This article explores the evolution of uniquely human cognition, emphasizing the psychological mechanisms that differentiate humans from other species and the evolutionary processes that shaped these traits. It suggests that human cognition is characterized by a synergistic relationship between representational and motivational factors, and discusses how cooperative motivations and cultural learning have played critical roles in this development. The piece also compares cognitive traits of humans to those of nonhuman primates, particularly focusing on the role of social tolerance and reduced aggression in human evolution.

1. SPECIAL FEATURE: PERSPECTIVE
Unraveling the evolution of uniquely
human cognition
Evan L. MacLeana,b,1
Edited by Richard G. Klein, Stanford University, Stanford, CA,
and approved January 7, 2016 (received for review November
12, 2015)
A satisfactory account of human cognitive evolution will
explain not only the psychological mechanisms that
make our species unique, but also how, when, and why these
traits evolved. To date, researchers have made
substantial progress toward defining uniquely human aspects of
cognition, but considerably less effort has
been devoted to questions about the evolutionary processes
through which these traits have arisen. In this
article, I aim to link these complementary aims by synthesizing
recent advances in our understanding of
what makes human cognition unique, with theory and data
regarding the processes of cognitive evolution.
I review evidence that uniquely human cognition depends on
synergism between both representational and
motivational factors and is unlikely to be accounted for by
changes to any singular cognitive system. I argue
that, whereas no nonhuman animal possesses the full
constellation of traits that define the human mind,
homologies and analogies of critical aspects of human
psychology can be found in diverse nonhuman taxa.
I suggest that phylogenetic approaches to the study of animal
cognition—which can address questions

2. about the selective pressures and proximate mechanisms driving
cognitive change—have the potential to
yield important insights regarding the processes through which
the human cognitive phenotype evolved.
cognitive evolution | human evolution | comparative psychology
| human uniqueness | cognition
Human minds seem unlike those of any other species.
We participate in large-scale institutions, wage wars
over beliefs, imagine the distant future, and commu-
nicate about these processes using syntax and sym-
bols. What aspects of human cognition allow us to
accomplish these seemingly unique feats, and are
these processes qualitatively different from those of
other animals? Equally importantly, how and why did
such a peculiar psychology evolve? What was it about
early human lifestyles that favored these flexible forms
of cognition, and how did natural selection sculpt
these features from a nonhuman ape-like foundation?
The questions above address different levels of expla-
nation (1, 2) for human cognitive uniqueness, but ulti-
mately a satisfactory account of human cognitive
evolution will explain not only the mechanisms that
make our species unique, but also how, when, and
why these traits evolved. To date, scientists have
made substantial progress toward defining uniquely
human aspects of cognition, but considerably less ef-
fort has been devoted to questions about the evolu-
tionary processes through which these traits have
arisen. In this article, I aim to link these unique but
complementary aims by first highlighting recent ad-
vances in our understanding of how human psychology
differs from that of other extant taxa. I then turn to the
less well-understood questions of how, when, and why

3. these traits evolved and underscore the importance of
understanding evolutionary processes, not just their
products, for a comprehensive understanding of hu-
man cognitive evolution.
What Makes Human Cognition Unique?
At first glance, the cognitive differences between hu-
mans and all other animals seem to be enormous.
Humans alone do calculus, travel in machines with
global positioning systems, search for life beyond our
planet, and store information about how to do so in
digital repositories accessible around the world. But
none of these feats are hardwired in the human brain,
nor were any of them invented de novo by a single
enterprising individual. Instead, all of these accom-
plishments depended on the accretion of thousands
of years of incremental progress and a cognitive and
cultural system that allowed (and motivated) individ-
uals to acquire and transmit accumulated knowledge
and skills (3). Tomasello and Rakoczy (4) highlight this
point by noting that “if we imagine a human child born
onto a desert island, somehow magically kept alive by
itself until adulthood, it is possible that this adult’s
aEvolutionary Anthropology, Duke University, Durham, NC
27708; and bSchool of Anthropology, University of Arizona,
Tucson, AZ 85719
Author contributions: E.L.M. wrote the paper.
The author declares no conflict of interest.
This article is a PNAS Direct Submission.
1Email: [email protected]
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113&domain=pdf
mailto:[email protected]
www.pnas.org/cgi/doi/10.1073/pnas.1521270113
cognitive skills would not differ very much—perhaps a little,
but
not very much—from those of other great apes (ref. 4, p. 121)”.
Therefore, there is an important distinction between trying to
understand how cumulative culture has influenced the collective

5. cognition of our species and trying to understand how humans
became such prolifically cultural beings in the first place.
For clues regarding the origins of human cultural cognition,
scientists have turned attention to human development, and the
fundamental aspects of human cognition that allow us to com-
municate with, share, and acquire information from others (5).
These processes emerge early in human ontogeny and are
supported by a nascent understanding of others as intentional
agents. Within the first year of life, human children begin to
relate to others in new ways, tuning into others’ attention
through
processes such as gaze following and exchanging information
with others through simple acts of referential gesture (6). These
basic skills for communication and shared attention provide the
social foundation for a variety of forms of cultural learning, in-
cluding the initial stages of language acquisition (7, 8). For ex-
ample, by 2 y of age, these perspective-taking skills allow
human
children to make pragmatic inferences linking new words with
the
(inferred) target of another’s attention (9). Thus, already in the
first
years of life, human children begin to experience the world not
only through their own eyes, but also together with others, and
these abilities for reasoning about others’ minds—collectively
termed “theory of mind”—provide children with powerful
mecha-
nisms for acquiring and sharing cultural information, including
lan-
guage, social norms, and societal beliefs. Around the age of
four,
human children reach another milestone in their understanding
of
others as intentional agents, explicitly interpreting others’
behavior

6. as the output of a belief–desire psychology and also reasoning
about the goals and beliefs not only of other individuals, but
also of
their cultural group more broadly (4). Recent cross-cultural
studies
reveal that these early-emerging skills for reasoning about
others’
minds develop at approximately the same age across diverse
cul-
tures (10, 11) and represent critical milestones on the path to
uniquely human cultural cognition. Of course, the acquisition of
a
symbolic language further propels human cognition, possibly by
providing a new representational medium that permits novel
forms
of abstract and relational reasoning, as well as unprecedented
forms of communicative flexibility (12–15). However, the
critical
point is that the acquisition and use of a human-like language is
simply neither possible, nor useful, for a species without pre-
requisite skills for reasoning about other minds. Are these foun-
dational sociocognitive skills unique to humans?
Until recently, there was a general consensus that humans
were indeed unique in their understanding of others as
intentional
agents (16, 17). However, research in the last 15 y has called
this black-and-white interpretation into question (but see ref.
18),
revealing that some nonhuman species do exploit information
about others’ perception, knowledge, and intentions (19). The
revised thinking about a nonhuman theory of mind has unfolded
in parallel with changes in experimental methods that now em-
phasize studies of animals under more ecologically relevant
con-
ditions. For example, the first positive evidence for perspective

7. taking in humans’ closest living relatives emerged when chim-
panzees were tested in competitive situations with conspecifics
rather than in cooperative contexts with humans (20, 21). Since
these initial studies, several species besides apes have shown
similar skills in the context of social competition (22–24). This
social–cognitive flexibility in competitive but not cooperative
contexts aligns well with the “Machiavellian intelligence”
hypothesis,
which proposes that primate minds were shaped by an evolu-
tionary arms race in which skills for manipulating and deceiving
others were paramount (25, 26).
If the ability to reason about others’ minds is not entirely
unique to humans, then what accounts for the uniquely human
forms of culture and communication that begin to emerge
around
children’s’ first birthdays? One explanation places a special em-
phasis on the combination of perspective-taking skills and a co-
operative motivation for sharing psychological states with
others,
including joint goals and intentions (27). Unlike nonhuman
apes,
who exploit others’ perspectives primarily for their own
purposes
(28), human infants put their perspective-taking skills to work
in
the contexts of sharing attention with others and communicating
cooperatively with one another. Importantly, human children
also
expect their social partners to be similarly motivated, creating a
reciprocally cooperative framework for communicative and col-
laborative endeavors. For example, around their first birthdays,
human children begin to produce pointing gestures simply to
call
others’ attention to objects of interest, and, when others point

8. for
them, children assume a cooperative motive relevant to the
common ground between the two communicators (29). In con-
trast, whereas great apes can learn to point imperatively, for ex-
ample when requesting food (30), they do not produce pointing
gestures simply to share information with others, and, when
others
point cooperatively for them (e.g., to indicate the location of
hidden food), nonhuman apes tend to perform poorly, most
likely
because they do not understand their partner’s cooperative in-
tention. Shortly after 1 y of age, human prosocial and
cooperative
motives begin to evidence themselves more explicitly through
acts of (unsolicited) instrumental helping, which again are
critically
supported by the ability to infer others’ intentions, knowledge,
and desires (31). Therefore, unlike nonhuman apes, human cog-
nition seems to be most tailored for cooperative and prosocial
rather than Machiavellian purposes (32).
Importantly, neither the understanding of others as intentional
agents nor prosocial and cooperative attitudes alone can support
uniquely human cultural cognition. Rather, it is the synergy be-
tween motivations to engage in collaborative activities with
shared goals and psychological processes for representing the
underlying “we” intentionality (33) that allows humans to create
the cultural products that differ so substantially from those of
other species (34). How and why then did this unusual
constella-
tion of traits evolve? Were these motivational and representa-
tional changes evolutionarily coupled or do they have
independent
evolutionary origins?
Becoming Human

9. Attempts to reconstruct human cognitive evolution in the last 5–
7
million y require inferences about the characteristics of our last
common ancestor (LCA) with our closest living relatives—
bonobos
and chimpanzees. Because bonobos and chimpanzees are
equally
related to humans, but differ from one another morphologically,
behaviorally, and cognitively, there has been active debate re-
garding which (if either) of the two Pan species serves as the
best
living model of our LCA (35–38). Although there is no strong
con-
sensus on this issue, studies comparing cranial development in
great apes reveal that bonobos deviate from the conserved
pattern
found in gorillas and chimpanzees and indicate that bonobos
may
be relatively derived due to neotenic development (39). These
changes in the timing of development are thought to have had
cascading effects on diverse aspects of bonobo biology, leading
to
derived aspects of social behavior and cognition that differ from
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chimpanzees, and presumably also from the LCA of bonobos,
chimpanzees, and humans (37, 40).
Assuming a more chimpanzee-like last common ancestor,
evolutionary changes in temperament and specifically a shift to-
ward more tolerant and less aggressive social behavior may
have
been critical prerequisites for the evolution of human forms of

10. cultural behavior and cognition. In wild chimpanzees, within-
group aggression among both males and females can be ex-
treme, and intergroup aggression is often lethal (41, 42).
Although
chimpanzees cooperate in contexts such as group hunting, the
successful captor typically retains the majority of the spoils and
provides scraps to others predominantly in response to harass-
ment (43). Therefore, although they are skillful cooperators in
some contexts, it seems that chimpanzees are motivated
primarily
by individual goals, are relatively intolerant sharing partners,
and
have little regard for the equitable distribution of resources
arising
from cooperative efforts (44). The constraining role of social
tol-
erance on chimpanzee cooperation and cognition has also been
well-documented through experimental studies. For example,
chimpanzees are more successful in instrumental cooperation
tasks when the reward is physically dispersed than when it is
clumped and individually monopolizable (45), and intolerance
between individuals can preclude social learning in model–
observer paradigms (46). Therefore, social intolerance between
individuals can present an emotional barrier that significantly
im-
pedes potential for cooperation and social learning.
Relative to chimpanzees, bonobos—who are equally related
to humans—are characterized by less intense forms of aggres-
sion both within and between social groups (47). Bonobos
cofeed with one another more tolerantly than chimpanzees
(ref. 48; but see refs. 49 and 50) and voluntarily share food with
conspecifics (51) (Fig. 1), including strangers (52). This
tolerance
and willingness to share allows bonobos to outperform chim-
panzees in some cooperative tasks, particularly when rewards

11. are potentially monopolizable (48). The relatively lower levels
of
aggression and increased social tolerance in bonobos relative
to chimpanzees have been hypothesized to result from a process
of “self-domestication” (40). This hypothesis is supported by
data revealing a syndrome of behavioral, morphological, and
psychological traits in bonobos that are similar to those found
in artificially domesticated species. The cooccurrence of these
traits in bonobos and domesticated species is thought to result
from selection (natural or artificial) against aggression, which
has
led to changes in developmental timing and neurophysiology,
including alterations to the hypothalamic–pituitary–adrenal
axis,
androgen levels, and the serotonergic system (40, 53).
Why might natural selection have favored these behavioral
changes in bonobos but not chimpanzees? One plausible ex-
planation emphasizes differences in feeding ecology between
the
regions north and south of the Congo River (54). Whereas chim-
panzees and gorillas share habitat and compete for vegetation
north of the river, bonobos are restricted to regions south of the
river that do not overlap with those of chimpanzees or gorillas.
Due
to high levels of scramble competition, chimpanzee females
resort
to foraging alone, do not form strong alliances with other
females,
and are vulnerable to sexual coercion by males (55–57). In
contrast,
bonobo females face lower levels of scramble competition and
form
alliances with other females that can effectively deter male
coercion,

12. and male bonobos benefit through affiliative rather than
aggressive
behavior toward females (47, 58). Under these conditions,
selection
likely favored males with a reduced propensity for aggression,
ul-
timately leading to a self-domesticated phenotype (40).
Data from artificially domesticated species provide further ev-
idence for how a reduction in aggression can potentially
transform
not only temperament, but also aspects of social cognition, in-
cluding some of the cooperative–communicative skills believed
to
be critical in human cognitive development. For example, com-
parisons of domesticated species and their wild forebearers—
including dogs and wolves, experimentally domesticated sliver
foxes and a control lineage, and domestic and wild ferrets—
reveal
that domesticated forms display an increased sensitivity to co-
operative communication, as well as alterations in other social
behaviors such as the willingness to sustain eye contact (59–
62).
Importantly, these changes in cooperative behavior are thought
to
have arisen as a byproduct of changes in temperament (63),
illus-
trating how emotional evolution can release constraints on
social
tolerance and effectively permit new forms of social
engagement
(64). Was this type of temperamental transformation also a key
step
in human cognitive evolution? Was Darwin correct in
conjecturing
that “[m]an in many respects may be compared with those

13. animals
which have long been domesticated” (ref. 65, p. 172)?
One major morphological trend in human evolution has been a
reduction in sexual dimorphism in both body mass and canine
tooth size. Comparative analyses with extant primates have
linked
these morphological traits to variance in social systems and sug-
gest that high levels of dimorphism are associated with intense
male–male competition and polygynous mating systems (66).
Thus, the reduction in sexual dimorphism in the human lineage
may reflect a transition from a more chimpanzee-like mating
sys-
tem, with high levels of male–male violence and sexual
coercion
of females, toward monogamy and cooperative breeding. This
shift in social systems—which may have begun as early as 3
million y
ago (67)–likely favored an initial reduction in aggression and
increased tolerance between individuals (68). Within the last
200,000 y, additional changes in human craniofacial
morphology
raise the intriguing possibility of a second wave of selection
against aggression that coincided with the emergence of behav-
ioral modernity (69). Specifically, Cieri et al. documented in-
creased feminization of human crania from the Middle
Pleistocene
through the present—evidenced by a reduction in brow ridge
projection and a shortening of the upper facial region (70).
These
anatomical changes are hypothesized to result from a reduction
in
androgen activity and are consistent with the well-documented
effects of testosterone on craniofacial masculinization. Given
that

14. Fig. 1. Two bonobos share a piece of fruit. Bonobos share food
more tolerantly than chimpanzees, allowing them to collaborate
successfully even when rewards are potentially monopolizable.
Selection
for increased social tolerance and concern for the equitable
distribution
of resources was likely an important precursor to large-scale
cooperation
in humans. Figure courtesy of Jingzhi Tan (photographer).
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testosterone is linked to dominance and aggressive behavior in
men (71), these changes may reflect a more recent wave of se-
lection for increased social tolerance that allowed humans to
work
productively with conspecifics in new ways (72), as suggested
by
the proliferation of cultural artifacts 20–70 thousand years ago
(kya) (69, 73, 74).
In addition to its effects on temperament, a reduction in tes-
tosterone may also have directly affected aspects of human cog-
nition, including processes related to communication and the
theory of mind. Evidence for this possibility stems from the sys-
temizing–empathizing theory, developed by Baron-Cohen et al.
(75) and proposed as an explanation for the deficits in theory of
mind observed in autism spectrum disorder (76). In brief, the
the-
ory proposes that cognitive differences between males and fe-
males arise in part due to differential prenatal androgen
exposure.

15. Baron-Cohen reviews evidence that, at the population level, hu-
man males outperform females on tasks involving visuospatial
and
spatiotemporal abilities (77), presumably reflecting
“systemizing”
skills—or the tendency to analyze the world in terms of lawful
and
deterministic rules (78). In contrast, females exhibit an
advantage
with language (77), when interpreting facial expressions and en-
gaging in interactive social exchange—including sharing and
turn
taking—and girls reach certain theory-of -mind milestones
earlier
in development than do boys (79). Although some of these
effects
may be culturally driven, similar differences have been reported
in
nonhuman animals, some of which are diminished when males
are
castrated, or females are artificially androgenized (78). The sys-
temizing–empathizing hypothesis proposes that high levels of
androgen exposure during prenatal development can cause an
extreme masculinization of the brain, leading to a (pathological)
bias toward systemizing and resulting in deficits in empathic
pro-
cesses, such as the theory of mind. Baron-Cohen and coworkers
suggest that autism spectrum disorders—which are
characterized
by deficits in theory of mind—are caused by an “extreme male
brain” but also present data that, even in typically developing
humans, prenatal androgen exposure is predictive of autistic
traits
measured later in childhood (80).
Although the systemizing–empathizing hypothesis was pro-

16. posed to account for intraspecific variation in modern humans,
its
predictions may also partially account for species differences in
the
social cognition of great apes and key changes in human
cognitive
evolution. For example, bonobos—who exhibit signatures of
lower
prenatal androgen exposure than chimpanzees (81)—outperform
chimpanzees on some measures of theory of mind and co-
operation (48, 82), attend to the face and eyes more often than
chimpanzees when viewing social images (83), and share food
and
play socially more often as adults than chimpanzees (refs. 48
and
84; but see refs. 49 and 50)—all traits associated with empathiz-
ing. In contrast, chimpanzees outperform bonobos in tests of
tool
use, causal reasoning, and spatial memory (82, 85)—cognitive
traits associated with systemizing—and chimpanzees exhibit
more
severe aggression than bonobos (47), consistent with the pre-
dictions of the systemizing–empathizing hypothesis (Fig. 2).
Comparative brain imaging studies with chimpanzees and bono-
bos also reveal that bonobos have more gray matter in regions
implicated in empathy and more robust neural pathways relating
to the inhibition of aggression (86), with bonobos having ap-
proximately twice the serotonergic innervation of the amygdala
in
comparison with chimpanzees (53).
Assuming that human ancestors were endowed with the basic
perspective-taking skills found in other great apes, reductions in
androgen activity in recent human evolution may have been a
catalyst both for the elaboration of these abilities and for their
application to new types of cooperative social interactions.

17. Therefore, under this scenario, critical aspects of human-typical
social motivation (e.g., tolerance and gregariousness) and social
cognition (e.g., aspects of the theory of mind) may have evolved
in
parallel, due in part to similar biological mechanisms regulating
both sets of traits.
Testing Hypotheses About Cognitive Evolution
The evolutionary scenarios described above are speculative and
draw on data from relatively few (but phylogenetically
informative)
taxa to make inferences about key processes in human cognitive
Fig. 2. Cognitive and behavioral differences between
chimpanzees and bonobos that align with the predictions of the
systemizing–empathizing
hypothesis. B, bonobo; C, chimpanzee.
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evolution. Although human cognition evolved only once and
represents the final product of myriad incremental evolutionary
changes, comparative research with nonhuman animals provides
an opportunity to explicitly test hypotheses about how and why
some of these changes may have occurred (2). To date, few
studies
have adopted this approach—mainly due to the challenges of
compiling large and high-quality interspecific datasets on
animal
cognition—but several recent studies illustrate the value of ex-
plicitly comparative approaches for research in cognitive evolu-
tion. For example, MacLean et al. (87) conducted tests of self-
control in 36 species of vertebrates and uncovered robust links
between absolute (but not relative) brain size and skills for self-

18. control. These data raise the possibility that increases in
absolute
brain size, a defining feature in human evolution, may have
yielded
improved abilities for self-regulation, possibly supporting the
in-
creased social tolerance (e.g., through the inhibition of
aggression)
that is critical for human cooperation. Interestingly, in this
study,
there was no relationship between species-typical social group
size
and self-control, suggesting that merely living in larger social
groups is not sufficient to favor these abilities. However, in
another
experiment with lemurs, MacLean et al. (88) found that larger
species-typical social group sizes were associated with
increased
skill relevant to visual perspective taking (a key component of
the
theory of mind), corroborating the hypothesis that life in
complex
social groups was a driver for cognitive skills that allow
individuals
to outcompete others for access to food and mates. Therefore,
living in larger and more complex social networks may have fa-
vored the initial evolution of some components of the theory of
mind, which in humans have been repurposed in novel co-
operative contexts. With regard to the evolution of human co-
operative and prosocial motives, Burkart et al. (89) tested 15
primate species in a series of proactive prosociality tasks and
ex-
amined a range of socioecological predictors of species differ-
ences. In this case, the extent of allomaternal care (care for
young
provided by individuals other than the mother) was the best pre-

19. dictor of species differences in prosocial behavior. These
findings
are consistent with the idea that a shift from polygamy to co-
operative breeding may have been critical for the evolution of
uniquely human forms of cooperative psychology (90). Impor-
tantly, because early humans were most likely already endowed
with many components of theory of mind, the motivational
changes accompanying cooperative breeding may have provided
a catalyst for the application of these skills to the cooperative
settings in which shared intentionality became adaptive (68). In
contrast, other cooperatively breeding primates (e.g.,
callitrichids)
may lack human-like shared intentions because they possess the
motivational, but not the representational, foundations for these
processes, and vice versa for extant great apes who possess
some
of the requisite representational abilities but may lack the level
of
prosocial motivation found in cooperative breeders (68).
The studies above highlight the utility of phylogenetic com-
parative approaches for inferring how and why particular
aspects
of psychology evolve—including psychological traits believed
to
be critical for the human cognitive phenotype. However, few
such
studies have been conducted, and many of the hypotheses out-
lined throughout this article remain ripe for comparative study.
For example, do social tolerance and skills for cooperation or
social learning covary across nonhuman species? Closely
related
taxa that differ substantially in social tolerance—for example,
the
macaque radiation (91)—provide powerful opportunities for
assessing whether these traits may be functionally linked. Simi-

20. larly, if the influence of prenatal androgen exposure yields
systematic differences in cognition related to systemizing and
empathizing, one would predict that these effects should be ev-
ident in a range of taxa outside the great apes. For these types
of questions, comparative studies will be critical for assessing
whether hypotheses about human cognitive evolution align with
the patterns observed in other taxa. There is no doubt that
human
cognition is unique and composed of a constellation of traits
that
collectively may not cooccur in any other species. However,
many
important aspects of human cognition have homologies—and
often, more interestingly, analogies (resulting from convergent
evolution)—in other taxa, creating rich opportunities to make
in-
ferences about when, how, and why these traits evolve.
Lastly, in cases where aspects of human cognition seem
radically
different from those of other species, phylogenetic approaches
can
be used to assess whether humans should be considered an evo-
lutionary outlier (92, 93). For example, recent analyses have
shown
that, despite having many more neurons than any other primate
brain, the number of neurons in human brains is not remarkable
given the volume of the human brain and data on the general
cellular scaling rules of primate brains (94, 95). Similar
analyses can
be undertaken with cognitive traits to assess whether apparently
outlying observations in humans represent an extreme but pre-
dictable occurrence, taking into consideration primate
phylogeny
and a set of predictor variables that covary with the trait of

21. interest
across taxa. When human traits can be partially explained by
broader evolutionary patterns, comparative approaches will be
particularly useful for addressing questions about how, when,
and
why these traits evolved in humans. In cases where humans de-
viate substantially from broad-scale evolutionary patterns, these
findings suggest that humans could be considered an evolution-
ary outlier (96) and demand great caution in reconstructing how
and why these aspects of human cognition may have evolved.
Conclusions
Humans are unusual animals in many respects, but it is in our
species-typical cognition that human uniqueness evidences
itself
most prominently. The precise ways in which human cognition
differs from that of other species remains a topic of intense
debate
(14), but many data currently support the hypothesis that it is an
early emerging set of social skills for reasoning about
conspecifics
as intentional agents, coupled with a distinctly cooperative and
prosocial motivation, that fuels many of our most remarkable
cognitive achievements (97). Although the scientific literature
is
replete with attempts to identify single capacities that make hu-
man cognition unique, it is likely that human cognition is more
than the sum of its parts and is dependent on synergy between a
unique combination of representational and motivation traits.
Therefore, although the whole of human cognition may be un-
paralleled in the animal kingdom, key components of our cogni-
tive phenotype can be found in other taxa, including not only
great apes, but also more distantly related species bearing cog-
nitive resemblances to humans as a result of convergent evolu-
tion. Accordingly, new lines of research integrating
phylogenetic

22. comparative methods with the study of animal minds will play
an
essential role in our quest to determine not only what makes hu-
man cognition unique, but also how and why these traits
evolved.
Acknowledgments
I thank B. Hare, J. Tan, C. Krupenye, and two anonymous
reviewers
for helpful comments on an earlier draft of this manuscript, J.
Tan
for sharing the photograph in Fig. 1, and the Stanton Foundation
for
financial support during the time the manuscript was written.
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6354 | www.pnas.org/cgi/doi/10.1073/pnas.1521270113
MacLean
www.pnas.org/cgi/doi/10.1073/pnas.1521270113
You work in an international IT company which is named ABC.
As a network administrator you need to chop the whole network
into a number of subnets. Given the network address and the
number of subnets needed:
Number of subnets needed: 37
Network address: 182.186.120.0 / 24
Find:
(a) Number of bits borrowed: _______________
(b) Total number of subnets available: _______________
(c) Total number of hosts/subnet: __________________
(d) Total number of usable hosts/ subnet: ________________
(e) Write down the first three subnet address with slash
notation.
Subnet
Network Address
First Host IP
Last Host IP
Broadcast address
1

32. 2
3
(Marks: 8.0)
(f) A user in Subnet 1 was trying to ping a user in Subnet 2 and
the ping reply comes with the message “Destination Host
Unreachable”. Explain what action you should take in order to
solve the problem.
Table 1. Conceptual and pragmatic definitions of learning
surveyed from different disciplines
CONCEPTUAL DEFINITIONS: LEARNING AS THE
PROCESSING OF INFORMATION OR EXPERIENCE
Psychology
“We can divide all learning into (1) learning by trial and
accidental success, by the strengthening of the

33. connections between the sense-impressions representing the
situation and the acts—or impulses and
acts—representing our successful response to it and by the
inhibition of similar connections with
unsuccessful responses; (2) learning by imitation...”
Thorndike 1911/2000
[1]
“Learning is a relatively stable unspecified change within an
organism that makes a change in behaviour
possible; that is due to experience; and that cannot be accounted
for in terms of reflexes, instincts,
maturation, or the influence of fatigue, injury, disease or drugs”
Chance 1979 [2]
“Learning refers to the process by which an animal (human or
non-human) interacts with its environment
and becomes changed by this experience so that its subsequent
behaviour is modified”
Hall 2003 [3]
“The process of acquiring new and relatively enduring
information, behaviour patterns or abilities
characterised by modification of behaviour as a result of
practice, study or experience”
Breedlove et al 2007 [4]
“In a representational theory of learning, the brain computes a
representation of the experienced world, and
behavior is informed by that representation. By contrast, in
associative theories of learning, which
dominate neurobiological thinking, experience causes a plastic

34. brain to rewire itself to make behavior
better adapted to the experienced world, without the brain’s
computing a representation of that world”
Gallistel 2008 [5]
“[…]learning is a process of change that occurs as a result of an
individual's experience” Mazur 2013 [6]
“Learning is a process by which an organism benefits from
experience so that its future behaviour is better
adapted to its environment”
Rescorla 1988 [7]
Cognitive Psychology
“Learning is any process that modifies a system so as to
improve, more or less irreversibly, its subsequent
performance of the same task or of tasks drawn from the same
population.”
Langley and Simon
1981 [8]
"...learning is conceived in terms of the storage of information
in memory as a consequence of any
experience the individual might have had.”
Medin 2001 [9]
"Learning and memory involve a series of stages. Processes
occurring during the presentation of the
learning material are known as "encoding" and involve many
processes involved in perception. This is
the first stage. As a result of encoding, some information is
sorted within the memory system. Thus,

35. storage is the second stage. The third (and final) stage is
retrieval, which involves recovering or
extracting stored information from the memory system."
Eysenck and Keane
2010 [10]
"The [incidental] acquisition of knowledge about the structural
properties of the relations between objects Buchner and
Wippich
or events." 1998 [11]
Neuroscience
“Learning is the process of information input and processing as
well as storage, and, on the other hand it is
a product which changes in the behaviour of an animal due to
experience”
Korte 2013 [12]
“We define memory as a behavioral change caused by an
experience, and define learning as a process for
acquiring memory.”
Okano, et al. 2000 [13]
“Learning is the process by which we acquire knowledge about
the world, while memory is the process by
which that knowledge is encoded, stored, and later retrieved.”
Kandel, et al. 2000 [14]
“Learning is the process of acquiring new information.” Rudy

36. 2008 [15]
“[…]learning is the capacity to change behaviour as the result
of individual experience in such a way that
the new behaviour is better adapted to the changed conditions of
the environment”
Menzel 2013 [16]
Behavioral Ecology
“that process within the organism which produces adaptive
change in individual behaviour as a result of
experience”
Thorpe, 1943 [17]
“The process which produces adaptive change in individual
behaviour as the result of experience. It is
regarded as distinct from fatigue, sensory adaptation,
maturation and the result of surgical or other
injury.”
Thorpe 1951 [18]
“[…]learning can be defined as a process by which long lasting
changes in behaviour are acquired by
experience”
Sitter 1999 [19]
“[…]learning involves the acquisition, storage and retrieval of
information that can potentially affect
behavior”
Bekoff 2004 [20]

37. Machine Learning
“The capacity… to acquire or develop new knowledge or skills
from existing or nonexisting examples for
the sake of optimizing performance criterion.”
Alpaydin 2004 [21]
"A system is said to learn if it can acquire (synthesize)
declarative knowledge from data and/or it
displays performance/competence improvement through
practice".
Neri and Saitta 1997
[22]
“[Learning is] the acquisition of structural descriptions from
examples.” McQueen and Holmes
1998 [23]
“[Learning is] the process of forming general concept
definitions by observing specific examples of
concepts to be learned.
Haglin et al 2005 [24]
LEARNING DEFINED AS BEHAVIORAL CHANGE
Psychology
“[…]the acquisition, maintenance, and change of an organism's
behavior as a result of lifetime events” Pierce and Cheney 2008

38. [25]
“[…]more or less permanent change in behaviour that occurs as
a result of practice” Kimble 1961 [26]
“[…]change in behavior that occurs as the result of practice”
Dewsbury 1978 [27]
“[…]specific and only partly reversible change [in behavior],
often related to a positive or negative
outcome”. “Experience can change behavior in many ways that
manifestly do not involve learning”
Staddon 1983 [28]
“[…]changes in the behavior of an organism that are the result
of regularities in the environment of that
organism”
De Houwer et al. 2013
[29]
Neuroscience
“[…]any fairly persistent change in behavioral attributes
produced by the action of experience on the
central nervous system”
Krasne 1976 [30]
“[…]a change in the behavior of an animal as a consequence of
the animal’s experience” Delcomym 1998 [31]
“Learning is a change in an organism’s behaviour as a result of
experience” Kolb & Whishaw 2011
[32]
“[…]a relatively permanent change in behavior that results from
experience” Kolb & Whishaw 2014

39. [33]
Behavioral Ecology
‘[…]a change/modification in behaviour with experience’
Shettleworth 1984 [34];
van Alphen and Let
1986 [35]; Szentesi &
Jermy 1990 [36];
Vet et al. 1990 [37];
Stephens 1993 [38];
Barron 1999 [39]
“a reversible change in behaviour with experience”
Papaj & Prokopy, 1986
[40]
No longer willing to define learning. Instead, offer criteria to
specify learning
1. The individual’s behavior changes in a repeatable way as a
consequence of experience
2. Behavior changes gradually with continued experience
3. The change in behavior accompanying experience wanes in
the absence of continued experience of the
Papaj & Prokopy, 1989
[41]
same type or as a consequence of a novel experience or trauma
“Learning is the adaptive modification of behaviour based on
experience” Alcock 2005 [42]

40. “a change in state due to experience” Shettleworth 2010 [43]
“Learning is the modification of behaviour due to stored
information from previous experience” Breed 2012 [44]
Machine Learning
"A computer program is said to learn from experience E with
respect to some class of tasks T and
performance measure P, if its performance at tasks in T, as
measured by P, improves with experience
E"
Mitchell 1997 [45]
“Things learn when they change their behaviour in a way that
makes them perform better in the future.” Witten and Frank
2005
[46]
LEARNING DEFINED AS CHANGES IN BEHAVIORAL
MECHANISMS
Psychology
“[…]the process by which a relatively stable modification in
stimulus-response relations is developed as a
consequence of functional environmental interaction via the
senses”
Lachman 1997 [47]
“[…]an enduring change in the mechanisms of behavior
involving specific stimuli and/or responses that
results from prior experience with those or similar stimuli and
responses”

41. Domjan 2010 [48]
“[…]a long-term change in mental representations or
associations as a result of experience” Omrod 2012 [49]
Neuroscience
“[Learning is] either a case of the differential strengthening of
one from a number of more or less distinct
reactions evoked by a situation of need, or the formation of
receptor-effector connections de novo; the
first occurs typically in simple selective learning and the
second, in conditioned-reflex learning"
Hull 1943 [50]
“Learning is a manifestation of the malleability of the nervous
system because it is a change in the behavior
of an animal based on experience. Memory refers to the stored
experience and to the process by which
it is stored. Memory is a requirement for learning.”
Delcomym 1998 [31]
Offer no definition of learning – rather provide mechanistic
definitions of specific learning subtypes, e.g.
habituation.
Carew 2000 [51];
Schwartz, et al. 2002
[52]; Kalat 2007 [53];
Reznikova 2007 [54];
Squire, et al. 2008 [55];
Gluck, et al. 2008 [56]

42. Behavioral ecology
“[…]the acquisition of neuronal representations of new
information” Dukas, 2009 [57]
“Learning is a change in the nervous system manifested as
altered behavior due to experience” West-Eberhard 2003
[58]
“Learning is a specific change or modification of behaviour
involving the nervous system as a result of
experience with an external event or series of events in an
individual’s life”
Grier 1992 [59]
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Embracing multiple definitions of learning
Andrew B. Barron1*, Eileen A. Hebets2*, Thomas A. Cleland3,
Courtney L. Fitzpatrick4,
Mark E. Hauber5, and Jeffrey R. Stevens6,7
1
Macquarie University, Department of Biological Sciences,
North Ryde, NSW 2109, Australia

49. 2
School of Biological Sciences, University of Nebraska-Lincoln,
NE 68588, USA
3
Department of Psychology, Cornell University, Ithaca, NY
14853, USA
4
National Evolutionary Synthesis Center, Durham, NC, 27708,
USA
5
Department of Psychology, Hunter College and the Graduate
Center, City University of New York, NY 10065, USA
6
Department of Psychology, University of Nebraska-Lincoln, NE
68588, USA
7
Center for Brain, Biology and Behavior, University of
Nebraska-Lincoln, NE 68588, USA
Forum
Definitions of learning vary widely across disciplines,
driven largely by different approaches used to assess
its occurrence. These definitions can be better reconciled
with each other if each is recognized as coherent with a
common conceptualization of learning, while appreciat-
ing the practical utility of different learning definitions in
different contexts.
The challenges of defining learning
Learning is a major focus of research in psychology, neuro-

50. science, behavioral ecology, evolutionary theory, and com-
puter science, as well as in many other disciplines. Despite
its conceptual prevalence, definitions of learning differ enor-
mously both within and between these disciplines, and new
definitions continue to be proposed [1]. Ongoing disputes
over the definition of learning generate uncertainty regard-
ing the boundaries of the learning concept and confuse
assessments about which phenomena genuinely constitute
learning. These disputes impair transdisciplinary collabo-
ration and synthesis between conceptually related fields.
Many of the definitions in use by these different disciplines,
however, can be aligned with a common ‘umbrella concept’ of
learning that can be applied across disciplines by consider-
ing learning simply as the processing of information derived
from experience to update system properties [2–5]. Many of
the definitions also have clear practical utility in that they
reflect a variety of approaches to determine whether or how
learning has occurred. We argue that embracing the multi-
ple definitions defined by individual subfields (Table S1 in
the supplementary material online) – while simultaneously
recognizing their shared relationship to this umbrella con-
cept – will facilitate the integration of neurophysiological,
psychological, computational, and evolutionary approaches
to learning.
The difficulty of establishing a single satisfactory scien-
tific definition for learning has long been recognized
[6]. Perhaps owing to this difficulty, many contemporary
psychology and neuroscience textbooks avoid defining
0166-2236/
� 2015 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.tins.2015.04.008
Corresponding author: Hauber, M.E. ([email protected]).
Keywords: definitions; experience dependence; function;

51. learning; mechanism;
plasticity.
*Shared first authorship.
learning altogether, preferring instead to explain specific
experimental subtypes of learning (such as operant condi-
tioning or habituation) for which it is easier to offer an
experimentally supported definition (Table S1). A weak-
ness of this approach, of course, is that it discourages
engagement with the complexity of the learning concept
and its manifestations within different areas of study.
While the specific definitions of learning can vary sub-
stantially among fields and even within fields (Table S1),
most contemporary theoretical considerations of learning
view it as a structured updating of system properties based
on processing of new information [2–5]. This concept of
learning can operate across disciplines. It does not neces-
sarily imply specific mental states, cognitive processes, or
processing by neurons. It does not limit learning to complex
brains: learning can be instantiated in machines or reflex
arcs. It emphasizes that learning is not behavioral change;
however, changes in behavior, neural systems, or other
elements of the performance of a system all can be useful
and practical experimental methods to assess whether
learning has occurred.
Despite this general underlying conceptual consensus,
there is a wide range of highly specified definitions of
learning that vary between disciplines. These variations
often arise out of the endeavors of the experimental scien-
tist. Because learning is a concept of information processing,
it can rarely be measured directly: instead, it is often
inferred to have taken place by changes in the (biological,
artificial, or virtual/computational) system’s properties or
performance. For this reason a range of pragmatic defini-
tions of learning delimit the concept in such a way that it can

52. be addressed experimentally [1,7]. Many define learning as
a change in behavior, and some define learning as changes
in the mechanisms that enable behavioral change
(Table S1). These pragmatic definitions vary between dis-
ciplines and have merit and utility in different experimental
circumstances. By appreciating the situational advantages
of these different perspectives, and by describing how the
term is being employed in a specific context, scholars of
learning can minimize confusion within fields of study and
facilitate the meaningful translation of studies of learning
across the disciplines.
Learning as a change in behavior
Learning is commonly defined as behavioral change. Early
on, Skinner [6,8], promoted this approach by arguing that,
Trends in Neurosciences, July 2015, Vol. 38, No. 7 405
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4.008&domain=pdf
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Trends in Neurosciences July 2015, Vol. 38, No. 7
because learning is usually determined by assessing be-
havioral change, defining learning as the behavioral
change or altered behavioral outcome per se eliminates
the need for speculative inference about (hidden) underly-
ing processes. Likewise, De Houwer [1,7] has more recently
advocated for defining learning as behavioral change be-
cause this ‘functional’ approach is more verifiable and
generalizable than mechanistic definitions, which require
direct knowledge of internal processes. Similar functional
definitions of learning are most common in disciplines that
focus on the evolution of behavioral outcomes and their

53. consequences, including evolutionary and ecological re-
search (Table S1). For instance, mathematical models of
evolution that include changes in behavior due to learning
most often take a functional approach and define learning
as behavioral change, because – rather than being con-
cerned with underlying physiological processes – they are
concerned with the ultimate fitness effects of the pheno-
typic changes caused by learning. Learning can be modeled
simply as non-genetic inheritance (e.g., song learning from
parents) [9] or as within-generation plasticity of a behav-
ioral phenotype (e.g., song learning from peers) [10]. Nota-
bly, while such models make few assumptions about
mechanisms, they nonetheless contribute to mechanistic
understandings of learning, its ecological distribution, and
its evolutionary consequences.
However, defining learning as behavioral change suffers
from significant limitations. Domjan [11], for example, has
argued that when defining learning as altered behavior, it is
both practically and philosophically difficult to disentangle
how much of a given behavioral change results from learning
and how much may result from other factors, such as altered
motivation, physiological changes, or muscle fatigue, matu-
ration, or damage [11,12]. For this reason, some definitions
of learning require changes in specific physiological mecha-
nisms that support learning to clarify the distinction be-
tween learning and other possible causes of behavioral
change (e.g., spraining an ankle and walking more slowly
thereafter) [11]. The limitation of these mechanistic defini-
tions is that they require identification and measurement of
the underlying physiological mechanisms of learning. Ac-
cordingly, such definitions of learning occur frequently in
the psychological and neural sciences (Table S1) [5,11].
As an alternative strategy to distinguish the effects of
learning from other factors that could affect behavior,

54. authors often attach various riders to behavioral defini-
tions of learning to constrain the definition. Many of these
qualifiers are negative, yielding lengthy discussions of
what forms of behavioral change do not reflect learning.
However, the most common positive qualifier is that learn-
ing depends on ‘experience’.
Learning and experience
Experience is strongly linked to the learning concept be-
cause experience is assumed to be the source of the infor-
mation that is learned [4,5]. Whereas experience is part of
most definitions of learning (Table S1), it is rare to find a
scientific definition of experience, or a discussion of what
experience is [13]. Furthermore, the definitions that do
exist recapitulate the imprecisions of some learning defini-
tions. For example, experience has been defined as an
406
environmental event that is perceived by an organism
and that can alter behavior [12]. However, the experience
of a startling noise may effect a behavioral response with-
out this response being considered learning [1]. Thus,
learning may depend on experience, but not all experiences
will be learned.
Moreover, the requirement that the event must be
perceived by the organism to be considered experience
has been criticized on functional grounds because it blurs
the line between the sensation of detectable environmental
events and the inference of cognitive processing [14]. This
is particularly problematic for animal behavior research,
which frequently assumes, but does not test internal men-
tal states and events for non-human animals. These pro-
blems are reduced if experience is considered simply as a
source of information. Viewed in this way, experience does
not presuppose any particular mental events.

55. Is it necessary to know what has been experienced to
claim that learning has occurred? As Rescorla [5,15] has
clearly argued, it can be very misleading to assume, rather
than test explicitly, what is being learned from any expe-
rience. For example, classical conditioning theorists origi-
nally considered learning to be a process by which a
behavioral response transferred to a conditioned stimulus,
whereas the contemporary perspective recognizes classical
conditioning as learning the relationship between stimuli
[5]: a radical change in perspective regarding what is
learned in classical conditioning. For a small number of
established laboratory neuroscience protocols with model
systems and controlled stimulus presentation, there has
been good experimental analysis of what is being learned.
For ethological or ecological data about learning in the
wild, however, it is often uncertain which environmental
events are salient to the animal, which convey information,
or precisely what has been learned. Although the terms
‘experience-dependence’, ‘behavioral plasticity’, and ‘in-
duced behavioral change’ appear increasingly in place of
‘learning’, we believe this is not constructive. There is no
compelling reason to limit the use of ‘learning’ to situations
where the nature of the experience is known or assumed.
To do so would invite serious errors of interpretation, and
inhibit transdisciplinary syntheses of learning by frag-
menting the discussion of clearly related phenomena.
An integrative perspective on learning
As with other complex concepts such as ‘fitness’ and ‘gene’,
there is no single definition of ‘learning’ that can best serve
all scientific purposes, or satisfy all fields and researchers.
Disciplines differ in their specific definitions of learning for
pragmatic reasons, but it is possible to reconcile most of
these definitions by reference to a common theoretical
framework: learning as a structured updating of system
properties based on the processing of new information.

56. Accordingly, acknowledging the different meanings of
learning and being clear on how the term is being used
in specific studies are the most effective ways to facilitate
transdisciplinary research.
Acknowledgments
This study was part of a working group on decision making
sponsored by
the National Evolutionary Synthesis Center (NESCent),
National Science
Forum Trends in Neurosciences July 2015, Vol. 38, No. 7
Foundation (NSF; grant EF-412 0905606). Additional funding
was
provided by NSF IOS-145624 to M.E.H. We thank the leaders
and all
members of the working group for stimulating discussions, and
especially
Kim Hoke, Maria Servedio, Rafael Rodriguez, and an
anonymous referee
for providing comments on this manuscript.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online
version, at http://dx.doi.org/10.1016/j.tins.2015.04.008.
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challenges of defining learningLearning as a change in
behaviorLearning and experienceAn integrative perspective on
learningAcknowledgmentsAppendix A Supplementary
dataReferences