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DARWIN, TINBERGEN, AND THE EVOLUTION OF COMPARATIVE COGNITION
A chapter for The Oxford Handbook of Evolutionary Comparative Psychology
Sara J. Shettleworth
Department of Psychology, University of Toronto
Shettleworth 2
ABSTRACT
Darwin and Tinbergen represent two enduring contrasts in comparative cognitive
psychology: in the types of behaviors studied and the kinds of explanations sought. Darwin
encouraged the search for human-like behaviors in animals as evidence for evolutionary
continuity of mental processes. Tinbergen encouraged the careful causal analysis of animal
behaviors as such and eschewed interpretations in terms of anthropomorphic processes. The
Darwinian program has reemerged in contemporary research on comparative cognition. Its
development and relationship to other areas of behavioral biology are traced. In using behavior
as a window onto the animal mind, it is important to remember the lessons of Tinbergen and
like-minded behaviorists in psychology. Several of the challenges that arise in attempting to
show that other species share complex cognitive processes with humans are discussed in the light
of the contrast represented by Darwin and Tinbergen, as are examples of how these approaches
are being productively integrated. (150 words)
Keywords. Darwin; ethology; behavioral ecology; comparative psychology, history; cognitive
ethology; associative learning
Author’s contact information: shettle@psych.utoronto.ca
100 St. George Street, room 4020, Toronto, Ontario M5S 3G3 Canada
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DARWIN, TINBERGEN, AND THE EVOLUTION OF COMPARATIVE COGNITION
INTRODUCTION (h1)
Darwin and Tinbergen in the title of this chapter represent two enduring contrasts in
comparative cognitive psychology: in the types of behaviors studied and the kinds of
explanations sought. This chapter begins with some historical background, from the line of
research that began with Darwin to a sketch of how several subfields in the biology of mind and
behavior are converging in the contemporary comparative study of cognition. This convergence
is a source of much that is exciting and new, but it can also be a source of misunderstanding and
controversy. Some of the challenges that result have their roots in the contrasting approaches
identified in the title, as discussed in the second half of the chapter.
Darwin is in the title because in The Descent of Man and Selection in Relation to Sex
(Darwin, 1871) he set an agenda for studying animal minds that to a remarkable degree is still
being played out. By focusing on human-like behaviors in other species, it encouraged
explanations that were often dangerously anthropomorphic in interpreting human-like behaviors
as produced by human-like thought without properly considering alternatives. Tinbergen
represents the big chunk of the 20th century in which the prevailing approach in both biology
and psychology was a reaction to anthropomorphism in its devotion to careful causal analyses of
animal behavior as such, much of it not terribly human-like but nonetheless important in the lives
of the creatures concerned -- homing in wasps or courtship in gulls and sticklebacks. Recently
there has been a big swing back toward the Darwinian program of looking for human-like
cognitive abilities in other species - tool use, planning (this volume Chapters 12 and 13), social
deception, and so on. This approach is fraught with old intractable problems. Some of them arise
because in the excitement of using behavior as a window onto the mind, it’s easy to forget the
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lessons of Tinbergen and Skinner about the importance of present cues and past history in
controlling it.
The contrast represented here by Darwin and Tinbergen is sometimes referred to as that
between anthropocentric and ecological approaches (Kamil & Mauldin, 1988; Shettleworth,
1993), that is, the study of animal behaviors in relation to human behavior vs. the study of
behavior in its ecological and evolutionary context. Of course, as a keen naturalist, Darwin
recorded many observations of behavior that are simply fascinating in their own right, as in his
experiments on the sensory abilities of earthworms (Darwin, 1881). Nevertheless, as discussed
more in a moment, his role in the history of comparative cognition was to focus it on human-like
accomplishments of other animals. Tinbergen and other ethologists, in contrast, were primarily
concerned with how animals do what they do in nature. Indeed, one of the ways in which early
ethologists defined their field as the biological study of behavior, distinct from animal
psychology, was by its focus on natural behaviors of diverse species - insects, birds, and fish as
much as mammals (Burkhardt, 2005). In addition, Tinbergen (1963) famously identified four
kinds of questions that can be asked about any behavior: questions about its proximal cause, as in
the cues that elicit it; its developmental history; its current function, as in its contribution to
survival and reproduction; and its evolution. The distinctions between these questions need to be
kept clearly in mind so that, for example, understanding the function of a behavior is not
mistaken for understanding its proximal cause, but Tinbergen emphasized that a complete
understanding of any behavior includes answers to all of them. Moreover, the answer to one may
illuminate answers to the others (Sherry, 2005).
Much of the richness and excitement that characterizes the study of animal or
comparative cognition in the early 20th century has come from the participation of people who
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bring knowledge of different species and study them from different perspectives, but the
contrasts symbolized by Darwin and Tinbergen endure. They are expressed not only in the
pervasive tension between anthropocentric vs. ecological or psychological vs. biological
approaches to choice of species and problems but in a perhaps more basic tension between
anthropomorphic vs. “killjoy” behaviorist, or “higher vs. lower” explanations for behavior. This
tension was already well developed when people began studying animal cognitive processes in
the late 19th century, as discussed in the next section.
THE EVOLUTION OF COMPARATIVE COGNITION (h1)
Darwin and the anthropocentric approach (h2)
In On the Origin of Species, Darwin (1859) largely steered clear of the touchy topic of
human evolution. But twelve years later, in The Descent of Man and Selection in Relation to Sex,
(Darwin, 1871) man -- or humanity -- was in the forefront, and right up front he devoted two
chapters to comparing human mental powers to those of other species. At the beginning of
Chapter 2 he writes…. “My object in this chapter is solely to shew that there is no fundamental
difference between man and the higher mammals in their mental faculties.” (Darwin 1871, p.35).
Even though he acknowledges that “no classification of the mental powers has been universally
accepted” he does a great job of providing one. The topics in Chapter 2 include “Certain instincts
in common, emotions, curiosity, imitation, attention, memory, imagination, reason, progressive
improvement, tools and weapons used by animals, language, self-consciousness, sense of beauty,
belief in God, spiritual agencies, superstitions.” In Chapter 3 he goes on to “the moral sense and
the qualities of social animals.” Maybe minus the part on religion, this could be a tour of
contemporary research.
Darwin concludes his review of the evidence as follows: “The difference in mind between
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man and the higher animals, great as it is, is certainly one of degree and not of kind.” (Darwin,
1871, p. 105). Proving this had to be central to Darwin’s agenda because it seems to clinch the
argument that humans are part of the same evolutionary tree as all the other animals. This is an
anthropocentric agenda, but Darwin is often accused (e.g. by Bolhuis & Wynne, 2009) of being
anthropomorphic as well, in the sense of interpreting animal behaviors as reflecting thoughts
similar to those a person would have when engaging in comparable behaviors. Since his goal was
to prove the anthropomorphic case, this is not surprising, but often Darwin was admirably
circumspect. For example, in a passage foreshadowing contemporary speculations that animals
engage in mental time travel (Chapter 15, this volume) he writes, “But can we feel sure that an
old dog with an excellent memory and some power of imagination, as shewn by his dreams,
never reflects on his past pleasures in the chase?” (p. 62, Darwin, 1871). He thus raises the
possibility of what we would now call episodic memory without claiming we can feel sure from
the dog’s behavior that he is reflecting on his past pleasures. Elsewhere, with regard to the
possibility that animals have abstract concepts he refers to “the impossibility of judging what
passes through the mind of an animal” (p.83, Darwin, 1874).
But some of Darwin’s most enthusiastic followers were not always so circumspect.
Gathering anecdotes about clever animals seemed to be an important way to support Darwin, and
some of their excesses of anthropomorphism had an important role in the early history of
comparative psychology (Boakes, 1984). Anecdotes about animals who could open gates and
doors were prominent here, and they invited anthropomorphic explanations, as when Romanes
(1892, p.421) described how his coachman’s cat came to open a door. “First the animal must
have observed that the door is opened by the hand grasping the handle and moving the latch.
Next she must reason, by 'the logic of feelings' - If a hand can do it, why not a paw?" The
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tendency to jump to conclusions about how a clever-looking behavior has developed in a single
subject or what mechanisms underlie it without much evidence one way or another is still with
us. For example, the now world-famous chimpanzee in a Swedish zoo who regularly piled up
stones in the off hours and threw them at spectators when the zoo was open is said to plan
(Osvath, 2009). Planning suggests that gathering stones and piling them on the side of the
enclosure nearest the spectators developed after the animal discovered the rewarding effect of
throwing the stones. But the piles of stones and the way in which the chimpanzee replaced them
when they had been removed were apparently not documented until after stone-throwing became
a problem for the zoo keepers. The possibility that the animal originally began collecting stones
for some reason other than planning to throw them is now untestable but is dismissed
nonetheless (see also Suddendorf & Corballis, 2009).
The excesses of Darwin’s supporters stimulated a killjoy backlash, with effects felt through
most of the 20th century. These include Lloyd Morgan’s Canon (p. 53, Morgan, 1894): “In no
case may we interpret an action as the outcome of the exercise of a higher psychical faculty, if it
can be interpreted as the outcome of the exercise of one which stands lower in the psychological
scale.” The problems with Morgan’s Canon include its assumption of a scala naturae of
“psychical faculties”, the implication that we can judge what is “higher” and “lower”, and
accepting the principle of parsimony in psychological processes (Mitchell, 2005; Sober, 2005).
One reasonable modern interpretation of the Canon derives from the widely accepted principle of
cladistic parsimony (see Sober, 2005): the fact that simple forms of memory and associative
learning have been found in all species tested, including even simple invertebrates (Papini, 2008)
justifies claims that these processes evolved very early, perhaps reflecting universal causal
processes and/or properties of nervous systems, and therefore are present throughout the animal
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kingdom. Thus no special evidence should be required to invoke them for explaining behavior of
a previously unstudied species such as New Caledonian crows or bonobos.
However, one can just as reasonably use such phylogenetic reasoning to argue that species
sufficiently closely related to humans should share cognitive processes other than associative
learning such as reasoning and imagination (Sober, 2005). Evolutionary thinking also supports
suggestions that even species distantly related to humans and apes but facing similar
environmental demands may have convergently evolved similar cognitive mechanisms (Emery
& Clayton, 2004). Nevertheless, the default in contemporary research continues to be the killjoy
assumption that, in the absence of good evidence to the contrary, behavior should be explained
by associative learning plus species-typical behavioral predispositions (Wasserman & Zentall,
2006). Given the ambiguities of even a modern interpretation of Morgan’s Canon, a betterjustified approach may be evidentialism: don’t accept “lower” or “higher” explanations of
behavior without good evidence (Fitzpatrick, 2008; Sober, 2005). Accordingly, as discussed later
in this chapter, one of the biggest challenges for contemporary research is formulating
unambiguous tests for alternative explanations of behaviors suggestive of “higher” processes
such as mental time travel, metacognition, theory of mind, and physical understanding (Heyes,
2008).
E. L. Thorndike (1911/1970; Galef, 1998) was a pioneer in testing alternatives to
anthropomorphic explanations. His studies of how dogs and cats learned to escape from “puzzle
boxes” were directly inspired by anecdotes like those purveyed by Romanes. His contribution
was to do experiments and to show that performances like opening latches can be accounted for
by simple trial and error learning, with no evidence for insight or imitation. Subsequently, with
the notable exceptions of Kohler’s (1925/1959) studies of apes and Tolman’s (e.g. 1948)
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writings, the tendency to look for human-like thought and reasoning in animal problem-solving
went largely underground for most of the rest of the 20th century. In the history of comparative
psychology, the resulting emphasis on studying causes of behavior without speculating about
internal mental processes is usually identified with the growth of behaviorism (Boakes, 1984),
but it was just as characteristic of ethology. For example, at the very beginning of The Study of
Instinct Tinbergen writes, “Because subjective phenomena cannot be observed objectively in
animals, it is idle either to claim or to deny their existence. Moreover to ascribe a causal function
to something that is not observable often leads to false conclusions.” (Tinbergen, 1951, p. 4).
The development of evolutionary comparative cognition (h2)
But a few years after Tinbergen was writing, we began to hear about something called
cognitive ethology from the distinguished biologist Donald Griffin (1976; 1978). “The basic goal
of … cognitive ethology will be to learn as much as possible about the likelihood that nonhuman
animals have mental experiences…..” (Griffin, 1978, p. 528), in other words, to bring animal
consciousness into ethology. Although Griffin updated the information informing his position
until almost the end of his life (Griffin & Speck, 2004), his emphasis on mentalistic
interpretations of animal communication and other behaviors found many detractors (e.g. Yoerg
& Kamil, 1991). After a couple of lively debates in The Behavioral and Brain Sciences (Dennett,
1983; Griffin, 1978) cognitive ethology as such was short lived (see Allen, 2004), inspiring one
published conference (Ristau, 1991a) and some new research on piping plovers (Ristau, 1991b).
Arguably, however, Griffin’s exhortations to animal behaviorists to throw off the shackles of
behaviorism and consider the possibility of human-like thinking even in assassin bugs have
stimulated much of the research discussed in this Handbook.
To some (Allen, 2004; Kamil, 1998), cognitive ethology ought to mean something rather
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broader than anthropomorphic considerations of animal consciousness, namely the role of
cognition as information-processing in the kinds of behaviors of interest to ethologists, what is
now referred to as cognitive ecology (Dukas & Ratcliffe, 2009; Real, 1993). Cognition as
information processing was what psychologists studying animals were getting interested in at
about the same time as Griffin began promoting cognitive ethology, but quite independently. In
the early 1970s, some of these researchers began trying to catch up with the cognitive revolution
that was sweeping the study of human psychology. Cognition in this context refers to the mind as
a receiver and processor of information, whether conscious or not. Indeed, until more recently
consciousness was not an issue even in research on human cognition, which looked mostly at
input-output relations and treated the mind as a black box or a computer. Most of the early
leaders of the revolution in animal experimental psychology were trained in good behaviorist
methods, which they now began using to test rats or pigeons for processes being studied in
humans. The book Cognitive Processes in Animal Behavior (Hulse, Fowler, & Honig, 1978) that
is often seen as proclaiming the beginning of the field of comparative cognition included a pretty
narrow range of topics: conditioning, memory, attention, serial learning, space, time, concepts.
The range of species was even narrower: rat, pigeon, chimpanzee. This research was thoroughly
anthropocentric. Encouragement from the growing field of behavioral neuroscience helped to
perpetuate this focus, as paradigms for testing memory and the like provided “animal models”
for use in studies of neurobiology and ultimately clinical applications (Chapter 14 this volume).
Even as this research developed, however, some psychologists complained, as others had
in the past (Beach, 1950; Hodos & Campbell, 1969), that “comparative” cognition research was
not genuinely comparative. They called not only for looking at more species but also for
studying cognitive processes used to solve information processing problems in nature. A leading
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example of this synthetic (Kamil & Mauldin, 1988) or ecological (Shettleworth, 1993) approach
was the comparative study of spatial memory in birds that do and do not store food. Wild birds
were studied both in the field and in standard laboratory paradigms to test the hypothesis that
reliance on stored food is associated with precise long-lasting spatial memory. This research
soon converged with studies of the neurobiology of spatial memory and the hippocampus to
become a leading example of so-called neuroecology, or the comparative study of brain
mechanisms in relation to differences in ecology (Brodin, 2010; Sherry, 2006). As species that
might be expected to show evidence of episodic memory and planning, food storing birds have
subsequently contributed to research on more general questions about memory and its neural
substrate (Chapter 12 and Pravosudov & Smulders, 2010). Similarly, another specialized
behavior shown by birds in the field, tool using by New Caledonian crows and woodpecker
finches, has led on to experimental analyses of more general processes, in this case mechanisms
involved in solving physical problems, possibly including insight and physical understanding
(Bluff, Weir, Rutz, Wimpenny, & Kacelnik, 2007; Kacelnik & Shettleworth, in preparation).
Another strand in the developing synthetic or, to adopt the title of this Handbook,
evolutionary comparative approach was the growth of behavioral ecology, the subfield of
ethology devoted to Tinbergen’s questions about function and evolution, now incorporating
mathematical models of the effects of behavior on fitness (Cuthill, 2005; Danchin, Giraldeau, &
Cezilly, 2008). Perhaps the biggest success in the early days of behavioral ecology was optimal
foraging theory (see Krebs & Davies, 1981). Predictions about fitness-maximizing decisions
concerning where to forage and for how long, what to eat, and so on were often tested in
situations resembling psychological studies of learning and choice. This resemblance was
exploited in some productive collaborations between behavioral ecologists and experimental
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psychologists (Kacelnik, Brunner, & Gibbon, 1990; Shettleworth, Krebs, Stephens, & Gibbon,
1988). One common finding was that although observed behavior might conform roughly to
predictions of foraging theory, psychological mechanisms such as variable perception and
memory for time intervals did a better job of accounting for the details. More recently some of
this research has converged with behavioral economics in looking at simple mechanisms of
economic decision-making that may cut across species (Kacelnik, 2006; Rosati, Stevens, Hare, &
Hauser, 2007; Schuck-Paim, Pompilio, & Kacelnik, 2004).
Other subfields in the broad study of animal behavior have embraced or converged with
areas of comparative cognition. Cognitive ecology (Dukas & Ratcliffe, 2009; Real, 1993) has
already been alluded to as the study of how cognitive mechanisms evolve and are used in their
natural context. Similarly, sensory ecology (Dusenbery, 1992; Endler & Basolo, 1998) is the
long–standing and well-developed study of animal sensory systems in relation to ecology. The
development, use, and neurobiology of bird song is a subject large and lively enough for its own
conferences and books (e.g. Marler & Slabbekoorn, 2004; Zeigler & Marler, 2004) but with
many links to broader issues in comparative cognition, especially language evolution
(Margoliash & Nusbaum, 2009). Spatial behavior (Gallistel, 1990; Jeffery, 2003) and social
learning (Galef & Heyes, 2004; Heyes & Galef, 1996; Laland & Galef, 2009) are also subareas
of evolutionary comparative cognition with their own thriving interdisciplinary research
communities and conferences. Finally, as evidenced by several chapters in this Handbook, longterm observations and clever field experiments on primates and some other species (e.g. Cheney
& Seyfarth, 2007; de Waal & Tyack, 2003) have raised important questions about the cognitive
processes involved in communication, tool use, social transmission of behavior, and so on.
Laboratory studies of chimpanzees and other primates suggested by such observations
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sometimes include human children, thus addressing shared mental powers very directly (e.g.
Herrmann, Call, Hernández-Lloreda, Hare, & Tomasello, 2007). Indeed, the recent explosion of
data on all aspects of animal cognition underpins several recent reexaminations of Darwin’s
claim that humans differ mentally in degree but not in kind from other species (Penn, Holyoak,
& Povinelli, 2008; Premack, 2007; Tomasello, Carpenter, Call, Behne, & Moll, 2005).
As a result of all of these developments, the study of comparative or animal cognition in
the broadest sense now embraces three major sets of cognitive processes about equally,
addressing them in species from ants to chimpanzees and humans (Shettleworth, 2010). Basic
mechanisms of perception, memory, associative learning, discrimination learning, and
categorization cut across all kinds of content. Although comparative psychologists have been
studying some of them since Thorndike’s time, research in these areas continues to develop.
Understanding these basic mechanisms is essential to appreciating how cognition in specific
physical or social domains may or may not be specialized. The latter processes of acquisition,
representation, and behavioral control, or cognitive modules, are defined largely in terms of their
functions, what aspects of the world they are about. Physical cognition includes time, space,
number, and instrumental learning, topics which have been studied for quite a long time within
experimental psychology, as well as tool using and planning. Social cognition includes social
knowledge, that is what animals know about their social companions and how they come to
know it, and the cognitive processing evident in animal communication systems, topics discussed
in several chapters in this Handbook. Thus, compared to 25 or 30 years ago, the whole field of
comparative cognition encompasses a much broader set of problems, being studied in a very
wide sample of species. They are also being studied by a very diverse community of researchers
- people trained not only in psychology but behavioral ecology, ethology, anthropology,
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sometimes human cognitive psychology and child development, not to mention philosophy. This
diversity undoubtedly contributes to making the field so rich and interesting, but it also can make
for misunderstanding and controversy. Some of the challenges are discussed in the next part of
this chapter.
CHALLENGES(h1)
From anthropomorphism to behavioral tests (h2)
When a chickadee stores a sunflower seed, is it planning ahead? When a vervet monkey
emits an alarm call, does it want to inform its companions a leopard is near? More generally,
when another animal does something that looks human-like, is that behavior evidence for the
same cognitive processes underlying analogous behavior in people? How would we tell? This
last question is especially challenging when dealing with processes documented in humans
primarily by verbal report and assumed to be associated with particular states of consciousness
(Heyes, 2008; Suddendorf & Corballis, 2009). The challenges range from minimal to virtually
intractable. Many are illustrated by research discussed in other chapters of this Handbook, which
can be consulted for thorough reviews of topics touched on here.
Animal memory and functional similarity (h3)
People often report what they remember by talking about it, but all species exhibit
memory nonverbally. In general, if behavior depends on past experience of a particular object or
event, the animal is said to have a memory for it. For instance, Darwin (1871) supported his
argument for long-lasting memories in animals with the observation that when he returned home
from his 5-year voyage on the Beagle, his dog greeted him as if he had never been away. Some
contemporary research similarly exploits the subtle and detailed memories that animals form
spontaneously, as in rats’ memory for the location and context where they encountered particular
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objects (Eacott & Norman, 2004). Even when testing behaviors requiring extensive training, as
in comparisons of serial position effects across monkeys, pigeons, and people (Wright, 2006),
the goal is to examine functional similarities among patterns of data resulting from comparable
manipulations (Heyes, 2008). Looking for functional similarities means looking for evidence that
behaviors and independent variables are related in the same way across species. Obtaining such
data may require testing humans in novel ways. Thus, to discover whether monkeys and pigeons
show serial position effects in memory for lists of visual items, the animals were presented with
four trial-unique images in sequence. Memory was probed with a single item, and reinforcement
was given for correctly indicating whether it had been in the list or not. Since the items had no
meaning for the animal subjects, comparable data were obtained from human subjects by testing
them with kaleidoscope patterns. All three species and others tested later show dynamic serial
position effects in serial probe recognition tests, with memory for the most recent items
dominating at short retention intervals and memory for the earliest ones at longer intervals.
Although the precise retention intervals at which recency and primacy are seen vary with
species, the common pattern is evidence for a common memory process (Wright, 2006). Here,
nothing need be said about consciousness.
Metacognition and awareness (h3)
The comparative study of metacognition, or awareness of one’s own cognitive processes
(see Chapters 12 & 15, this volume) contrasts with the comparative study of serial position
effects in that it is addressed to a process that is usually conscious in humans. Metacognition
strictly speaking implies second-order representation, cognition about cognition, rendering
controversial any claim to demonstrate it in a nonhuman species (e.g. Carruthers, 2008; Penn et
al., 2008). As with serial order effects, studies of metacognition include numerous reports of
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identical patterns of data from human and nonhuman (usually rhesus monkey) subjects in
identical tests (e.g. Kornell, 2009; Shields, Smith, Guttmanova, & Washburn, 2005; Smith,
Shields, Allendoerfer, & Washburn, 1998; Smith, Shields, Schull, & Washburn, 1997). Many
such tests involve near-threshold perceptual discriminations in which subjects may either classify
stimuli into one of two categories or opt out of trials by choosing a third, “uncertain”, response.
Monkeys, dolphins (Smith et al., 1995), pigeons (Sole, Shettleworth, & Bennett, 2003), and
people typically make this third response most often where the stimulus is near the threshold of
discriminability. This pattern can be explained by signal detection theory and/or responding
based on the relative probabilities of reward for the three options at each point along the stimulus
continuum (Hampton, 2009; Jozefowiez, Staddon, & Cerutti, 2009; Smith, Beran, Couchman, &
Coutinho, 2008; Sole et al., 2003). Thus although people say they feel uncertain when they opt
out of trials, the functionally similar behavior of other species need not be mediated by a
conscious state of uncertainty. Turning the argument around, on some accounts human
metacognitive responses may not be mediated by consciousness either (Carruthers, 2008; Koriat,
Hilit, & Nussison, 2006).
The existence of “simpler” explanations here does not rule out the possibility that some
sort of metacognitive awareness plays a role since both are compatible with the observed results
(Hampton, 2009). Arguments like those just sketched have therefore challenged investigators to
devise other tests to isolate evidence of processes closer to what is implied by metacognition in
people, that is to find a situation in which the anthropomorphic “higher” explanation predicts a
different outcome from one based on sensitivity to reward rates and external (or public,
Hampton, 2009) cues. The current best candidate is the test of metamemory used by Hampton
(2001) with rhesus monkeys (see Hampton, 2009), although some claim that other approaches
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qualify (e.g. Smith, Beran, Redford, & Washburn, 2006). In any case, since we cannot get from
other species the sorts of verbal reports that we commonly rely on for evidence of consciousness
in humans, cleverly delineating functional similarities may be as far as we can go. Even in the
rare cases when other forms of behavioral control are ruled out, we still cannot know how
closely other species’ mental states resemble our own (see e.g. Hampton, 2005; Heyes, 2008).
Episodic memory: the challenge of multiple definitions (h3)
When other species cannot be tested in the same way as humans, new challenges arise.
Research on episodic memory in animals (Chapter 15) provides examples. Episodic memory was
originally (e.g. Tulving, 1972) defined as memory for one’s own experiences, as distinct from
memory for facts and ideas (semantic memory). But the definition evolved to include autonoetic
consciousness, the sense of reexperiencing an episodically recollected event, mentally traveling
back in time to it (Tulving, 2005). Since we are unlikely ever to know if animals travel anywhere
mentally, comparative researchers have fallen back on the original definition and sought
evidence that animals have integrated memories for unique events, sometimes called whatwhere-when memories (see Crystal, 2010). . Accordingly, when Clayton and Dickinson (1998)
reported the first demonstrations of such memory, using Western scrub jays’ memory for their
food caches, they scrupulously referred to the birds as having “episodic-like” memory. Clayton
and colleagues went on to show that the birds’ memory for what they had cached where and how
long ago had other properties attributed to episodic memory. For instance it is a single integrated
representation of the caching episode that can be used in a flexible way (see Chapter 12, this
volume).
But not all agree that the essential features of episodic memory are well captured by the
studies with scrub jays. For instance, the animals were trained, albeit not very extensively, to
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expect certain kinds of tests, making the paradigm essentially one of conditional discrimination
learning (Roberts, 2002). How long ago a caching episode had occurred was a cue to what items
would be edible at test, whereas “when” in human episodic memory is typically a definite time in
the past such as “last Monday.” In a paradigm logically equivalent to that developed by Clayton
and Dickinson, rats indeed encode “how long ago” rather than “when” (time of day; Roberts et
al., 2008), but they can also use time of day when required to (Zhou & Crystal, 2009). It has also
been argued (e.g. by Eichenbaum, Fortin, Ergorul, Wright, & Agster, 2005) that the ”when” in
episodic memory need not be the time at which something occurred but its entire spatio-temporal
context. Accordingly, a rat’s memory for the location of an object in a particular spatial context
(Eacott & Norman, 2004) qualifies as episodic-like, as does memory for the position of an odor
in a sequence of odors (Eichenbaum & Fortin, 2005). Performance in tasks of the latter type,
which exploit rats’ outstanding ability to discriminate and learn about odors, shares an
impressive number of other properties with verbal memory, including effects of hippocampal
lesions (Fortin, Wright, & Eichenbaum, 2004).
Another issue is that with human subjects episodic memory is typically probed
unexpectedly, so subjects cannot prepare for the test at the time of memory encoding.
Accordingly, some clever tests with pigeons have shown that they can “answer unexpected
questions” about what they just did (Zentall, Clement, Bhatt, & Allen, 2001). But because these
tests probe only very short term memory, they in turn lack another property generally attributed
to episodic memory, namely that it is a species of long term memory (cf. Hampton & Schwartz,
2004). As another approach, it may be possible to study naturalistic examples of the spontaneous
use of episodic-like memory that do not involve food-caching. One candidate is the male
meadow vole’s ability to recall where and when he encountered a female about to come into
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estrus so he can time his next visit appropriately (Ferkin, Combs, delBarco-Trillo, Pierce, and
Franklin, 2008). In any case, a wide variety of tests for episodic memory is used by students of
human cognition. Studies of comparatively short term memory for stimuli encountered in the
laboratory may be labeled studies of episodic memory (Kohler, Moscovitch, & Melo, 2001)
equally with studies of subjects’ rich recollections of personal experiences (Addis, Wong, &
Schacter, 2007). One potential contribution of research with nonverbal species is to focus
attention on behavioral criteria for episodic memory in humans (Crystal, 2010). The best current
conclusion from comparative research seems to be that memory in nonhuman species shares
numerous features with episodic memory in humans, but no single example or species
necessarily shares all of them, and autonoetic consciousness may be unique to humans (Crystal,
2009; Suddendorf & Corballis, 2009).
Future planning, tool use, and folk psychology (h3)
Memory presumably evolved not so creatures could ruminate about their pasts but so
they could use past experience to determine future behavior. Reasonable as this idea seems, only
recently has it been incorporated into conceptions of human episodic memory. Planning, or
mental time travel into the future, is now seen as the flip side of mental time travel into the past.
Both involve autoneotic consciousness and depend on closely similar parts of the brain (Addis et
al., 2007). Research with other species has followed, with attempts to demonstrate that animals
show future planning (chapters 12 & 13 and Suddendorf & Corballis, 2009). Two problems
bedevil this research. The first is that animals have many kinds of behavior that prepare them for
the future but which can be accounted for without assuming any representation of the future as
such or even requiring specific past experiences (Roberts, 2002). Examples include migrating,
hibernating, storing food, building nests, responding to learned signals about delays to food. The
Shettleworth 20
second is that relevant research on planning or future time travel in people, including how it
develops in young children, is at an early stage, leaving researchers studying animals with little
more than folk psychology to guide the formulation of behavioral definitions. As a result, the
crucial features distinguishing behavior based on some sort of representation of the future as
such from other kinds of future oriented behavior are still debated. Suddendorf and Busby (2005)
proposed that a behavior that was novel and that was performed in the service of a motivational
state other than the one the animal was in at the moment, as in gathering food when not hungry,
would be evidence of future planning. But with the accumulation of findings fitting these criteria,
some have seen a need for additional specifications. For instance, domain-generality (Premack,
2007) rules out adaptations of particular behavior systems such as food-storing. Being performed
on a single test (Suddendorf & Corballis, 2009) rules out gradual learning through delayed
reward. Specificity to a particular future time (Roberts & Feeney, 2009) implies a conception of
the future as such.
Such criteria seem to rest more on appeal to folk psychological, anthropomorphic,
conceptions of planning than on parallel data from humans. Similarly, in a purported
demonstration of planning by apes (Mulcahy & Call, 2006), the animals had multiple
opportunities to collect a tool that could not be used right away, but in most cases successful
trials were scattered more or less randomly across all trials. Folk psychology would seem to
suggest that once an animal had discovered the rules of the game it would plan on every trial, so
is this or is this not evidence of future time travel (Shettleworth, 2010; Suddendorf & Corballis,
2008)? In any case, folk psychology is not necessarily a good predictor of how people behave
when tested like other animals. For instance, when people are given tests similar to those failed
by apes, they may behave irrationally too. Human subjects avoid a tool that brings a reward close
Shettleworth 21
to a hole or trap, even when it will not fall in (Silva, Page, & Silva, 2005), and like apes they
may choose to pull a string contacting but not actually connected to a reward (Silva, Silva,
Cover, Leslie, & Rubalcaba, 2008).
Varieties of proximal cause (h2)
Translating anthropomorphism into unambiguous behavioral tests challenges researchers
to isolate the specific cognitive mechanism of interest from other possible causal factors for
observed behavior. This can be entirely straightforward and uncontroversial, as in research on
nonverbal numerosity discrimination, i.e. discrimination among arrays of visual stimuli, strings
of sounds, and so on according to the numbers of items they contain. Here it is necessary to rule
out reliance on some correlated feature such as amount of space or time the items occupy (for
review see Shettleworth, 2010). Only if discrimination remains accurate with novel items
varying in shape, size, color, contour length, and so on, are subjects said to be displaying
evidence of sensitivity to numerosity. This need not mean that control by nonnumerical features
is entirely absent (see Cantlon & Brannon, 2005), but the strategy of establishing one sort of
control by ruling out alternatives works well here. Sometimes, however, in the enthusiasm for
using behavior as the readout of cognition other kinds of proximal causes for a target behavior
can be overlooked. What Skinner and Tinbergen taught us about the importance of past history
and present cues is not obsolete just because cognition is being tested. In the spirit of
Tinbergen’s four kinds of causes of behavior, even when looking only at proximal causes, there
may be more than one kind of answer.
A classic example comes from an early experimental study of theory of mind in
chimpanzees. The animals could choose to beg for hidden food from one of two people when
one had seen it hidden and one had not. To test whether their choice of the former individual was
Shettleworth 22
based on sensitivity to the person’s knowledge state (i.e. theory of mind), the animals were given
a series of rewarded tests in which one observer had a paper bag over his head, a novel cue to
ignorance (Povinelli, Nelson, & Boysen, 1990). Overall, the animals tended to beg from the
observer who could see, a result consistent with theory of mind. on But rewarded tests are
learning trials, and closer analysis showed that the animals chose randomly on the first two tests
but learned to use the novel cue during the next few trials ( Povinelli, 1994). Similarly in recent
studies of dogs’ sensitivity to human social cues, the possibility of learning during a short series
of test trials may be dismissed too readily (Udell, Dorey, & Wynne, 2010). This is especially so
because a plausible alternative to a preexisting or very early-developing theory of mind in dogs
is that during domestication they have evolved an exceptional sensitivity to and ability to learn
about human behavior (Reid, 2009).
Another example comes from a simple test for the spontaneous use of metamemory
devised by Call and Carpenter (2001) for apes and children and then used by Hampton and
colleagues (Hampton, Zivin, & Murray, 2004) with rhesus monkeys. In the latter version, the
monkey watches as an experimenter places a treat in one of four horizontal tubes and is then
allowed to tip up one tube so the reward slides out. By bending over, the monkey can peer into
the tubes before choosing, a slightly effortful behavior that is unnecessary when the opportunity
to choose a tube comes immediately after witnessing baiting. But when baiting is done behind an
opaque screen, the task becomes a test of metamemory: if they are aware they have no memory
of a recent baiting, monkeys should more often look before choosing. The possibility that
looking on trials with the screen was acquired because it was rewarded more frequently than not
looking under this condition was ruled out by familiarizing the monkeys with the elements of the
Shettleworth 23
task, pulling and looking, before introducing the opaque screen. When the opaque screen was
then introduced, most monkeys looked more often right away.
But the proximal cause of looking here is not necessarily awareness of memory strength,
i.e. metamemory. An old fashioned ethological causal analysis readily shows that the monkeys’
behavior can be accounted for in terms of conflict between two competing behavioral tendencies
(see Hampton, 2009). Of the two responses learned before the test, pulling and looking, correct
pulling leads to reward with a shorter delay. Because monkeys were well trained on this response
initially, it is likely to be elicited when baiting is seen. With baiting unseen or forgotten the
tendency to pull should be weaker, if only because the normal cues that elicit it are absent.
Additionally, regardless of whether baiting was seen or not, looking is rewarded only after the
delay necessary to locate the baited tube. But when baiting is unseen and the tendency to pull
therefore weak, looking will emerge as the stronger response. In this way appropriate
information-seeking -- metacognitive behavior in a sense -- need not depend on explicit
awareness of memory strength. The same may be true of people in similar situations (Carruthers,
2008; Hampton, 2009; Kornell, 2009). And the requirement for viewing tests of “higher”
cognitive capacities from the animal’s point of view, in terms of the cues present, their past
histories, and the relative strengths of responses they elicit by virtue of training and/or evolution
transcends these few examples. In such cases it may not be necessary to assume any
representations of memory strength or similar higher-order causal factors are present. This is
similar to the point made repeatedly by Povinelli and his colleagues about tests of theory of mind
or physical understanding (e.g. Penn & Povinelli, 2007): animals may behave effectively on the
basis of representations of observable cues, without representing unseen mental or physical
causes. Examples from animal problem-solving are discussed later in this section.
Shettleworth 24
Function and mechanism and different ways of using language (h2)
Many interdisciplinary fields bring together people who use the same words in different
ways. In comparative cognition, this often means using terms that have both functional and
mechanistic definitions, leading to disagreement and debate when people understand different
things by them. In cognitive science, this is the problem of confusing functional and
representational explanations (Penn & Povinelli, 2009). In various guises, the same problem is
widespread in the study of animal behavior. In Tinbergen’s writings and subsequent discussions
(Bolhuis & Verhulst, 2009), it is the confusion of function with cause. As discussed by John
Kennedy (1992) in his insightful little book The New Anthropomorphism, such confusion is
particularly insidious in behavioral ecology, where the tendency to label behaviors
anthropomorphically – for example “rape”, “deception”, “strategy – creates the illusion that they
can be explained by the same conscious processes that accompany analogous behavior in people.
Even when such behaviors have a formal definition within behavioral ecology, labeling them
with terms current in ordinary language and/or other fields can lead to unnecessary
disagreements. One example comes from recent claims to demonstrate animal teaching (Hoppitt
et al., 2008; Shettleworth, 2010).
Teaching has had an accepted functional definition in evolutionary studies of behavior for
nearly 20 years (Caro & Hauser, 1992). To qualify as teaching, an animal must modify its
behavior in the presence of naïve individuals in a way that makes possible or speeds up their
learning, and it must do so at some immediate cost to itself. This last criterion suggests that, as
seems to be the case, teaching may be rare but that when it is seen those taught will be relatives,
thus eventually conveying a fitness benefit to the teacher. Notice that this definition implies
nothing about whether the teacher understands the pupils’ need to learn, only that some cues
Shettleworth 25
from potential pupils elicit appropriate and costly behavior from the teacher. This is exactly what
was shown in an elegant demonstration that meerkats teach their young how to handle scorpions
(Thornton & McAuliffe, 2006). When pups are very young (and hence inexperienced), as
indicated by their begging calls, adults present them with dead or disabled scorpions. As the pups
mature (again indicated by their begging calls) adults bring scorpions that need more and more
processing. Adults also engage in other time-consuming behaviors that ensure the young learn to
catch and disable scorpions for themselves. That the young do in fact learn from the experience
so provided was shown by the beneficial effects of experimentally providing extra scorpions.
The adult meerkats’ behavior thus meets all the accepted functional criteria for teaching, but for
those to whom “teaching” means human pedagogy, labeling it as such is deeply controversial
(Csibra, 2007; Premack, 2007). In the context of behavioral ecology the adult meerkats’ behavior
could be described as governed by “rules of thumb” such as “when hearing calls of very young
pups, bring dead scorpions.” In ethological terms the calls are sign stimuli, but either way the
adults teach effectively in the absence of any representation of the pups’ understanding or need
to know.
Sampling in optimal foraging theory also exemplifies the sort of term discussed by
Kennedy (1992) in that it has a functional meaning in a certain context but implies an
anthropomorphic mechanism. Classic models of optimal foraging indicate that to choose the best
food item or foraging patch, individuals should sample, gathering information that might be
useful when conditions change even if that sometimes means abandoning the currently best
option. The notion that animals might sample in this way seems to fly in the face of the wellestablished finding from psychology laboratories that animals strongly prefer immediate
rewards. In terms of reinforcement theory, if Patch A is currently paying off at the highest rate
Shettleworth 26
available, animals should never choose Patch B except by mistake. Shettleworth, Krebs,
Stephens, and Gibbon (1988) pitted these accounts against each other in an operant study with
pigeons that simulated two foraging patches, one of which was stable and one of which changed
abruptly in payoff rate from time to time, sometimes becoming much better than the stable patch.
The optimal strategy is to sample this fluctuating patch with a specifiable frequency depending
on such things as the reward rate in the stable patch (Stephens, 1987). The birds did in fact
sample at roughly the predicted frequency in several experimental conditions, but their behavior
was best accounted for by a general model of instrumental choice (Gibbon, Church, Fairhurst, &
Kacelnik, 1988) in which animals always choose the option perceived to predict the shortest
delay to reward based on a fuzzy memory of the delays associated with each option present.
Thus animals can “sample” approximately optimally but without doing so in the
anthropomorphic sense of deliberately rejecting the shortest current delay to reward in order to
collect information that is useful in the long term.
This same timing model of choice between reinforcement schedules accounts for
behavior in a variety of other laboratory tests of optimal foraging (e.g. Kacelnik & Bateson,
1996; Kacelnik et al., 1990). More recently the analysis of animal foraging decisions has evolved
to connect with studies of economic decision making. In studies of analogues to the sunk costs
effect and influences of irrelevant alternatives on choice, other species make the same sorts of
“irrational” decisions as do humans, and again the results can be explained by general
mechanisms of reinforcement and choice (Bateson, Healy, & Hurly, 2003; Kacelnik & Marsh,
2002). As discussed in depth by Kacelnik (2006) such work integrates the contrasting but
interrelated approaches to rationality in evolutionary biology, economics, and psychology.
Clever animals and killjoy explanations (h2)
Shettleworth 27
Explanations of apparently complex processes as the product of simpler ones, as in
animal teaching and sampling behavior, sometimes seem to have a different status in
comparative cognition than elsewhere in biology. As Darwin so eloquently argued in The Origin
of Species, the whole wondrous complexity of organic evolution can be explained as the outcome
of simple processes, here variation, selection, and inheritance. Similarly, collective behavior of
animal groups as in the exquisite regulation of a honeybee colony or the coordinated movements
of schooling fish is increasingly well understood as the result of local responses by myriads of
individuals (Couzin, 2009). It is hard not to be in awe of how a perfectly air-conditioned termite
mound can be constructed without any instructions from a termite architect. But explaining
behaviors that look as if they require human-like thought in terms of simple processes such as
associative learning and species-typical predispositions seems tantamount to denial of mental
continuity between humans and other animals. Labeling such explanations as “killjoy” originated
in Dennett’s (1983) analysis of levels of intentionality in cognitive ethology. In his principal
example, a monkey’s alarm call might reflect wanting its fellows to move away, wanting them to
know a predator is approaching, or even higher levels of intentionality. The killjoy alternative is
that the sight of a predator reflexively elicits calling. One of Dennett’s central points was that
because in principle they can be put to the test with experiments and opportunistic observations,
the mentalistic claims of the cognitive ethologists should not be dismissed out of hand.
Characterizing first- and higher-order intentionality as “exciting” explanations, in contrast with
“killjoy bottom of the barrel” zero-order or reflexive explanations may have been no more than a
device for emphasizing the important potential contribution of cognitive ethology, but the
implication that complex or higher-order explanations of apparently cognitively demanding
behavior should be valued more than simpler ones encourages a tendency implicit in some
Shettleworth 28
contemporary research reports to dismiss accounts based on simple learning mechanisms before
thoroughly testing them.
Research on the possibility of insight in animals provides some illustrations. Controversy
over whether animals solve problems insightfully dates from the earliest days of comparative
psychology (Boakes, 1984). Thorndike’s (1911/1970) conclusion from his puzzle box
experiments – that only trial and error learning was involved – was in turn put to the test by
Wolfgang Kohler (1925/1959) in his studies of chimpanzees using sticks or climbing on boxes to
reach inaccessible food. Some animals did appear to solve problems insightfully, but there was
much evidence for trial and error learning too (Kacelnik & Shettleworth, in preparation;
Povinelli, 2000). Subsequent research suggested that experience with sticks, boxes, and the like
contributed to their later successful use (Birch, 1945; Schiller, 1957). Current interest in the
possibility of animal insight (e.g. Bird & Emery, 2009a; Heinrich, 1995) is thus one more swing
of the pendulum.
The definition of insightful behavior most often referred to in this context is that
formulated by the British ethologist W. H. Thorpe (1956): “the sudden production of a new
adaptive response not arrived at by trial behavior” (i.e. trial and error; Thorpe, 1956, p. 100;
italics in original). Similarly, in human problem solving (Sternberg & Davidson, 1995)
(Weisberg, 2006) insightful solutions have three general properties:
1. They appear suddenly, accompanied by a distinctive subjective experience of surprise
and delight, the “aha moment” (Kounios & Beeman, 2009).
2. They usually appear after an impasse, i.e. a period of unsuccessful attempts, rather than
by gradually homing in on the solution through an analytical approach (Weisberg, 2006).
3. They involve restructuring the problem, i.e., approaching it in a new way.
Shettleworth 29
Other than “aha moments’, these criteria correspond well with what is implied by
Thorpe’s definition. The question with both animal and human subjects is whether behavior
meeting these criteria involves a distinctive cognitive process, namely insight. As originally
conceived of by Kohler and other Gestalt psychologists, insight consists of suddenly “seeing” the
solution, a perceptual process like seeing the alternative form of a reversing figure (Mayer,
1995). In contemporary discussions of human problem solving (e.g. Kounios & Beeman, 2009;
Weisberg, 2006) insight contrasts with the analytical approach, examining possible solutions in
the mind’s eye or in actuality until an effective one is found. People readily report whether they
have used insight or analysis to solve even simple verbal problems (Bowden, Jung-Beeman,
Fleck, & Kounios, 2005). Some contemporary students of animal problem-solving (e.g. Bird &
Emery, 2009b) have incorporated mental analysis into their conception of insight, but since we
cannot ask animals whether they mentally tried possible solutions or simply “saw” how to
succeed, this is not only historically and comparatively inaccurate but behaviorally meaningless.
In any case, not all agree that subjective experience is the best guide to mechanism even in
human problem-solving (cf. Weisberg, 2006).
With both animal and human subjects, discovering whether behavior fitting criteria 1-3
above reflects a special cognitive mechanism typically consists of ruling out alternative
mechanisms. For nonhuman species the generally accepted alternative is associative learning, but
the subtle ways in which past learning together with species-typical behaviors and present cues
might generate complex and apparently novel behavior are not always fully appreciated or
rigorously tested. A well-known illustration of how simple forms of learning and other basic
behavioral principles can produce apparently insightful behavior is the demonstration (Epstein,
Kirshnit, Lanza, & Rubin, 1984) that pigeons with appropriate past experience solve a novel
Shettleworth 30
“banana and box” problem just as Kohler’s (1925/1959) apes did, by moving a box and climbing
on it to reach a hanging (toy) banana. All pigeons in the study had previously been rewarded
with food for climbing onto the box and pecking the banana, but the only birds that
“insightfully” solved the problem had also been extinguished for jumping and flying toward the
banana when the box was absent. In addition, in interleaved sessions without the banana they had
been rewarded with food for pushing the box toward a spot at varying locations on the wall. In
the test they looked back and forth between banana and box at first, appearing confused, but then
approached the box and pushed it toward the banana.
Directed pushing and climbing to peck contributed to success through a few simple
mechanisms (Epstein, 1985). First, a basic principle of ethology is that various cues in a situation
may control incompatible behaviors in ways that may change dynamically with behavior. Here,
when the pigeons first confronted the banana and the displaced box, looking back and forth was
the expression of two learned behaviors which were both elicited but could not be completed.
Because the birds had been explicitly extinguished for flying and jumping at the banana,
approach to the box and then pushing it soon predominated. The banana became the target of
pushing in the absence of the spot because both banana and spot had been associated with food,
i.e. through functional (also called mediated) generalization (Urcuioli, 2006). Finally, by
pushing the box toward the banana, the “insightful” pigeons produced the situation (box under
banana) in which climbing had been reinforced. The latter process is what Epstein calls
automatic chaining. Epstein (e.g. 1985) claims that such combining of old behaviors in new ways
underlies human as well as animal creativity. This is a strong claim to be put against claims that
insightful problem solutions involve a special process of insight or physical understanding.
Taking Epstein’s claim seriously means deconstructing situations in which animals solve
Shettleworth 31
problems in apparently novel ways into elements and testing whether experience with these
elements can explain the performances of interest.
Several recent examples involve tool using by birds of the crow family (corvids), both
New Caledonian Crows, which naturally make and use tools, and non-tool using species such as
rooks and ravens, which can solve similar tasks when tested appropriately. In one case (Bird &
Emery, 2009a) rooks were shaped to nudge a stone so it fell down a tube and released food from
an apparatus at the bottom. Remarkably, when stones were no longer provided at the mouth of
the tube, rooks brought stones from nearby. What experience might contribute to the apparently
spontaneous fetching and dropping of stones remains to be determined. A further study using
New Caledonian crows (von Bayern, Heathcote, Rutz, & Kacelnik, 2009) showed that once
experienced pushing stones down the tube, these birds also bring stones to the apparatus, but
they do not require previous experience seeing stones release the food. Birds that had pecked or
used a stick to operate a similar apparatus fitted with a short tube became more likely to bring
and drop stones through the long tube. Exactly what the birds need to learn here remains to be
determined. For instance, they may learn that contact with the mechanism at the bottom of the
tube releases food and for still-unknown reasons generalize this to producing contact with stones.
More extensive analyses of the experiences necessary for New Caledonian crows to use
tools in novel ways have been done with metatool use, i.e. using one tool to obtain another with
which food can be obtained. Metatool use has been claimed to be especially human-like (see
Taylor, Hunt, Holzhaider, & Gray, 2007), and various species of primates can show it (see
Wimpenny, Weir, Clayton, Rutz, & Kacelnik, 2009). Using a metatool effectively on the first
opportunity, before the required chain of behavior could be primarily reinforced, has been
suggested to reveal insight or reasoning (Taylor, Elliffe, Hunt, & Gray, 2010; Taylor et al.,
Shettleworth 32
2007). In the first demonstration of such behavior (Taylor et al., 2007), New Caledonian crows
provided with a stick too short to reach a reward used that stick to obtain a long stick, which they
then used effectively. Notwithstanding the claim in the article’s title this behavior was
“spontaneous”; in fact the birds were trained beforehand on several elements of the problem:
obtaining meat with the long stick, extracting a long stick from a container, and failing to obtain
meat with the short stick. But even though use of the short stick to obtain meat directly was
extinguished during this pretraining, both the crows’ natural predisposition to handle sticks and
stimulus (or mediated) generalization from the long stick would have been expected to support
the observed use of the short stick. No special understanding of metatools need be invoked. In an
extension of this study (Taylor et al., 2010), the same group of researchers compared two groups
of birds with different experiences of the elements in a sequence of two metatools. Although they
concluded that the successful birds in this study used causal reasoning rather than solely the
mechanisms identified by Epstein, further research is needed to test this conclusion (Kacelnik &
Shettleworth, in preparation).
Epstein’s approach implies that in cognitive tests we need to make a careful ethological
analysis of the cues available and their past histories and/or the animal’s predispositions toward
them. For example, when only one potential tool is offered, it should not be surprising if the
animal tries to use that tool in some way, especially if the tool is a stick and the animal has a
natural or learned propensity to manipulate sticks. Thus tests presenting only one option, in
effect asking whether animal uses the tool or not, may be less informative than tests with several
different tools, asking whether the animal chooses the best tool for the job. In such a test with
New Caledonian crows (Wimpenny et al., 2009), inexperienced birds did not always choose the
correct length stick. Related research is ongoing with primates as well as birds. Even with
Shettleworth 33
chimpanzees, how a task requiring tool use is presented may influence whether the animals
appear to understand the solution or not. For instance, chimpanzees may be able to avoid losing
food into a trap when allowed to slide it along with a finger but fail when required to use a stick
tool (Seed, Call, Emery, & Clayton, 2009; also see Girndt, Meier, & Call, 2008). The examples
sketched here indicate that progress in understanding how animals solve novel problems is more
likely from examining the contributions of simple learning mechanisms and species-typical
behavioral predispositions than from attempting to prove the existence of reasoning or insight.
Discovering how animals successfully interact with their social and physical worlds through such
simple mechanisms should be as much as cause for joy as any validation of anthropomorphism.
Cognition from the bottom up (h2)?
The approach to tool use represented by the study of the “insightful” pigeons illustrates a
central theme of this chapter: complex behavior often arises from simple elements. This bottomup approach is becoming increasingly common in the comparative study of cognition. Some of
the oldest questions about the cognitive abilities of nonhuman species such as “Do animals
count” “Do they talk?” “Or have cognitive maps?” are being replaced by questions such as,
“What are the elements of this cognitive ability?” “How are they shared across species and
why?” This approach to episodic memory, planning, and metacognition has been mentioned.
Other examples include the study of numerical cognition (Brannon, 2006; Nieder, 2005),
language (Hauser, Chomsky, & Fitch, 2002; Margoliash & Nusbaum, 2009), same-different
conceptualization (Wasserman & Young, 2010), spatial behavior (see Shettleworth, 2010,
Chapter 7), other-regarding behavior (de Waal, 2008; Warneken & Tomasello, 2009, chapter
19), and theory of mind (Call & Tomasello, 2008). Sometimes this “bottom up” approach means
looking for shared neural mechanisms (de Waal & Ferrari, 2010), but more often elemental
Shettleworth 34
cognitive processes are defined at the level of behavior (Shettleworth, 2010). Either way, this
approach compares human cognition to that of other species in terms of an interplay of shared
and unique processes. In numerical cognition, for example, even human babies share with other
species an ability to discriminate approximately among different numerosities, with accuracy
dependent on their ratios as described by Weber’s Law. But precise discrimination among
quantities greater than about four appears to depend on number language and thus be confined to
numerate humans. A more sweeping claim is that despite all the cognitive abilities shared by
humans and other animals, only humans have higher order or relational concepts (Penn et al.,
2008). Only in the light of so much new research on cognitive abilities in nonhumans is it
possible meaningfully to address such claims.
Further contributions to a bottom up analysis of cognition come from developments in
the study of human psychology which are revealing an unexpected role in human behavior for
simple, largely unconscious, nonverbal processes such as those found in other animals. Such
work contrasts with the traditional Darwinian search for the human-like in animals in being a
search for the animal-like in humans, but it is equally Darwinian. Although, as discussed at the
beginning of this chapter, emphasis on demonstrating human-like mechanisms in animal
behavior can be traced to The Descent of Man and Selection in Relation to Sex (Darwin, 1871),
in The Expression of the Emotions in Man and Animals Darwin (1872/1965) emphasized the
opposite. For instance, in the Introduction he writes, “With mankind some expressions, such as
the bristling of the hair under the influence of extreme terror, or the uncovering of the teeth under
that of furious rage, can hardly be understood, except on the belief that man once existed in a
much lower and animal-like form” (p. 19, (Darwin 1872/1965). Darwin’s ideas about the origins
and functions of emotional expression are still being tested (e.g. Susskind et al., 2008). Many
Shettleworth 35
examples of unconscious and unexpected influences on human behavior come from classic
evolutionary psychology (see Chapter 4 this volume).
In cognitive psychology, implicit memory and automatic processing of some fundamental
information (Hasher & Zacks, 1984) are well studied in humans, but other unconscious processes
lead to more “animal-like”, less rational outcomes such as preference for immediacy and other
biases in economic decision-making. Presented appropriately, abstract transitive inference
problems are solved by human subjects in the “stupid” way typical of pigeons (Frank, Rudy,
Levy, & O'Reily, 2005). Evidence that we respond unconsciously to simple social cues is
widespread even though it violates our intuitions about how we behave (Goddard, 2009). As one
example, when images of eyes were “watching” the box for contributions to a coffee pool,
average payments more than doubled over weeks when flower images were present instead
(Bateson, Nettle, & Roberts, 2006). And in some cases processes studied first in other species
have later been demonstrated in people, using nonverbal tests. For instance, the ability of
disoriented rats to reorient using the geometry of a surrounding space is shared not only with
birds, fish, and monkeys, but also with young children and, under appropriate conditions, adults
(Cheng & Newcombe, 2005), leading to a proposal that in human spatial cognition insights from
animals should replace the anthropomorphic concept of the cognitive map (Wang & Spelke,
2002). Such developments seem to promise convergence of bottom up analyses of human
behavior with similar analyses of other species into a richer comparative psychology more
deeply rooted in an understanding of how fundamental evolutionary processes produce
complexity from simple species-general elements.
FUTURE DIRECTIONS (h1)
Shettleworth 36
As this Handbook illustrates, these are exciting times for comparative cognitive
psychology, but the fundamental tension represented in this chapter by Darwin and Tinbergen is
still with us. The participation in the field of people with diverse backgrounds and interests
maintains this tension. It can be negotiated productively to the extent that researchers with
different approaches continue to be engaged with one another. There is a fine line between
dismissing “higher” processes as excessively anthropomorphic and dismissing “lower” ones as
not conceivably capable of doing the job. A priori Morgan’s Canon or evolutionary theory does
not necessarily dictate either. Solid evidence supporting one or another explanation for a given
behavior depends to some extent on what alternative explanations are entertained, and that often
depends on deep and imaginative understanding of the species under study. As emphasized in
this chapter, it also depends on an appreciation of the contributions made by ethology and basic
learning theory as well as the many contemporary fields that converge in the comparative study
of cognition.
Shettleworth 37
Acknowledgements
I thank the graduate students in the Oxford University Department of Zoology for the invitation
to give the inaugural Tinbergen Lecture, on which much of this chapter is based. My research
has been supported for nearly 40 years by the Natural Sciences and Engineering Research
Council of Canada.
Shettleworth 38
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