblat, Bever, & Terrace, 1984), which is the subfield of comparative psychology concerned with explaining animal behavior through the same theoretical lens that emerged in the 1960s in human psychology and is now known as cognitive psychology (Neisser, 1967). The goal in this article is to provide the reader with a basic overview of some of the most dominant research themes and findings in the field of animal cognition. There are other excellent sources to which the reader is also directed (e.g., Call, Burghardt, Pepperberg, Snowdon, & Zentall, 2017; Gallistel, 1989; Maestripieri, 2003; Miklósi, 2014; Roberts, 1998; Shettleworth, 2009; Tomasello & Call, 1997; Vonk & Shackelford, 2012; Wynne & Udell, 2013; Zentall & Wasserman, 2012).

I have approached the organization of the article as if one were perusing a human cognitive psychology textbook and looking at the topics in that book as they might appear in a book on animal cognitive psychology. In doing so, four things become immediately evident. First, there are some aspects of human cognition that are understudied with other species, or that are, at this time, controversial with regards to whether we can conclude that animals share these capacities with humans. Second, there are some topics in animal cognition that are rarely considered in books on human cognition (for example, the development of tool-using skills), although these topics are sometimes also areas of investigation in developmental psychology. This leads to the third observation: whereas animal cognition research nearly always makes explicit the relation of what animal studies mean, and how they relate, to human cognition, the reverse is not always true. Comparative psychology has always had a keen sense that studying animals is important not only for understanding other species, but for understanding our species also. Human cognitive psychology does not always recognize the reverse (nor, for that matter, does developmental psychology). In some ways, this makes sense—we are humans, and so understanding ourselves must be a greater priority in all areas of social science (and even some areas of physical science, such as the need for astronomers and astrophysicists to remember that people want to know where we specifically fit in the broader picture of what the universe is). But our understanding of ourselves is greatly improved when we recognize that a comparative perspective lends insights into the nature of cognitive processes that cannot be seen when studying only one species (this argument aligns with the same idea about studying cognition in humans across cultures and across the lifespan).

Finally, it will be evident to the reader that my biases guide my coverage of topics. There is simply no way to fully and appropriately cover all of the interesting aspects of behavior seen in the wide-ranging species that biologists, psychologists, and ethologists study. Much is left out of this article, with my apologies to those who are pushing new limits in what we know about cognition in understudied species such as bats, spiders, bees, octopi, bears, cuttlefish, ants, fish, and other groups, often through use of new tests that these creative scientists have designed to see whether these species join the more commonly tested dogs, birds, rats, and various nonhuman primates. There are excellent sources that give the needed greater coverage to at least some of this work that I hope the reader will also examine (e.g., Agrillo, Petrazzini, & Bisazza, 2017; Cross & Jackson, 2016, 2017; Darmaillacq, Dickel, & Mather, 2014; Finn, Tregenza, & Norman, 2009; Geva-Sagiv, Las, Yovel, & Ulanovsky, 2015; Gould, 1990; Japyassu & Laland, 2017; Johnson-Ulrich et al., 2016; Lucon-Xiccato & Bisazza, 2017; Matsubara, Deeming, & Wilkinson, 2017; Reznikova, 2007; Wilkinson & Huber, 2012). With these ideas and limitations in mind, the following sections outline some of the most productive and well-established areas of knowledge about nonhuman animal behavior and cognition, and some of the areas of greatest historical importance. Given the space constraints, I can acknowledge only a small portion of the larger literatures one can find for these topics, and many other important areas of comparative cognition must be left out (such as important topics in perception, many forms of communication, various types of memory, navigation, timing behavior, relational learning, aspects of concept formation, social awareness, and many others). In addition, I will not have space to give full and fair coverage to many species that have been and are being studied in great detail. For example, one could have written this article focusing extensively on recent research with canine subjects and the excellent overviews of that research that also reflect the international character of the researchers working with dogs and other canids (e.g., Byosiere, Chouinard, Howell, & Bennett, 2018; Horschler et al., 2019; Miklósi, 2014; Miklósi & Kubinyi, 2016; Range & Virányi, 2014; Utrata, Virányi, & Range, 2012; Wynne, 2016). This also could have aided in demonstrating aspects of social cognition studied in comparative perspective (e.g., Range & Virányi, 2013) and provided some examples of potential evolutionary divergence in animal cognition (e.g., Lampe, Bräuer, Kaminski, & Virányi, 2017; Lea & Osthaus, 2018; Marshall-Pescini, Schwarz, Kostelnik, Virányi, & Range, 2017; Wynne & Udell, 2013). This last point is an important one, given that we know that some species reach equivalent degrees of performance on some cognitive tests from different selective pressures, and often through engagement of different neural and behavioral mechanisms. I have focused on studies I know best, which undoubtedly will leave out many other important studies. In penance for this personal bias, I have ignored one of my favorite areas, self-control research with nonhuman animals (see Beran, 2018, for a long discussion of that topic). My goal here is to provide a jumping-off point for these topics for anyone interested in finding their way into those larger literatures, in which they will see a great diversity of species performing tasks that now nearly fully cover the topics covered in textbooks of human cognition.

Categories and Concept Learning

One of the earliest and most influential claims in the emerging field of comparative cognition was that other species could conceptualize (for a review, see Zentall, Wasserman, Lazareva, Thompson, & Rattermann, 2008). Category formation and concept formation are considered essential aspects of representational thought, allowing humans to parse the world into different classes of things, often on the basis of qualities that are not simply part of the featural make-up of a stimulus. Humans can classify the same thing at different levels, such as a basic level (my pet is a dog), a superordinate level (my pet is a mammal), and a subordinate level (my dog is considered a Bostalian, which is a mix of an Italian greyhound and Boston terrier). We do this in different ways, and largely the debate in the human cognitive literature has focused on the role that exemplars (individual experiences with different examples of some category or concept) and prototypes (idealized forms that come to represent these categories and concepts) play in how we represent the world categorically (e.g., Mandler & Bauer, 1988; Mervis & Rosch, 1981; Minda & Smith, 2002; Nosofsky & Johansen, 2000; Smith & Minda, 1998). Early comparative work focused on whether other species also could learn about trained exemplars as being members of a category and then respond appropriately when asked to classify new exemplars immediately to the correct categories (e.g., Herrnstein, Loveland, & Cable, 1976; Zentall & Hogan, 1974).

More recently, comparative research has focused on how best to instantiate abstract categorical rules such as same–different in other species, with a fairly strong consensus that what works best is to train more examples from the outset rather than fewer (e.g., Bodily, Katz, & Wright, 2008; Katz & Wright, 2006; Wright, Cook, Rivera, Sands, & Delius, 1988). This seems to lead animals to glean the relevant concept from the stimuli and transfer that representation of concept to new experiences with new stimuli. Although this is a consistent finding across species, other research has shown strong differences in how monkeys learn category rules compared with pigeons (Smith et al., 2012a). Monkeys perform much better in learning to classify arbitrary stimuli when a declarable rule determines the categories into which stimuli should be placed compared with tests in which the classification of the same kinds of stimuli is determined on the basis of implicit rather than explicit rules (Smith, Beran, Crossley, Boomer, & Ashby, 2010; Smith et al., 2012b). In other words, when a rule that humans can declare explains why some things are As and other things are Bs, humans and monkeys learn more quickly than when trial-and-error is the only way to come to memorize how to classify stimuli. Rules help primates. They seem to help pigeons less so, as their learning curves are the same for both types of tasks. Before being too hard on the pigeons, however, one should note that more associatively constrained learning in pigeons is highly adaptive in other contexts. In one of my favorite examples, pigeons substantially outperform monkeys and humans in the famous Monty Hall game (Herbranson, 2012; Herbranson & Schroeder, 2010; Klein, Evans, Schultz, & Beran, 2013), likely because they attend to stimulus–reward contingencies whereas monkeys and humans exhibit choice biases and instead tend to match probabilities. So, where pigeons may struggle more to learn categories, they succeed more in understanding some contingencies.

Numerical and Quantitative Cognition

One of the longest-running and most thoroughly studied areas in comparative psychology has been that of asking whether and how animals represent and respond to quantitative aspects of stimuli. Our world is quantifiable. Magnitudes are a central feature of stimuli experienced through all sensory channels: the number of things we see, the loudness of sounds we hear, the amount of pressure applied to our bodies and encoded as tactile feedback, and so forth. It has been a major topic in comparative psychology to engage in psychophysical and other approaches to understanding cognitive processing of quantitative information, and especially number (Boysen & Capaldi, 1993; Geary, Berch, & Mann Koepke, 2015; Cohen Kadosh & Dowker, 2015). An early question in comparative cognition was whether animals could count (Boysen & Capaldi, 1993; Davis & Memmott, 1982; Davis & Perusse, 1988).

At that time, the idea was to examine what things were needed to show counting behavior in children (Gelman & Gallistel, 1978), and then see whether other species could match those criteria. Some studies came close. A parrot showed the capacity to label the numbers of items in sets and also respond to questions about what sets of items were of a specific number (Pepperberg, 1987, 1994, 2006). Pigeons learned to map symbols to quantities (Xia, Emmerton, Siemann, & Delius, 2001). Rats learned to run to a specifically designated numerical alley (e.g., the third alley) within mazes where the distances were unrelated to the numbers of alleys (Capaldi & Miller, 1988). Monkeys mapped Arabic numerals to specific quantities of food (Washburn & Rumbaugh, 1991), and chimpanzees learned to “count out” dot arrays on a computer screen to match a target number (Beran & Rumbaugh, 2001; Beran, Rumbaugh, & Savage-Rumbaugh, 1998), or they learned to use symbols to answer queries about the numbers of specific kinds of things (Matsuzawa, 1985). Perhaps most famously, chimpanzees learned to move to different areas where they saw different numbers of items, and then label the summed total with an Arabic numeral (Boysen & Berntsen, 1989). In all of these cases, some aspects of human counting were present, but not all of the criteria could be met. This led to a shift in thinking about how animals deal with number, which in turn led to the now widely recognized idea that all species, including humans, seem to share a capacity for representing number in an approximate way (Brannon, 2005; Brannon & Roitman, 2003; Cantlon, Platt, & Brannon, 2009). What this means is that many species can discriminate between quantities, and can sometimes sum or combine approximate representations of number in a way that leads to proficient, but not perfect, results in terms of resulting representations. In other words, numbers are represented like other magnitudes such as area, loudness, density, darkness—as magnitudes that obey Weber’s law when it comes to discriminability.

From this perspective, we see that insects (e.g., Carazo, Font, Forteza-Behrendt, & Desfilis, 2009; Pahl, Si, & Zhang, 2013), reptiles (Gazzola, Vallortigara, & Pellitteri-Rosa, 2018; Krusche, Uller, & Dicke, 2010; Petrazzini et al., 2017), fish (Agrillo, Dadda, Serena, & Bisazza, 2008; Agrillo, Piffer, & Bisazza, 2011; Gómez-Laplaza & Gerlai, 2011; Potrich, Sovrano, Stancher, & Vallortigara, 2015), birds (e.g., Garland, Low, & Burns, 2012), and mammals show many similarities in the ways in which they engage this system of magnitude representation. Among the mammals, we see this for dogs and wolves (e.g., Baker, Morath, Rodzon, & Jordan, 2012; Petrazzini & Wynne, 2016; Utrata et al., 2012; Ward & Smuts, 2007), elephants (Irie-Sugimoto, Kobayashi, Sato, & Hasegawa, 2009; Perdue, Talbot, Stone, & Beran, 2012), bears (Vonk & Beran, 2012), sea lions (Abramson, Hernandez-Lloreda, Call, & Colmenares, 2011), lemurs (e.g., Jones & Brannon, 2012; Lewis, Jaffe, & Brannon, 2005; Merritt, MacLean, Crawford, & Brannon, 2011), various monkey species (e.g., Beran, 2007, 2008; Brannon & Terrace, 2000; Cantlon & Brannon, 2006; Jordan & Brannon, 2006; Nieder & Miller, 2004), and the great apes (Beran, 2001, 2004; Hanus & Call, 2007). However, these are not examples of human counting, and they are incomplete as the basis for a formal understanding of higher-order mathematics. Numbers matter to animals, but their capacity for more formal aspects of mathematics is restricted because they do not fully, symbolically represent numerical concepts.

Memory

In some human cognition textbooks, chapters on different types or forms of memory may fill a quarter to even a third of the book. This is because we have come to recognize that humans use memory in different ways, from remembering a phone number for only a few seconds to remembering events from the past for decades. Memory studies were among the first to be conducted with nonhuman animals (e.g., Yerkes & Yerkes, 1928), and substantial interest in that topic from a comparative perspective emerged as the cognitive revolution in human psychology came to dominate the research agenda for studies with humans. Animals clearly remember things. This means they encode information, retain that information, and then retrieve it at the appropriate time, either through recall mechanisms that draw the information to conscious awareness or through the use of cues that generate recognition of some stored item. What is most often studied in nonhuman animals is short-term memory (for things such as stimulus identity or spatial locations), but I will not focus on those studies here. Suffice it to say that when considering studies of short-term and spatial memory, we know that many species can remember things they have experienced after relatively brief or sometimes even extended delays.

In some cases, studies have looked at combined features of a remembered event, in what might approximate an assessment of episodic memory (see “Mental Time Travel and Prospective Cognition”) or at least require remembering rehearsed information for those multiple components of the experienced event. These are often called what–where tasks or what–where–when tasks, and they involve seeing different items in different locations at different times, and then being queried on only one feature (what, where, or when) or queried successively on multiple features without any indication of the order of queries. Such studies show that animals such as monkeys (e.g., Hampton, Hampstead, & Murray, 2005; Hoffman, Beran, & Washburn, 2009), rats and mice (e.g., Babb & Crystal, 2005; DeVito & Eichenbaum, 2010), and various birds (e.g., Feeney, Roberts, & Sherry, 2009; Skov-Rackette, Miller, & Shettleworth, 2006; Zinkivskay, Nazir, & Smulders, 2009) can remember the different features of such events at least through delay intervals. For example, Babb and Crystal allowed rats to visit four different locations in a maze where only one held a highly preferred reward. Later, rats could go to any of eight locations including the four already visited. If a short period of time passed between these two stages of the test, the four previously visited locations held nothing but the new locations held a food reward. If a long period of time passed between the stages, then the new locations provided food and the location with the earlier high preference reward also again held food. In this latter case, the rats were more likely to return to that location as well as visit the new locations, suggesting they remembered where the high preference item had been located and how long it had been since they visited that location originally. Importantly, if that high preference item was manipulated to make it a negative experience (i.e., it was chocolate paired with lithium chloride), then the rats avoided a return to that location even after a long delay, adding evidence that they remembered what they had eaten as well as when and where they had eaten it earlier in the test session.

In human cognitive psychology, a tremendous amount of research has focused on working memory (Baddeley & Hitch, 1974), which is the engagement of attentional mechanisms that manipulate information held in consciousness toward the goal of problem solving. Working memory often is conceptualized as including a phonological loop, which is a subvocal articulatory system that presumably other species do not engage if they lack language. However, other aspects of working memory models are potentially available to nonhuman or nonverbal individuals, and so this has become an area of focus in comparative psychological studies of memory. Some studies have assessed whether working memory may occur in other species by forcing animals to engage in rehearsal while under concurrent cognitive load or some other form of attentional load. This cognitive load tends to disrupt performance, suggesting that maintenance of active information (i.e., rehearsal) is cognitively demanding, and perhaps limited in ways that make it susceptible to load. The results of some studies suggest that some species therefore may have analogous working memory systems to those seen in humans (Brady & Hampton, 2018a, 2018b; Washburn & Astur, 1998).

Mental Time Travel and Prospective Cognition

Some of the most fascinating studies of memory in animals pertain to episodic memory and the question of mental time travel (Michaelian, Klein, & Szpunar, 2016). Humans engage in episodic memory when they remember a specific event in their own past, something that often is unshared with anyone else. And, as part of that experience, they feel as if they are traveling back in time to experience the event again (what is called autonoesis; Tulving, 1972, 2002). This aspect of memory has been argued to be unique to humans, due to the limitation that such autonoetic components cannot be demonstrated in nonverbal species. However, some of the most creative experimental designs in comparative cognition have challenged that argument, especially for those who recognize that this claim of autonoetic experience is not a valid disqualifier of other behavioral indications that animals remember unique events of their own past.

Extensive coverage of this debate, and these demonstrations in other species, have been written (e.g., Crystal, 2010; Griffiths, Dickinson, & Clayton, 1999; Templer & Hampton, 2013). Some of the earliest work was with caching bird species, where birds cached food items of differing quality but also differing lengths of viability. When given the chance to cache less preferred but longer-lasting items and more preferred but shorter-lasting items, the birds would selectively retrieve the better items after shorter delays, and the lower preference (but still viable) items after longer delays. This suggested that in addition to remembering what was hidden, and where, the birds also remembered when they had hidden the items or how long it had been since they did so (e.g., Clayton & Dickinson, 2001; Clayton, Yu, & Dickinson, 2001). A host of follow-up studies addressed many of the alternate explanations of this behavior, with some strong evidence that birds seem to remember these caching events as events in their past (for reviews, see Jelbert & Clayton, 2017; Templer & Hampton, 2013; Watanabe, 2018).

Research has also been pursued with nonhuman primates, and especially great apes. Studies have shown that after long delays, apes remember unique events from the past, and behave in ways that suggest they are recalling those events in their entirety. This can happen, for example, when the sudden reappearance of an experimenter seemingly leads to recollection of the past tests the apes did with this individual, and to immediate and appropriate responding as if the same type of event is about to begin (e.g., Lewis, Call, & Berntsen, 2017) or when the passing of time acts as a cue to how apes next should respond (e.g., Martin-Ordas, Haun, Colmenares, & Call, 2010). In at least one case, chimpanzees seemed to recall a previously viewed video event in a way that suggested they remembered the scripted scene in terms of its content, and their eye-tracked behavior suggested they anticipated what would come next as they re-watched that video, an outcome that aligns with the idea they remembered the movie (Kano & Hirata, 2015).

An extensive series of experiments with a language-trained chimpanzee also showed that she could remember what was hidden, where it was hidden, and even that such hiding events did not have to be seen in person to be encoded and remembered (Menzel, 1999). For example, after seeing an experimenter hide an item outdoors, and out of reach, the chimpanzee would recruit another human who was unaware of what was going on after a delay of minutes or hours by informing them about what was outdoors, and then taking and directing them to the item through use of gestures and vocalizations. In another example, a gorilla was presented with events that involved specific people and items, and then later was given picture cards to report who had given what to the gorilla earlier (Schwartz, Colon, Sanchez, Rodriguez, & Evans, 2002; Schwartz, Hoffman, & Evans, 2005). These demonstrations with apes suggest that they structure experiences as personal events and can recall aspects of those events at a later time. Moreover, it is not only birds caching and chimpanzees remembering events that suggest episodic memory may exist in animals. Rats also seem to engage in such recollection, as assessed through a number of different kinds of tasks (e.g., Babb & Crystal, 2005, 2006; Eacott, Easton, & Zinkivskay, 2005). These are just a small sample of the many papers now available to probe the limits of episodic-like memory in animals (see Dere, Kart-Teke, Huston, & Silva, 2006). The key issue here is that from birds to rats to nonhuman primates, the evidence suggests some degree of remembering the personal past as consisting of experienced events.

Mental time travel is not just about the past. Humans engage in such time travel when we anticipate the future and engage in thinking about what we will do in the future. This, too, has been a controversial topic in animal cognition. Even claims that animals can plan for the future, independent of whether they do so by anticipating themselves as future agents, has been controversial. However, some definitions of planning behavior have allowed for such possibilities in animals, and a large number of studies show that some species seem to anticipate future needs, and perhaps even future states. For example, chimpanzees can determine what tool will be needed in the future and select that tool now so that they have it, even though they cannot use it now (e.g., Bräuer & Call, 2015; Mulcahy & Call, 2006). Nonhuman primates navigate through computerized and manual mazes in ways that suggest they “look ahead” at least partly (e.g., Fragaszy, Johnson-Pynn, Hirsh, & Brakke, 2003; Völter & Call, 2014). In more naturalistic designs, caching birds show evidence of prospective cognition and planning when they anticipate future conditions in which they will retrieve caches (e.g., Clayton, Emery, & Dickinson, 2006; Correia, Dickinson, & Clayton, 2007; Raby, Alexis, Dickinson, & Clayton, 2007).

A specific kind of planning behavior involves what is known as prospective memory. One engages in prospective memory when one anticipates a future need and then acts on that need at the correct time or correct place. For example, when you realize that there is no more coffee in the house, and so you will need to stop to buy more on the way home that evening, you engage in prospective memory when you successfully make that purchase hours later. Prospective memory involves encoding that future intention, assessing how well one can retrieve it at the correct time (and, if necessary, making adjustments to ensure remembering), and then implementing the intention when one should (see Brandimonte, Einstein, & McDaniel, 2014; McDaniel & Einstein, 2007). Prospective memory is an important part of daily life for humans, but also one of our most fallible cognitive processes, and one that may decline as we age. Interestingly, cognitive psychologists working on prospective memory tend not to place a strong emphasis on the autonoetic component of this form of mental time travel, and so the methods can be adapted in some cases for use with animals. And when they are, some species seem to engage in prospective memory and encoding. These include rats and monkeys engaging in sequences of item retrieval where they must shift from what they remember that they already did to what they still need to do or remember what they need to do later (Beran, Evans, Klein, & Einstein, 2012; Cook, Brown, & Riley, 1985; Evans & Beran, 2012; Klein, Evans, & Beran, 2011). Chimpanzees also engage in forms of prospective memory in which they must remember that a unique moment is the time at which a different response will be required, and then recall that intended response when the right time appears (Beran, Perdue, Bramlett, Menzel, & Evans, 2012; Evans, Perdue, & Beran, 2014; Perdue, Evans, Williamson, Gonsiorowski, & Beran, 2014). In another example, rats showed prospective memory during tests in which they could anticipate a future meal and remember when it was time to eat, while also engaged in other ongoing tasks that showed the prospective memory did access and deplete other cognitive resources (Wilson & Crystal, 2012; Wilson, Pizzo, & Crystal, 2013). These demonstrations (and others) suggest that nonhuman animals can engage mental processes that extend beyond present stimuli. An equally compelling example of this level of sophistication emerged from studies that asked whether animals may also reflect somehow on their own perceptual experiences and memories.

Metacognition

Metacognition is often defined as the process of thinking about one’s own thinking. More specifically, metacognition involves the control and monitoring of ongoing mental activity, so that one can assess states of certainty or uncertainty, degrees of confidence in knowledge or memory, and then adjust behavior to seek information or reduce uncertainty when possible. Humans engage in metacognition when we assess whether we know or remember something, when we gauge how well we have studied, or when we choose to ask for help or delay responding until we can learn more (Dunlosky & Bjork, 2008; Flavell, 1979; Metcalfe & Kober, 2005; Nelson, 1992; Nelson & Narens, 1990; Schwartz, 1994). The first effort to determine whether other species may also assess confidence and monitor uncertainty involved an auditory discrimination task with a dolphin (Smith et al., 1995). The dolphin had to determine whether a tone was high or low in pitch, and there were difficult tones at which the dolphin struggled to classify them correctly. The dolphin also was given another type of response that acted to avoid the primary discrimination and instead lead to an easier task. The dolphin avoided exactly those trials on which it performed worst, and a new experimental paradigm emerged that has been called the uncertainty monitoring test. Subsequent studies focused more on nonhuman primates, and especially rhesus macaques, with those monkeys showing that they, too, escaped exactly the hardest trials in psychophysical discrimination tasks such as judging the density of a pixelated stimulus (e.g., Smith, Shields, Schull, & Washburn, 1997), judging the similarity of two visual stimuli (Shields, Smith, & Washburn, 1997), assessing whether a stimulus was part of a to-be-remembered list of items (Smith, Shields, Allendoerfer, & Washburn, 1998), and providing confidence ratings about judgments that were made (Shields, Smith, Guttmannova, & Washburn, 2005). In many of these cases, humans were given the same task, and the patterns of escaping the most difficult trials, for which the risk of error was highest, were nearly identical for monkeys and for people.

Other approaches were soon developed (e.g., Basile, Schroeder, Brown, Templer, & Hampton, 2015; Brown, Templer, & Hampton, 2017; Kornell, Son, & Terrace, 2007; Suda-King, Bania, Stromberg, & Subiaul, 2013; Templer & Hampton, 2012). In one, monkeys could choose to take a memory test or avoid the test; monkeys avoided trials on which they were most likely to err, and they did better when they chose to take a test versus when the same test was forced on them (Hampton, 2001). This suggested that they knew when their performance was likely to be correct. In another task developed for use with chimpanzees and children (Carpenter & Call, 2001), these participants either saw an item being hidden or they did not see the hiding event. Then, they could choose to look first into possible hiding locations, or just point out where they thought the item was hidden. Children and chimpanzees pointed right away more often when they saw the hiding event, but looked first more often when they had not, suggesting they knew when they knew the location or did not know it, and adjusted behavior accordingly. This task was later given to orangutans (Marsh & MacDonald, 2012) and to different monkey species (Basile, Hampton, Suomi, & Murray, 2009; Fujita, 2009; Vining & Marsh, 2015), showing that these kinds of successful performances were fairly widespread among multiple primate species.

These approaches converged on the idea that nonhuman primates exhibit metacognition. However, alternative explanations emerged, as did strong critiques of the approach. Those critiques were then met with counter-arguments (e.g., Carruthers, 2008, 2009; Crystal, 2014; Crystal & Foote, 2009; Hampton, 2009; Jozefowiez, Staddon, & Cerutti, 2009; Kornell, 2009, 2014; Le Pelley, 2012; Smith, 2009; Smith, Beran, Couchman, & Coutinho, 2008), and this has led to a productive and fast-moving area of research in which new paradigms have been developed and new approaches have been created to address criticisms. Concerns that uncertainty responses were lower-level stimulus-controlled responses were countered with tests in which stimuli were trial unique, or feedback was deferred and rearranged to prevent monkeys from learning to avoid stimuli with poor reinforcement histories (Smith, Beran, Redford, & Washburn, 2006). Concerns about metamemory tests where monkeys might rely on various cues present in the task were rebutted by carefully controlled studies that prevented such cues (e.g., Basile et al., 2015). Concerns about the information-seeking tasks with hidden items were countered by studies in which low-level interpretations could be ruled out (e.g., Beran, Smith, & Perdue, 2013; Call, 2010), allowing for stronger claims of metacognitive control and monitoring to be offered.

New approaches in recent years with nonhuman primates have also demonstrated efficient confidence ratings by chimpanzees as measured through their movements toward reward before being told they were correct on tasks (Beran et al., 2015). Other studies showed that macaque monkeys calibrated their regions of uncertainty based on the level of reward at risk (Zakrzewski, Perdue, Beran, Church, & Smith, 2014), much as humans would do when playing games in which small or large amounts of reward were on the line as they performed. And studies showed that monkeys gauged their confidence retrospectively and prospectively by choosing to take tests that would be performed well more often than those that would not and choosing to have tests scored that were more likely to have been correct than those that were less likely to be correct. This approach was also combined with symbolic representation of what was at stake, to again show a change in monitoring performance as a function of the stakes (Kornell et al., 2007). Furthermore, among the newest studies are examples in which metacognition misaligns with reality, as in the case of fluency errors in which monkeys show more confidence in trials that involve easier-to-perceive stimuli, even though their memory for those stimuli is no better than memory for less easily perceived stimuli (Ferrigno, Kornell, & Cantlon, 2017). These metacognitive errors are particularly compelling because they align with such errors in humans (see Kornell, 2014), suggesting that monitoring and control mechanisms are fallible in ways that highlight how those mechanisms interact with primary perceptual and cognitive processes.

After the first study with the dolphin, animal metacognition research has remained largely focused on nonhuman primate species, but some work has been conducted with rats, pigeons, and dogs. Although early work with rats suggested they also monitor confidence (Crystal & Foote, 2007), later work questioned this (Foote & Crystal, 2009; Crystal, 2014). However, as is often the case, refinements in the experimental approach have ultimately led to a number of positive reports of metacognition in rats (Foote & Crystal, 2012; Templer, Lee, & Preston, 2017). For pigeons, the story is less clear, with some reports of metacognitive failures in pigeons (Sutton & Shettleworth, 2008), but other reports where these species show some aspects of metacognitive control (Castro & Wasserman, 2013; Iwasaki, Watanabe, & Fujita, 2013). Although this may suggest that nonhuman primates are the best candidates for studying metacognition, inter-species differences exist among primates as well. The best example of this is the comparison of rhesus monkeys and capuchin monkeys, an Old World and New World species, respectively. Whereas macaques monitor perceptual uncertainty, seek information proficiently, assess memory strength, and engage in prospective and retrospective monitoring, capuchin monkeys struggle with these same tasks. They do not escape difficult trials as readily as macaques (Beran, Smith, Coutinho, Couchman, & Boomer, 2009), and they fail to seek information efficiently, often acting to gain redundant information or failing to get what is needed to perform well (e.g., Basile et al., 2009). A major question is why this happens, with some evidence that it is because capuchins are more risk tolerant, and thus may be less attuned to uncertainty and less sensitive to knowledge states when the opportunity to make responses is prepotent. For example, when given a difficult task with only two choices, macaques avoid such tests, but capuchins take those tests. However, when there are six choices, and the chance level is smaller (i.e., risks of error are much higher), then some capuchin monkeys begin to escape such trials (Beran, Perdue, & Smith, 2014). These results, and the lack of data from many nonhuman primate species such as baboons, lemurs, and other New World monkeys, prevent any strong conclusions from being made about the universality of metacognition in nonhuman primates. It is perhaps likely that some species may show consistent patterns of metacognitive responding across task types whereas others will be more constrained to only performing well in certain kinds of tasks. This would highlight that metacognition is not an all-or-none capacity, but perhaps a more continuous psychological state in which more or less control over responding is possible depending on environmental factors and individual/species factors.

Language and Symbol Use

Perhaps of all areas in animal cognition research, none rival studies of animal language for the controversy they evoke and for the extent to which some of the resulting data most “threaten” a sense of human uniqueness. There has been a longstanding recognition that other species engage in various form of communication, from chemical signaling to aspects of birdsong or other forms of vocal communication. These forms were accepted as being hardwired, but also as a means of communicating specific forms of information (e.g., territory defense, reproductive availability, or perhaps even emotional state in the face of predator presence). What was withheld from this acceptance of animal communication was any type of symbolic representational content that would approximate human language. Philosophers, biologists, and most early comparative psychologists argued that animals did not communicate symbolically, and that they could not communicate about things that required representation of stimuli rather than behavioral responses to present stimuli. In other words, they did not symbolically represent their worlds, and thus could not communicate symbolically.

This idea came under strong challenge in the middle of the 20th century, although earlier reports offered some clues that perhaps some species could engage in symbolic representation. However, it was work on ape language that initiated a new pursuit of the question of animal language. These ape language studies have been extensively detailed elsewhere (e.g., Rumbaugh, 1977; Savage-Rumbaugh, 1986) as have critiques of that work (Hixson, 1998; Ristau & Robbins, 1982; Sebeok & Rosenthal, 1981; Terrace, Petitto, Sanders, & Bever, 1979). What I think was learned in terms of the performance of apes is that they can engage in some forms of symbolic communication. Sign-language-trained chimpanzees and an orangutan (Gardner & Gardner, 1969, 1984; Miles, 1978) made requests and in some cases responded to queries using signs, although concerns about this approach were presented. Later work with chimpanzees moved toward other symbol systems such as tokens (Premack, 1976) and lexigram symbols that chimpanzees and bonobos came to use to engage in “conversations” with humans and with each other (Rumbaugh, 1977; Savage-Rumbaugh, 1986; Savage-Rumbaugh, Rumbaugh, & McDonald, 1985).

The lexigram work was the longest-running program of research and showed three important features in common with how human children learn language. First, such symbolic communication best emerges when the participant is immersed in a relevant, continuously changing environment in which being able to produce and comprehend symbols and speech provides clear advantages in anticipating and reacting to those changes. Language is needed because environments are dynamic, and unpredictable, and language provides control. Apes raised by humans to engage with those humans and each other so as to navigate and control their environment learned hundreds of symbols and came to comprehend speech at the level of a 2- to 2½-year-old child (Savage-Rumbaugh et al., 1993). Second, this level of proficiency is not obtained through trial-by-trial training, although such regimented training can lead to important new capacities for communication between individuals, as was originally shown with the chimpanzee Lana (Rumbaugh, 1977), and was later shown to be a productive means of aiding humans with severe language delays (Adamson, Romski, Deffebach, & Sevcik, 1992; Romski, Sevcik, Robinson, & Bakeman, 1994). Third, this lexigram system and speech comprehension ability in these apes then opened the door to other capacities that could be demonstrated because symbolic communication was possible. I have noted that apes can report what they remember seeing (e.g., Menzel, 1999), and they can also communicate and coordinate behavior to solve problems that require the exchange of information, such as requesting tools that are needed (Savage-Rumbaugh, Rumbaugh, & Boysen, 1978), and they can use symbols as a means of announcing future intentions such as where they want to go (Menzel, Savage-Rumbaugh, & Menzel, 2002).

Other animal language studies have made important contributions to how we think about what language is, and how it is used. Research with dolphins (e.g., Herman, Kuczaj, & Holder, 1993) and sea lions (Schusterman & Gisiner, 1988; Schusterman & Krieger, 1984) showed referential abilities in these species, as they could respond to arbitrary commands or engage in tests where symbolic questions were posed to them. Grey parrots, which can produce vocal responses that are human words, have shown many abilities, such as naming or responding to queries about the color, number, or identities of things shown to them, and they do so in highly flexible and appropriate ways, even in novel tests and with novel stimuli (see Pepperberg, 2009). Moreover, that work also showed that a key component of human language acquisition—seeing and hearing modelers who use language around the learner—is also the central aspect of learning in parrots. They, too, need modelers who show them how to make utterances, respond to utterances, and engage in the bi-directional aspects of language.

Taken together, these approaches converge on the idea that other species can communicate sophisticated information to each other, and in some cases can engage in symbolic, representational communication. Whether these capacities deserve the label “language” will remain a debated question, but the answer is less important than the recognition that what animals can do serves as an important part of understanding what language is, how it functions, and what selective pressures and adaptations led to the ubiquity and nearly unbounded capacity for using language by humans that has opened so many possibilities for our social, economic, and scientific advances.

Conclusions

To understand cognition requires knowing more than how some adult humans process information and make decisions. It requires studying such cognitive processes across development and across cultures. And, it requires studying the mental lives of animals as they can be understood through the behaviors they exhibit. Researchers who engage in such comparative cognitive science have discovered many similarities in these processes across species. There are also important differences, and it is this phylogenetically rich approach to studying behavior and cognition that best reveals the evolutionary foundations of human cognition. At the same time, these efforts reveal the selective pressures and environmental factors that elicit some kinds of behaviors and some cognitive processes, but not others. Furthermore, studying animal minds through observing animal behaviors tells us more about those species, and perhaps how we might come to better share the world with them.

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