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Chapter Title Intelligence in Nonprimates
Copyright Year 2015
Copyright Holder Springer Science+Business Media New York
Corresponding Author Family Name Zentall
Particle
Given Name Thomas R.
Suffix
Division Department of Psychology
Organization University of Kentucky
Address Lexington, KY, 40506, USA
Email zentall@uky.edu
Abstract We humans value intelligence because we tend to have more of it
than other species. But the flexibility implied by intelligence is not as
reliable as genetically predisposed behavior and is only valuable when
environments change. Nevertheless, environments do change and many
species do have the flexibility to adapt behaviorally to those changes
either through simple Pavlovian or instrumental learning or through
more complex cognitive mechanisms. For example, in addition to the
absolute properties of stimuli, they appear to be able to use the relation
between stimuli to guide their behavior and they can also learn general
principles of learning (learning how to learn). More interesting, perhaps
they can form stimulus classes of stimuli that have perceptually common
characteristics (trees or water) and even classes of arbitrary stimuli that
share a common meaning (stimulus equivalence). When it comes to
memory, animals appear to have flexibility in remembering not only
events that have already experienced (retrospectively) but also events
that they expect to experience (prospectively), and they appear to have
some control over what they will and will not remember. Evidence
suggests further that they have some representation of their environment,
such that when necessary, they can take a novel pathway to get to a goal
(cognitive mapping), and they have rudimentary numerical competence.
Although their ability to reason may be limited, they do show transitive
inference behavior, the ability to imitate actions that they cannot see
when they perform them, and some other animals are able to recognize
themselves in a mirror. Interestingly, studying animal intelligence can
also lead us to more critically examine the origins of what has been
thought to be certain complex human behavior. For example, humans
tend to value rewards more when they have to expend greater effort
to obtain them (cognitive dissonance or justification of effort) but this
tendency is also found in other animals and may more parsimoniously
be explained as an example of contrast (between the effort expended and
the reward obtained). Similarly, the tendency that humans often have to
choose suboptimally, alternatives that have a very low probability of a
high payoff (commercial gambling) can be found in other animals and
appears to result from a general tendency to discount losses and focus
on the value of gains rather than their probability. Overall, it appears
that other animals have (to some degree), many of the cognitive abilities
of humans, while also suffering from some of the suboptimal behavior
thought to be attributable to human culture.
Keywords
(separated by “-”)
Relational learning - Learning-to-learn - Stimulus classes -
Equivalence - Prospective memory - Intentional forgetting -
Cognitive mapping - Counting - Mirror recognition - Imitation -
Suboptimal choice - Cognitive dissonance - Gambling - Sunk cost
- Less is more effect
S. Goldstein et al. (eds.), Handbook of Intelligence: Evolutionary Theory,
Historical Perspective, and Current Concepts, DOI 10.1007/978-1-4939-1562-0_2,
© Springer Science+Business Media New York 2015
Humans tend to have an anthropocentric view of
intelligence that views them at the top and quite
often animals that look like us close behind.
Although the notion of an evolutionary scale with
humans at the top is popularly held, it is also self-
serving. We tend to overvalue our problem-
solving ability, our capacity to modify our
environment, and our ability to communicate with
each other. Conversely, we tend to undervalue the
exceptional sensory skills of other animals, for
example, the tracking and drug-detecting ability
of dogs; the navigational abilities of homing
pigeons, whales, and monarch butterflies; and the
ability of birds of prey to detect the minute move-
ment of a small animal on the ground far below
them. The role of our intelligence in the domina-
tion of our species over others seems obvious, but
in the broader perspective of evolutionary suc-
cess, as measured by the number of surviving
members of a species, intelligence, as a general
characteristic, correlates somewhat negatively
with most measures of evolutionary success.
Consider the relatively small numbers of our clos-
est relatives, the great apes, compared with the
large numbers of neurologically simpler insects,
bacteria, and viruses. And it is estimated that if a
massive disaster were to occur (e.g., if the Earth
were hit by a large asteroid or suffered a self-
inflicted nuclear disaster), many simpler organ-
isms would likely survive much better than large
intelligent animals like us.
From a purely biological perspective, the ideal
survival machine is a simple, one-celled, organ-
ism (e.g., the amoeba) that has survived as a spe-
cies in one of two ways. Either it has needed to
undergo little change in morphology or behavior
for millions of years because it exists in a remark-
ably stable (predictable) environment, in which
case there has been little need for change, or if its
environment does change, it relies on natural
selection by means of very rapid reproduction
and mutation (e.g., bacteria and viruses). This
ability to reproduce quickly and often, ensures
the survival of many of these organisms (albeit
not necessarily in the same form) even in the
event of a major catastrophe. Many other organ-
isms whose rate of reproduction has not been
able to keep up with relatively rapid changes in
the environment have relied on the ability to
modify their behavior during their lifetime.
Intelligence, in its simplest form, can be thought
of as the flexibility endowed by our genes that
allows organisms to adjust their behavior to rela-
tively rapidly changing environments. For some
animals, a stable supply of a highly specific food
may be predictable (e.g., eucalyptus leaves for
the koala or bamboo leaves for the giant panda)—
at least until recently. For most animals, however,
environments are much less predictable, and their
T.R. Zentall (*)
Department of Psychology, University of Kentucky
Lexington, KY 40506, USA
e-mail: zentall@uky.edu
2
Intelligence in Nonprimates
Thomas R. Zentall
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predisposed eating preferences have had to be
much more flexible. For still other animals, the
environment is sufficiently unpredictable that it is
impossible to for them to be predisposed to know
(by genetic means) what food will be available
(consider the varied diet of the city-dwelling rat).
For these animals to survive, more general
(abstract) rules must be available. Rules about
what to eat may not be based on the sight or taste
of what is ingested but on its consequences.
Instead of instructing the animal to eat eucalyp-
tus leaves or to eat a certain class of seeds, these
genes instruct the animal that if it feels sick after
eating a new food, it should avoid eating more of
that food. Such general rules allow for the behav-
ioral flexibility that we call learning.
But there is a price to pay for this added flex-
ibility. The animal must sometimes suffer the
consequences of eating something bad. If the
novel food is poisonous, the animal may not sur-
vive to use its newfound knowledge. The creation
and maintenance of a nervous system capable of
such learning represents a cost as well. For many
animals, the benefits of the capacity for simple
associative learning outweigh the cost, but for
some animals, the negative consequences of trial
and error learning are sufficiently costly that sim-
ple learning rules are not enough.
Some animals have found ways to reduce this
cost. Rats, which live in highly unpredictable
environments, have evolved the ability to learn,
in a single experience, the consequences of eat-
ing a small amount of a novel food, even when
those consequences are experienced hours after
the food was ingested (Garcia and Koelling
1966). Rats also have developed the ability to
transmit food preferences socially. If a rat experi-
ences the smell of a novel food on the breath of
another rat, it will prefer food with that smell
over another equally novel food (Galef 1988),
and it may also be able to assess the consequences
to the other rat of having eaten a novel food
(Kuan and Colwill 1997).
But what if this degree of flexibility in learn-
ing is still not enough to allow for survival? In
the case of humans, for example, our poorly
developed sense of smell, our relatively poorly
developed gross motor response (e.g., slow
running speed), and our relative physical weak-
ness may not have allowed us to hunt competi-
tively with other predators (e.g., large cats). The
competition with other animals for food must
have come about slowly enough for us to develop
weapons and tools, complex forms of communi-
cation (language), and complex social structure
(allowing for cooperation, teamwork, and recip-
rocation). According to this view, although our
intellect appears to have given us a clear advan-
tage over other animals, its evolution is likely to
have emerged because of our relative weakness
in other areas. Other animals have compensated
for their weaknesses by developing strengths in
nonintellectual areas (e.g., the snail compensates
for its lack of rapid mobility by building a pro-
tective shell around itself). Discussions of ani-
mal intelligence often assume, inappropriately,
that intelligence is inherently good. In our case,
it has turned out to be generally true (at least to
the present). For us, intelligence has had a run-
away effect on our ability to adapt to change (an
effect that Dawkins 1976, calls hypergamy),
which has allowed us to produce radical changes
in our environment. However, from a biological
perspective, in general, intelligence can be
viewed as making the best out of a bad situation,
or producing a complex solution to problems
that other species have often solved in simpler
ways. As we evaluate the various intellectual
capacities of nonhuman animals, let us try to
keep in mind that they have survived quite well
(until recently) without the need for our complex
intellectual skills.
The Comparative Approach:
Two Caveats
First, most people have a vague idea of the rela-
tive intelligence of animals. As a general rule,
those species that are more like us physically are
judged to be more intelligent. But we must be
careful in making such judgments because we
humans are the ones who are defining intelligent
behavior. We make up the rules and the testing
procedures, and those tests may be biased in favor
of our particular capacities. Isn’t it interesting that
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animals that are more similar to us, that have
similar sensory, motor, and motivational systems,
just happen to be judged as more intelligent?
Bitterman (1975) has suggested that a rela-
tional view of animal learning can be used to cor-
rect for peripheral differences in sensory capacity
and motor coordination. He suggests that rather
than looking for differences in the rate at which
different species can learn, we might look at dif-
ferences, for example, in an animal’s ability to
learn from the experience of learning. In other
words, to what extent can learning facilitate new
learning (learning to learn)? Then, using the rate
of original learning as a baseline, one can deter-
mine the degree to which later learning, presum-
ably involving the same processes, is facilitated.
However, this approach is not always possible,
and we must be aware that our assessment may
be biased by the use of testing procedures not
well suited for the species we are studying.
Second, we must guard against the opposite
bias—the tendency to interpret behavior as intel-
ligent because of its similarity to intelligent
human behavior. In evaluating research address-
ing the cognitive capacity of animals, we should
adopt C. Lloyd Morgan’s (1894) position that it is
not necessary to interpret behavior as complex
(more cognitive) if a simpler (less cognitive)
account will suffice. This is the principle of par-
simony. Thus, higher-level cognitive interpreta-
tions should always be contrasted with simpler,
contiguity- and contingency-based, associative-
learning accounts. I will start with several classi-
cal issues concerned with the nature of learning
and intelligence in animals, move to more com-
plex behavior thought to be uniquely human, and
end with examples of presumably complex
behavior that are likely to be based on simpler
predisposed processes.
This review will focus primarily on the flexi-
ble behavior of nonprimates because the cogni-
tive behavior of primates is covered elsewhere in
this volume, and thus, it will not address several
areas of research that have been conducted
uniquely with primates, such as analogical rea-
soning, conservation of volume and mass, lan-
guage, perspective taking, theory of mind, and
deception.
Absolute Versus Relational Learning
One of the most basic cognitive functions is not
being bound to the absolute properties of a stimu-
lus. Although Hull (1943) claimed that learning
involves solely the absolute properties of a stimu-
lus, he proposed that animals will appear to
respond relationally because they will respond
similarly to similar stimuli, a process known as
stimulus generalization. Spence (1937) elabo-
rated on this theory by proposing that discrimina-
tion learning establishes predictable gradients of
excitation (approach) and inhibition (avoidance)
that summate algebraically. And this theory of
generalization gradient summation can account
for a number of phenomena that were formerly
explained as relational learning (see Riley 1968).
The fact that one sees little discussion of this
issue in the modern literature suggests that ani-
mals are capable of using either the absolute or
relative properties of a stimulus in making
discriminations.
Learning to Learn
Can an animal use prior learning to facilitate new
learning? That is, can animals learn to learn? If
an animal learns a simple discrimination between
two stimuli (an S+, to which responses are rein-
forced, and an S− to which responses are extin-
guished) and then, following acquisition, the
discrimination is reversed (the S+ becomes S−
and the S− becomes S+), and then reversed again,
repeatedly, are successive reversals learned faster
than earlier reversals? Animals trained on such a
serial-reversal task often show improvement
within a few reversals, and the rate of improve-
ment can be used as a measure of learning to
learn. For example, rats show more improvement
than pigeons, and pigeons show more improve-
ment than gold fish (Bitterman and Mackintosh
1969). Mackintosh (1969) attributes these differ-
ences in serial-reversal learning to the differential
ability of these species to maintain attention to
the relevant dimension and ignore irrelevant
dimensions.
2 Intelligence in Nonprimates
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A different approach to learning to learn is to
look for improvement in the rate at which dis-
criminations involving new stimuli are learned.
This phenomenon, known as learning set (Harlow
1949), has been studied primarily using visual
discriminations with monkeys, but good evidence
for such effects has also been found with olfac-
tory discriminations with rats (Slotnick and Katz
1974). In the limit, learning of a new discrimina-
tion, or of a reversal, can occur in a single trial.
When it does, it is referred to as a win-stay-lose-
shift strategy because stimulus choice is com-
pletely controlled by the consequences of choice
on the preceding trial. One means of developing
such a strategy is to learn to forget the conse-
quences of trials prior to the immediately preced-
ing trial. In fact, research has shown that memory
for the specific characteristics of the stimuli from
prior discriminations does decline as the number
of discriminations learned increases (Meyer
1971). Thus, animals approach optimal learning
by learning to ignore the effects of all but the
most recent experience.
Stimulus Class Formation
Perceptual Classes
Pigeons are remarkably adept at responding
selectively to photographs of natural scenes,
depending on whether the scene involves a
human form (Herrnstein and Loveland 1964) or
trees or water (Herrnstein et al. 1976) and those
objects need not be anything that they might have
actually encountered in their past (e.g., underwa-
ter pictures of fish; Herrnstein and deVilliers
1980). To demonstrate that the pigeons do not
simply memorize a list of pictures and their
appropriate responses, Herrnstein et al. showed
that the pigeons would respond appropriately to
new examples of the positive and negative stimu-
lus sets.
What is interesting about perceptual classes is
that it is difficult to specify what features humans
or pigeons use to discriminate members from
nonmembers of the perceptual class. However,
examination of the kinds of errors made can tell
us about the attributes that were used to categorize
the exemplars and the similarities in the underly-
ing processes for different species. For example,
pigeons make errors similar to those of young
children (e.g., they often erroneously assign a
picture of a bunch of celery or an ivy- covered
wall to the category “tree”).
Equivalence Relations
The emergent relations that may arise when arbi-
trary, initially unrelated stimuli are associated
with the same response are often referred to as
functional equivalence because they belong to a
common stimulus class (see Zentall and Smeets
1996). The best example of equivalence relations
in humans is that aspect of language known as
semantics—the use of symbols (words) to stand
for objects, actions, and attributes. What makes
these relations so powerful is what one learns
about one member of the stimulus class (i.e., a
word) will transfer to others (i.e., the object that
it represents). Thus, a child can be told about the
varied behavior of dogs (sometimes friendly but
not always) without having to actually experi-
ence them (and getting bitten). Thus, stimuli that
belong to the same stimulus class can be thought
of as having the same meaning. The most com-
mon procedure for demonstrating the develop-
ment of functional equivalence involves training
on two conditional discriminations. In the first,
for example, a red hue (sample) signals that a
response to a circle will be reinforced (but not
a response to a dot), and a green hue signals that a
response to a dot will be reinforced (but not
a response to a circle; see Fig. 16.1). In the second
conditional discrimination, a vertical line signals
that a response to the circle will be reinforced
(but not a response to the dot), and a horizontal
line signals that a response to the dot will be rein-
forced (but not a response to the circle). Thus, the
red hue and vertical line can be described as
meaning choose the circle and the green hue and
horizontal line as choose the dot. This procedure
has been referred to as many-to-one matching
because training involves the association of two
samples with the same comparison stimulus. To
show that an emergent relation has developed
between the red hue and the vertical line and
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between green hue and the horizontal line, one
can train new associations between one pair of
the original samples (e.g., the red and green hues)
and a new pair of comparison stimuli (e.g., blue
and white hues, respectively). Then on test trials,
one can show that emergent relations have devel-
oped when, without further training, an animal
chooses the blue hue when the sample is a verti-
cal line and chooses the white hue when the sam-
ple is a horizontal line (Urcuioli et al. 1989;
Wasserman et al. 1992; Zentall 1998).
Although pigeons are not capable of language
learning, the ability of small-brained organisms
like pigeons to develop arbitrary stimulus classes,
the main characteristic of symbolic representa-
tion, suggests that this capacity is much more
pervasive than once thought.
Memory Strategies
The task most often used to study memory in
animals is delayed matching to sample, in which
following acquisition of matching to sample, a
delay is inserted between the offset of the sample
and the onset of the comparison stimuli (Roberts
and Grant 1976). However, the retention functions
typically found with this procedure generally
greatly underestimate the animal’s memory capac-
ity for two reasons. First, in many studies, the novel
delay interval is quite similar in appearance to the
time between trials. This leads to an ambiguity in
the meaning of the delay. When the delay interval
and the intertrial interval are made distinctive, the
retention functions obtained often provide a very
different picture of the animal’s memory (Sherburne
et al. 1998). Second, the novelty of the delays may
result in a generalization decrement that is con-
founded with memory loss. When pigeons are
trained with delays, considerably better memory
has been found (Dorrance et al. 2000). Of more
interest in the assessment of animal intelligence is
an animal’s ability to actively affect its memory.
Prospective Processes
Traditionally, animal memory has been viewed as a
rather passive process. According to this view, sen-
sory events can leave a trace that may control
responding even when the event is no longer pres-
ent (Roberts and Grant 1976). However, there is
evidence that animals can also actively translate or
code the representation of a presented stimulus into
an expectation of a yet-to-be- presented event
(Honig and Thompson 1982). What does it mean to
have an expectation of a future event? Imagine a
delayed matching task in which vertical- and hori-
zontal-line samples are mapped onto red and green
comparison stimuli. During the delay, one can
imagine that some representation of the just seen
sample stimulus would be remembered. But it is
also possible that the sample is translated into a
response intention to select one of the comparison
stimuli. The ability to use expectations, or prospec-
tive coding processes, has important implications
Fig. 16.1 Many-to-one matching training used to show
that pigeons will learn that red and vertical (as well as
green and horizontal) “mean the same thing.” If red and
green samples are now associated with new comparison
stimuli, blue and white, respectively, there is evidence that
vertical and horizontal lines are also associated with the
blue and white stimuli, respectively
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2 Intelligence in Nonprimates
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for the cognitive capacities of animals. If the expec-
tation of a stimulus, response, or outcome can serve
as an effective cue for comparison choice, it sug-
gests that animals may be capable of exerting active
control over memory, and in particular, it may sug-
gest they have the capacity for active planning.
The notion of expectancy as an active pur-
posive process can be attributed to Tolman
(1932). Although one can say that a dog sali-
vates when it hears a bell because it expects
food to be placed in its mouth, the demonstra-
tion that an expectation can serve as a discrimi-
native stimulus (i.e., as the basis for making a
choice) suggests that the expectancy has addi-
tional cognitive properties.
The Differential Outcome Effect If a
conditional discrimination or matching task is
designed such that a correct response following
one sample results in one kind of outcome (e.g.,
food) and following the other sample results in a
different kind of outcome (e.g., water), one can
show that acquisition of the conditional
discrimination is faster (Trapold 1970) and
retention is better when a delay is inserted
between the conditional and choice stimuli
(Peterson et al. 1980). Furthermore, there is
evidence from transfer-of- training experiments
that in the absence of other cues, outcome
anticipations can serve as sufficient cues for
comparison choice. That is, if the original
samples are replaced by other stimuli associated
with the same differential outcomes, positive
transfer has been found (Edwards et al. 1982;
Peterson 1984). This line of research indicates
that presentation of a sample creates an
expectation of a particular kind of outcome and
that expectation alone can then serve as the basis
for comparison choice. In most cases, the
differential outcomes have differential hedonic
value (e.g., a high probability of food versus a
low probability of food), and it is possible that
outcome anticipation can elicit differential
emotional states in the animal. But there is also
evidence that nondifferentially hedonic events
such as the anticipation of a particular stimulus
can affect response accuracy (Kelly and Grant
2001; Miller et al. 2009; Williams et al. 1990).
Planning Ahead One of the hallmarks of
human cognitive behavior is our ability to
consciously plan for the future. Although animals
sometimes appear to plan for the future (birds
build nests; rats hoard food), these behaviors are
likely to be under genetic control. That is, animals
do it but it is not likely to be with the expectation
of later use. Alternatively, what appears to be
future planning just may be the ability to delay
reinforcement. To distinguish between planning
for the future and learning with a long delay of
reinforcement, Suddendorf and Corballis (1997)
have suggested that the behavior indicative of
planning must occur in the absence of the relevant
motivation. Roberts (2002) reported the absence
of planning by monkeys. After they had eaten a
portion of their daily allotment of food, they
threw out of their cage whatever food remained
but then requested more food later in the day.
However, convincing evidence for planning was
reported by Raby et al. (2007). Western scrub
jays, which cache food for future use, learned
that unpredictably, they would either spend the
night in a compartment in which in the morning
they would find one kind of food (peanuts) or in
a compartment in which they would find a
different kind of food (kibble). On test trials, the
night before, they were allowed to eat and cache
food in either compartment. When they were
given peanuts, they tended to cache them in the
kibble compartment, and when they were given
kibble, they tended to cache them in the peanut
compartment (i.e., they cached the food in the
compartment in which they would not find that
particular food in the morning).
Directed (Intentional) Forgetting
The notion of directed or intentional forgetting
is borrowed from human memory research. It
implies that memory is an active rather than a
passive (automatic) process. Presumably, fol-
lowing presentation, items that human partici-
pants are instructed to forget may not be well
stored or maintained in memory and, thus,
should not be well retained. In a directed
forgetting task with animals, for example,
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pigeons are trained on a matching task, and then
a delay of a fixed duration is introduced between
the sample and the comparisons. On remember
trials, during the delay, the pigeons are cued that
there will be a test of sample memory, whereas
on forget trials, the pigeons are cued that there
will be no test of sample memory. On selected
probe trials, the forget cue is presented, but
there is a test of sample memory. Matching
accuracy on these probe trials is generally below
that of remember trials on which there was an
expected test of sample memory (Grant 1981).
But this design confounds differential motiva-
tion on remember and forget trials with sample
memory effects because food can be obtained
only on remember trials. In a more complex
design that controls for motivational effects and
that better approximates the human directed for-
getting procedure by allowing the animal to
reallocate its memory from the sample to an
alternative memory on forget trials in training,
better evidence for directed forgetting in pigeons
has been demonstrated (Roper et al. 1995).
Thus, under certain conditions, it appears that
animals do have at least some active control
over memory processes.
Episodic Memory
Human memory can be identified by the kinds of
processes presumed to be involved. Procedural
memory involves memory for actions (e.g., rid-
ing a bicycle), and it has been assumed that most
learned behavior by animals involves this kind of
memory. Human declarative memory is assumed
to be more cognitive because it involves memory
for facts (semantic memory) and memory of per-
sonal experiences (episodic memory). Although
animals cannot typically describe factual infor-
mation, their conditional rule-based learning can
be thought of as a kind of semantic memory (e.g.,
if the sample is red, choose the vertical line; if the
sample is green, choose the horizontal line). But
do animals have episodic memory? Can they
retrieve personal experiences or do they simply
remember the rules.
Tulving (1972) proposed that an episodic
memory should include the what, where, and
when of an experience. Clayton and Dickinson
(1999) showed that western scrub jays that
cached peanuts and wax worms (what) on one
side or the other of an ice cube tray (where)
learned that their preferred wax worms would be
edible after one day but after four days only the
peanut would be edible (when; see also Babb and
Crystal 2006, for a similar finding with rats). But
it can be argued that it is insufficient to retrieve
the what, where, and when of an episode because
those have been explicitly trained (i.e., they are
likely to be semantic or rule-based memories).
Instead, better evidence for episodic memory
would come from the finding that animals can
retrieve information about a past episode when
there is no expectation that they will be requested
to do so in the future (Zentall et al. 2001). That is,
imagine that pigeons are first trained to report the
location where they recently pecked (instruc-
tions) and then they are trained on an unrelated
conditional discrimination in which choice of a
vertical line was correct when the sample was
blue and choice of the horizontal line was correct
when the sample was yellow. Singer and Zentall
(2007) found that on probe trials on which fol-
lowing a vertical- or horizontal-line comparison
response the pigeons were asked unexpectedly to
report the location that they had pecked, and they
reliably did so. Thus, by either criterion (what-
where- when or responding to an unexpected
question), pigeons show some evidence of
episodic- like memory.
Navigation
Compared to many animals, humans have rela-
tively poor navigational skills. Consider how
dependent we are on external supports such as
compasses, maps, and more recently global posi-
tioning devices. Many animals (e.g., migrating
whales, birds, monarch butterflies) can navigate
over many hundreds of miles using magnetic
fields, chemical gradients, and star patterns. And
homing pigeons use a number of these naviga-
tional systems including landmarks consisting of
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natural and man-made geographic features (Lipp
et al. 2004).
However, many humans have the ability to
imagine a route that they will take and even to
imagine how to get to a familiar destination by a
novel path. This ability, known as cognitive map-
ping, consists of knitting together landmarks one
has experienced, such that the relation among
them can be used to determine a novel path to
arrive at a goal. Landmarks are needed to form a
cognitive map, but they should not be necessary
to use it. Can animals form a cognitive map?
Before trying to answer this question, we need
to make an important distinction. Some animals
have the remarkable ability to navigate in the
absence of landmarks or other external cues. This
ability, known as path integration (or dead reck-
oning), involves the representation of direction
and distance one has traveled from a starting
point. Dessert ants are particularly adept at path
integration as can be shown not only by the direct
path that they take to return to their nest after a
foraging trip but also by the systematic error
incurred if they are displaced just before they
attempt to return home (Collette and Graham
2004). The distinction between path integration
and cognitive mapping has been a point of con-
troversy. However, under conditions that cannot
be accounted for with either landmark use or path
integration, there is evidence for the development
of a simple cognitive map in rats (Singer et al.
2007) and dogs (Chapuis and Varlet 1987).
Counting
The term numerical competence is often used in
animal research because the more common term,
counting, carries with it the surplus meaning that
accompanies the human verbal labels given to
numbers. That this distinction is an arbitrary one,
based on limitations of response (output) capac-
ity rather than conceptual ability, is suggested by
Pepperberg’s (1987) work with generalized ver-
bal number use in an African gray parrot.
An excellent review of the animal counting
literature is provided by Davis and Memmott
(1982), who conclude that “although counting is
obtainable in infra humans, its occurrence
requires considerable environmental support”
(Davis and Memmott, p. 566). In contrast,
Capaldi (1993) concludes that under the right
conditions, animals count routinely. In simple but
elegant experiments, Capaldi and Miller (1988)
demonstrated that following training, rats can
anticipate whether they will get fed or not for
running down an alley depending solely on the
number of successive times they have run down
that alley and found food or the absence of food
on successive earlier trials.
The difference in the conclusions reached by
Davis and Memmott (1982) and by Capaldi and
Miller (1988) has general implications for the
study of intelligence in animals (including
humans). The context in which one looks for a
particular capacity may determine whether one
will find evidence for it. As noted earlier, because
we, as human experimenters, devise the tasks that
serve as the basis for the assessment of intelli-
gence, we must be sensitive to the possibility that
these tasks may not be optimal for eliciting the
behavior we are assessing. That is, much of our
view of the evolutionary scale of intelligence
may be biased in this way by species differences
in sensory, response, and motivational factors.
Reasoning
Reasoning can be thought of as a class of cogni-
tive behavior for which correct responding on test
trials requires an inference based on incomplete
experience. Although, for obvious reasons, most
research on reasoning in animals has been done
with higher primates (e.g., chimpanzees), there is
evidence that some reasoning-like behavior can
be demonstrated in a variety of species.
In its simplest form, the transitive inference
task can be described as follows: if A is greater
than B (A > B), and B is greater than C (B > C),
then it can be inferred that A > C (where the let-
ters A, B, and C represent arbitrary stimuli).
A correct response on this relational learning task
requires that an inference be made about the rela-
tion between A and C that can only be derived
from the two original propositions. To avoid
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potential problems with end point effects that
could produce a spurious nonrelational solution
(i.e., A is always greater, and C is never greater),
experimental research typically uses a task that
involves four propositions: A > B, B > C, C > D,
and D > E, and the test involves the choice
between B and D, each of which is sometimes
greater and sometimes lesser.
When humans are tested for transitive infer-
ence, the use of language allows for the proposi-
tions to be completely relational. Relative size
may be assigned to individuals identified only by
name (e.g., given that Anne is taller than Betty,
and Betty is taller than Carol, who is taller, Anne
or Carol?). With animals, however, there is no
way to present such relational propositions with-
out also presenting the actual stimuli. And if the
stimuli differ in observable value (e.g., size), then
a correct response can be made without the need
to make an inference.
McGonigle and Chalmers (1977) suggested
that a nonverbal relational form of the task could
be represented by simple simultaneous discrimi-
nations in which one stimulus is associated with
reinforcement (+) and the other is not (−). A > B
can be represented as A + B−, B > C as B + C−, and
so on. With four propositions, an animal would
be exposed to A + B−, B + C−, C + D−, and D + E−.
A is always positive and E is always negative, but
B and D, stimuli that were never paired during
training, would share similar reinforcement histo-
ries. If animals order the stimuli from A is best to
E is worst, then B should be preferred over D.
Findings consistent with transitive inference
have been reported in research with species as
diverse as chimpanzees (Gillan 1981), rats (Davis
1992), and pigeons (Fersen et al. 1991). Although
some have argued that these results can be
accounted for without postulating that an infer-
ence has been made (Couvillon and Bitterman
1992; Fersen et al. 1991; Steirn et al. 1995), tran-
sitive inference effects have been found when
these presumably simpler mechanisms have been
controlled (Lazareva and Wasserman 2006;
Weaver et al. 1997). Thus, although it is not clear
what mechanism produces it, pigeons clearly
show transitive choice that is not produced by
differential reinforcement history or differential
value that transfers from the positive to the nega-
tive stimulus in a simultaneous discrimination.
Taking the Perspective of Others
An organism can take the perspective of another
when it demonstrates an understanding of what
the other may know. For example, when Susan
sees a hidden object moved to a second hidden
location after Billy has left the room and Susan
understands that Billy will probably look for the
object in the first location rather than second, we
would say that Susan can take the perspective of
Billy or she has a theory of mind because she
understands that Billy doesn’t know that the
object has been moved (see Frye 1993). To dem-
onstrate, perspective taking in an animal is a bit
more complex because, in the absence of lan-
guage, theory of mind must be inferred from
other behavior (see, e.g., Hare et al. 2001).
Self-Recognition
Recognition of the similarity between ourselves
and other humans would seem to be a prerequisite
for perspective taking. If we can recognize our-
selves in a mirror, we can see that we are similar
to others of our species. Gallup (1970) has shown
that not only will chimpanzees exposed to a mir-
ror use it for grooming, but if their face is marked
while they are anesthetized, they will use the mir-
ror to explore the mark visually and tactually (i.e.,
they pass the mark test). Furthermore, both prior
experience with the mirror and the presence of the
mirror following marking appear to be necessary
for mark exploration to occur. Mirror-directed
mark exploration appears to occur generally in the
great apes (orangutans and perhaps also in goril-
las) but not in monkeys even with extensive mirror
experience (Gallup and Suarez 1991). However,
using the mark test, there is some evidence of self-
recognition in dolphins (Reiss and Marino 2001),
elephants (Plotnik et al. 2006), and magpies (Prior
et al. 2008). Thus, self-recognition appears to
occur in several nonprimate species thought to
show other kinds of cognitive skills.
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Imitation
A more direct form of perspective taking
involves the capacity to imitate another (Piaget
1951), especially opaque imitation for which
the observer cannot see itself perform the
response (e.g., clasping one’s hands behind
one’s back). But evidence for true imitative
learning requires that one rule out (or control
for) other sources of facilitated learning follow-
ing observation (see Whiten and Ham 1992;
Zentall 1996, 2012). A design that appears to
control for artifactual sources of facilitated
learning following observation is the two-action
procedure based on a method developed by
Dawson and Foss (1965). For example, imita-
tion is said to occur if observers, exposed to a
demonstrator performing a response in one of
two topographically different ways, perform the
response with the same topography as their
demonstrator. Akins and Zentall (1996) trained
Japanese quail demonstrators to either step on a
treadle or peck the treadle for food reinforce-
ment. When observer quail were exposed to one
or the other demonstrator, they matched the
behavior of their demonstrator with a high prob-
ability (see also Zentall et al. 1996, for similar
evidence with pigeons). Furthermore, there is
some evidence that pigeons can imitate a
sequence of two responses, operating a treadle
(by stepping or pecking) and pushing a screen
(to the left or to the right; Nguyen et al. 2005),
an example of what Byrne and Russon (1998)
refer to as program-level imitation.
If Piaget is correct, the ability to imitate
requires the ability to take the perspective of
another. But children do not develop the ability
to take the perspective of another until they are
5–7 years old, yet they are able to imitate oth-
ers at a much earlier age. Furthermore, if
pigeons and Japanese quail can imitate, it is
unlikely that they do so by taking the perspec-
tive of the demonstrator, in the sense that
Piaget implied. Thus, although cognitively
interesting, imitation may not provide evidence
for the kind of cognitive behavior implied by
perspective taking.
What Animals Can Tell Us About
Human Reasoning
I have saved for last the discussion of several lines
of research with animals directed to biases and
heuristics characteristic of humans that appear to
be somewhat irrational or at least suboptimal. The
results of these studies are important, not so much
because of their implications for animals, but pri-
marily for their implications for how we interpret
human behavior. That is, if other animals have
these same biases, then the basis for those biases
does not depend on language or human culture as
is sometimes proposed.
Cognitive Dissonance
One of these biases has to do with a phenomenon
extensively studied in humans called cognitive
dissonance. Cognitive dissonance is the discom-
fort that comes when there is a discrepancy
between one’s beliefs and one’s behavior. For
example, if one believes that one should tell the
truth, one is likely to feel dissonance on occa-
sions when one fails to do so. That dissonance
may be resolved by deciding that there are some
conditions under which lying is appropriate or
the person lied to may have deserved it. Cognitive
dissonance presumably comes about because of a
need to be consistent or to avoid being labeled a
hypocrite. Does this represent a kind of social
intelligence? And if so, would nonhuman ani-
mals show a similar effect? But how would one
go about asking this question of animals?
One approach involves a version of cognitive
dissonance called justification of effort (Aronson
and Mills 1959). In their study, undergraduates,
who underwent an unpleasant initiation to
become part of a group, reported that they
wanted to join the group more than those who
underwent a less unpleasant initiation. It is
assumed that those individuals who underwent
an unpleasant initiation gave more value to
membership in the group to justify undergoing
the unpleasantness.
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The justification of effort design allows for a
direct test of cognitive dissonance in animals. For
example, if on some trials a pigeon has to work
hard to receive signal A that says food is coming
and on other trials the pigeon does not have to
work hard to receive signal B that says the same
food is coming, will the pigeon show a prefer-
ence for signal A over signal B? Several studies
have shown that they will (e.g., Clement et al.
2000; Kacelnik and Marsh 2002). But is this cog-
nitive dissonance? Do animals need to justify to
themselves why they worked harder for one sig-
nal than the other?
Alternatively, we have suggested that this
choice behavior results from the contrast between
the relatively negative emotional state of the
organism at the end of the effort and upon presen-
tation of the signal (Zentall and Singer 2007).
That difference would be greater when more
effort was involved. Thus, the subjective value of
the signal for reinforcement might be judged to
be greater. Contrast provides a more parsimoni-
ous account of the pigeons’ choice behavior.
Could contrast also be involved in the similar
behavior shown by humans? This possibility
should be examined by social psychologists.
Maladaptive Gambling Behavior
Humans often gamble (e.g., play the lottery) even
though the odds against winning are very high.
This behavior may be attributable to an inaccu-
rate assessment of the probability of winning,
perhaps resulting in part from public announce-
ments of the winners but not the losers (an avail-
ability heuristic). Would animals show a similar
kind of maladaptive gambling behavior?
According to optimal foraging theory, they
should not because such inappropriate behavior
should have been selected against by evolution.
Furthermore, if the choice is to have any meaning
for the animal, it would have to have experienced
the probability associated with winning (rein-
forcement) and that should reduce the likelihood
that the animal would not be able to assess the
probability of winning and losing. However, we
have recently found conditions under which
pigeons will prefer an average of 2 pellets of food
over a predictable 3 pellets of food (Zentall and
Stagner 2011). The procedure is as follows: If the
pigeon chooses the left alternative, on 20 % of
the trials, a green stimulus appears and is fol-
lowed by 10 pellets of food. The remainder of the
time it chooses the left alternative; a red stimulus
appears and is never followed by food. Thus, on
average the pigeon receives 2 pellets of food for
choosing that alternative. If the pigeon chooses
the right alternative, it received either a blue or a
yellow stimulus but in either can it receives 3 pel-
lets of food. Curiously, the pigeons prefer the left
alternative 2–1 over the right alternative, and they
do so in spite of the fact that they would get 50 %
more food for choosing the right alternative.
This result suggests that gambling behavior is
likely to have a simple biological basis and,
although social and cognitive factors may con-
tribute to human gambling behavior, the underly-
ing mechanism is likely to be simpler. The
mechanisms responsible for this suboptimal
behavior appear to involve the enhanced effect of
the signal for the large magnitude of reinforcer
(the 10-pellet “jackpot”) and the reduced effect
of the signal for nonreinforcement with training
(Stagner et al. 2012). This account appears to be
consistent with research with humans that has
found that gamblers overvalue wins in spite of
their low probability of occurrence and they give
too little negative value to their losses in spite of
their high probability of occurrence (Blanco et al.
2000).
Sunk Cost
The sunk cost effect occurs when one allows an
amount of money, time, or other resource already
invested to affect one’s decision to invest more
resources. For example, one may sit through a
film that one does not like because to leave would
be to waste one’s investment of the price of the
ticket. But in so doing, one is spending additional
resources, one’s time, and there is no way to
recoup the money already invested. Similarly,
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one may choose to continue with a failing busi-
ness because of the past investment one has made
in it. This phenomenon comes under the general
rubric of prospect theory (Kahneman and
Twersky 1979) which suggests that humans will
take greater risks to avoid a loss than to obtain a
gain. The question is to what extent does this
behavior stem from the cultural tenet to avoid
wasting resources and to complete what one
starts. If one could show that animals show the
same behavior, it would suggest that sunk cost is
a general phenomenon that has basic behavioral
origins.
In fact, evidence for sunk cost has been found
in pigeons (Navarro and Fantino 2005; Pattison
et al. 2012). For example, pigeons learn that
pecking a green light requires 30 pecks, whereas
pecking a red light requires only 10 pecks. They
then learn that after pecking the green light a
number of times (that varied from trial to trial),
they would be able to choose to continue with the
green light (to complete the 30 pecks) or switch
to the red light for which 10 pecks were required.
Results indicated that the pigeons often choose to
return to the green light even when 20 more pecks
are required. Thus, pigeons show a sunk cost
effect that is very similar to that shown by
humans. Why pigeons show the sunk cost effect
is not clear. One can speculate that it arises from
the fact that in nature switching to a different
patch often involves uncertainty, some travel
time, and possible danger, but one can certainly
conclude that culture and language are not neces-
sary components.
When Less Is More
When humans are asked to judge the value of a
set of objects of excellent quality, they often give
it higher value than those same objects with the
addition of some objects of lesser quality (Hsee
1998). This bias is an example of the affect heu-
ristic in which it appears that the average quality
of a set is used to determine the value of the set
rather than the quantity. The phenomenon has
become known as a less is more effect. It has
been found when humans are asked to judge the
value of sets of dishes and sets of baseball cards
(Hsee 1998), and it also has been found when
academics are asked to judge the quality of a cur-
riculum vita (Hayes 1983). For example, a vita
with three publications in excellent journals is
judged better than one with the same three publi-
cations in excellent journals plus six more in
lesser quality journals.
Recent evidence suggests that even pigeons
are susceptible to this bias. We found that pigeons
will work for dried peas and dried milo seeds, but
when given a choice between the two, they prefer
the peas. However, when they are given a choice
between a pea and a pea together with a milo
seed, they prefer the pea alone (Zentall et al.
2013). Apparently, the pigeons too are averaging
the high-quality pea with the lower-quality milo
seed and value the pair less than the pea by itself
(see also Kralik et al. 2012). The basis of this bias
may originate in the need to make rapid deci-
sions, presumably because of intense competi-
tion from conspecifics and the possibility of
predation, and they use it in the laboratory even
when speed is not a factor. Once again, the fact
that other animals show this suboptimal choice
indicates that the bias is probably not dependent
on human cultural influence.
Conclusions
The broad range of positive research findings that
have come from investigating the cognitive abili-
ties of animals suggests that many of the “special
capacities” attributed to humans may be more
quantitative than qualitative. In the case of many
cognitive learning tasks, once we learn how to
ask the question appropriately (i.e., in a way that
is accommodating to the animal), we may often
be surprised with the capacity of animals to use
complex relations.
In evaluating the animal (and human) intelli-
gence literature, we should be sensitive to both
overestimation of capacity (what appears to be
higher-level functioning in animals that can be
accounted for more parsimoniously at a lower
level; see Zentall 1993) and underestimation of
capacity (our bias to present animals with tasks
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convenient to our human sensory, response, and
motivational systems). Underestimation can also
come from the difficulty in providing animals
with task instructions as one can quite easily do
with humans (see Zentall 1997). The accurate
assessment of animal intelligence will require
vigilance, on the one hand, to evaluate cognitive
functioning against simpler accounts and, on the
other hand, to determine the conditions that will
maximally elicit the animal’s cognitive capacity.
The study of human biases by examining ani-
mals for the presence of similar phenomena in
animals can also help us to determine that sim-
pler mechanisms are involved. Thus, the study of
animal intelligence can inform us not only of the
cognitive abilities of animals but also can suggest
the bases of certain human phenomena thought to
have complex social origins.
Acknowledgments Preparation of this chapter was made
possible by grant MH 63726 from the National Institute of
Mental Health and grant HD60996 from the National
Institute of Child Health and Human Development.
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Author Queries
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