The evolution of cognitive mechanisms in response to cultural innovations.

The evolution of cognitive mechanisms in response to
cultural innovations
Arnon Lotema, Joseph Y. Halpernb, Shimon Edelmanc, and Oren Kolodnyd,1
aDepartment of Zoology, Tel Aviv University, Tel Aviv 6997801, Israel;bDepartment of Computer Science, Cornell University, Ithaca, NY 14850;cDepartment
of Psychology, Cornell University, Ithaca, NY 14850; anddDepartment of Biology, Stanford University, Stanford, CA 94305
Edited by Kevin N. Laland, University of St. Andrews, St. Andrews, United Kingdom, and accepted by Editorial Board Member Andrew G. Clark April 29, 201 7
(received for review January 20, 2017)
When humans and other animals make cultural innovations, they
also change their environment, thereby imposing new selective
pressures that can modify their biological traits. For example,
there is evidence that dairy farming by humans favored alleles for
adult lactose tolerance. Similarly, the invention of cooking possibly
affected the evolution of jaw and tooth morphology. However,
when it comes to cognitive traits and learning mechanisms, it is
much more difficult to determine whether and how their evolution
was affected by culture or by their use in cultural transmission. Here
we argue that, excluding very recent cultural innovations, the
assumption that culture shaped the evolution of cognition is both
more parsimonious and more productive than assuming the
opposite. In considering how culture shapes cognition, we suggest
that a process-level model of cognitive evolution is necessary and
offer such a model. The model employs relatively simple coevolv-
ing mechanisms of learning and data acquisition that jointly
construct a complex network of a type previously shown to be
capable of supporting a range of cognitive abilities. The evolution
of cognition, and thus the effect of culture on cognitive evolution,
is captured through small modifications of these coevolving learn-
ing and data-acquisition mechanisms, whose coordinated action is
critical for building an effective network. We use the model to show
how these mechanisms are likely to evolve in response to cultural
phenomena, such as language and tool-making, which are asso-
ciated with major changes in data patterns and with new compu-
tational and statistical challenges.
tool-making |language evolution |niche construction |
cognitive evolution |social learning
An open question in the study of culture and cognitive evo-
lution is whether (and to what e xtent) cognitive mechanisms,
especially those viewed as advanced or sophisticated, evolved in
response to social-learning challenges or are merely the product of
domain-general mechanisms (1 –3). According to one view —still
widely held in cognitive science and evolutionary psychology —cog-
nitive adaptations take the form of specialized brain modules (or
neuronal mechanisms) that evolved for specific, often social pur-
poses, such as “imitation ”(4, 5), “mind reading ”(6, 7), “cheating
detection ”(8), or most famously, languag e acquisition (9, 10). These
ideas have been criticized on theoretical and empirical grounds (11,
12), and the debate around them demonstrates our limited un-
derstanding of the evolution of cognition, its relationship to the
evolution of social behavior and, in some organisms, culture.
The question of whether culture and social behavior shape the
evolution of the brain is, in our view, best considered using the
evolutionary framework of niche construction (13 –16): that is,
culture and social behavior change the ecological niche to which
cognitive traits must adapt in the same manner that nest-building
by birds changes the ecological niche in which their nestlings evolve.
For example, animals ’ability to learn from each other may have
initially been a by-product of domain-general associative learn-
ing mechanisms that did not evolve for social learning (1).
However, as soon as these mechanisms enabled social learning
and were recruited by it for regular use, social learning and its
outcomes also became part of their ecological niche. From thatmoment on, learning mechanisms that need not have initially been
specifically social were also selected according to their ability to
support social learning. If that is the case, one can certainly claim
that these mechanisms were adapted or shaped to serve their new
social function (although using the term “evolved for ”may still be
premature without knowing the degree of genetic modification
and specialization).
Similarly, when social learning enables the accumulation or
spread of shared group behaviors —these days recognized as the
formation of “culture ”(17, 18) —this culture becomes the new
ecological niche for all of the learning mechanisms that con-
tribute to it, and therefore has the potential to shape their evolution.
Thus, in theory, given sufficient evolutionary time, cultural phe-
nomena that are adaptive for the individual, and whose acquisition is
supported by advanced learning or cognitive skills, such as the ability
to imitate or to learn language, ar e expected to select for improve-
ments in these cognitive skills (see also ref. 19). In practice, however,
clear evidence showing the effect of culture on cognition is lacking,
and alternative accounts for the evolution of advanced cognition
and culture through domain-general learning principles cannot
be ruled out (1, 2, 20). As a result, whether and how culture
really shapes the evolution of cognition is still under debate.
In what follows, we first clarify some of the theoretical issues in
this debate, using two recent controversies in the fields of language
evolution and social learning. We then offer a process-level ap-
proach to cognitive evolution that may be useful in predicting what
aspects of learning and cognition are likely to coevolve with cul-
ture. Finally, we use the model to demonstrate how cultural
phenomena such as language and tool-making (each related to
one of the two controversies discussed earlier) are likely to shape
cognition, given their association with changes in data distribution
and with new computational and statistical challenges.
Can Culture Evolve Without Shaping Cognition? On
Parsimony, Likelihood, and Scientific Productivity
Evolution takes time, so it is clear that very recent cultural in-
novations, such as cars, computers, cellular phones, or the In-
ternet, could not have yet generated detectable effects (or
perhaps any effect at all) on the evolution of cognition. But what
about relatively ancient cultural phenomena, such as song-
learning in birds or tool-making and language acquisition in
humans? Although there is evidence for the effect of human
culture on biological traits and gene frequencies (21), evidence
for specific effects of human culture on learning and cognitive
mechanisms is mostly circumstantial. This evidence includes
This paper results from the Arthur M. Sackler Colloquium of the National Academy of
Sciences, “The Extension of Biology Through Culture, ”held November 16 –17, 2016, at the
Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering
in Irvine, CA. The complete program and video recordings of most presentations are available
on the NAS website at www.nasonline.org/Extension_of_Biology_Through_Culture .
Author contributions: A.L., J.Y.H., S.E., and O.K. designed research, performed research,
and wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. K.N.L. is a guest editor invited by the Editorial
Board.
1To whom correspondence should be addressed. Email: okolodny@stanford.edu.
www.pnas.org/cgi/doi/10.1073/pnas.1620742114 PNAS |July 25, 2017 |vol. 114 |no. 30 |7915 –7922
EVOLUTION PSYCHOLOGICAL AND
COGNITIVE SCIENCESCOLLOQUIUM
PAPER
signs of selection on genes implicated in brain growth, learning,
and cognition (22 –24) that may be attributed to human culture
(21), and differences in gene expression in the brain between
human and nonhuman primates (25, 26) that may be interpreted
similarly. There is also recent evidence relating structural changes
in the human brain to Paleolithic tool-making abilities (27, 28),
but additional work is still needed to clarify the direction of
causation between culture and cognition (we will return to dis-
cuss these findings toward the end of the paper). Finally, when
animal culture is considered, a recent study (29) suggests an
effect of culture on learning in songbirds: a larger repertoire size
is found in species that developed open-ended learning ability.
The lack of clear empirical evidence for the effect of culture on
cognition is not surprising, giv en that cognitive mechanisms and
their genetic underpinning are still poorly understood, making it
difficult to track their evolution ( as opposed to that of clearly de-
fined biological traits). As a result, much of the debate is focused on
theoretical arguments of plausi bility and likelihood, which may be
interpreted differently by psycholog ists and evolutionary biologists.
We use two examples of such controversies to illustrate this prob-
lem and to suggest a methodologically productive resolution.
Problem 1: The Evolution of Social-Learning Mechanisms. Social
learning is broadly defined as learning that is influenced by ob-
servation or interaction with other individuals or with the
products of their behavior (30). This definition leaves open the
question of whether social-learning mechanisms have evolved
specifically to serve their social function or whether they are
domain-general associative learning mechanisms that are also
used to learn socially. In a series of thought-provoking papers,
Heyes and her colleagues (1, 2, 31 –33) have demonstrated that
most mechanisms of social learning and imitation that are nor-
mally viewed as specialized adaptations for social life (4, 5, 34)
can also be explained by domain-general associative learning
principles. In this light, and in the absence of convincing evi-
dence to the contrary, they also suggested that there is no need
to posit that these domain-general mechanisms were shaped by
their social or cultural function (35). In other words, it would
appear more parsimonious to assume that these mechanisms did
not evolve beyond their initial domain-general form. However,
this appeal to parsimony is somewhat misleading in evolutionary
contexts and time scales, where changes are actually to be
expected (36, 37). In fact, for most evolutionary biologists, it
would appear highly unlikely that some learning mechanisms
would be used for many generations to serve social functions, yet
remain unaffected by this new social niche. It would almost be
like expecting the surface of the moon to remain unmarked with
craters after millions of years of exposure to space debris.
To explain why, imagine a population of finches that expands
its range. The beaks of these finches have a certain morphology
that evolved to handle seed types present in the original habitat;
the expanded habitat includes novel types of seeds. In this set-
ting, the assumption that after many generations there would be
no evolutionary change in beak morphology is not at all parsi-
monious. For a biological trait to remain unchanged over time,
an active process of stabilizing selection is necessary, otherwise it
will change through directional selection or genetic drift. That is,
the probability that the new types of seeds will not affect the
previous stabilizing selection regime is extremely small.
Returning to the evolution of learning, and following the same
reasoning, it is difficult to imagine how adding a new function for a
basic learning mechanism would not affect its evolution. As in the
beak example, the change may be subtle, and merely quantitative.
This is in part because a bird has only one beak that must serve
many different functions (from foraging for different types of food
to feather-preening and nest-building); it cannot be specialized
into different kinds of beaks. However, even in this case of a single
multipurpose adaptation, every new function must affect evolu-
tion; we would expect a more significant change if specialization is
possible. Similarly, even if all cognitive functions are supported by
the same domain-general learning mechanisms, it is unlikely thatsocial learning and culture could evolve without somehow affect-
ing these mechanisms. In reality, of course, unlike in the beak
example, learning mechanisms are not necessarily constrained to
be uniform across all domains; there is plenty of evidence for
adaptive specialization in associative learning mechanisms (e.g.,
refs. 38 –40).
The key point in this evolutionary argument is that, even in the
absence of supportive evidence, it is more parsimonious to as-
sume that learning mechanisms were shaped by their social
function and, if the relevant evidence is lacking, that we have
simply failed to find it, than to assume the opposite. The as-
sumption of no change requires us to posit a lack of genetic
variance in learning mechanisms, which runs counter to sub-
stantial evidence (39, 41 –45), or else to explain how the selection
regime was miraculously unaffected by the new social niche. The
assumption of evolutionary change may also be viewed as sci-
entifically more productive, as it encourages further research
(46). This implies that a useful working hypothesis should be that
“culture did shape cognition ”and that we have to find out how.
Demonstrating that social learning and the so-called “mirror
neurons ”phenomenon can be explained by associative learning
principles (1, 2) is important and consistent with our view. But
given the evolutionary argument above, the demonstration need
not imply that these associative learning mechanisms did not
evolve beyond their basic state (see also ref. 47). Indeed, the ar-
gument suggests that associative learning mechanisms are the
building blocks of cognitive evolution, and are finely tuned to
serve new social, cultural, and other advanced functions (48). This
view may help us make the shift from postulating “black box ”
adaptations that evolve “for”particular social purposes to having
credible process-level mechanistic models of cognitive evolution.
Problem 2: The Evolution of Language and Memory Constraints. The
same evolutionary argument discussed above is also relevant to
the question of whether or not human language shaped the
evolution of cognition (16, 49 –52). It implies that it is highly
unlikely that the use of language for at least several thousand
generations [or even more (53 –55)] failed to affect some aspects
of learning and cognition. The real question is not whether or
not it did, but rather what aspects were affected and how. For
example, Chater et al. have pointed out correctly that many as-
pects of human language change too fast for genetic evolution to
respond (56). The authors used computer simulations to show
that even in the presence of genetic variation, cultural conven-
tions of language are like “moving targets ”for natural selection,
making the evolution of genetic adaptations to specific languages
highly implausible. However, although this analysis makes a
convincing argument against strong nativism (the claim that
there is a significant innate component to human language), it
also implies that genes for language can evolve if they serve
general skills for language learning that are stable over time (49,
56, 57). In other words, the need to learn a language may still
select for many general cognitive abilities, such as better mem-
ory, computational abilities, or greater attention to verbal input.
Indeed, it does not seem possible that language could evolve
without affecting such abilities.
Interestingly, in a more recent paper, Christiansen and Chater
(58) have extended their view that language cannot shape the
evolution of cognition by proposing that the limited sensory
memory span —the window of time during which a linguistic ut-
terance is retained in its entirety —creates a bottleneck that
strongly constrains language and cannot evolve to become wider.
The authors suggest that language has evolved to cope with this
memory limitation, but that the evolution of this memory limita-
tion was not affected by language. As we noted elsewhere (59),
this assumption runs counter to the evolutionary argument above
and to substantial evidence for genetic variance in memory pa-
rameters (60 –63). In a reply to this criticism, Chater and Chris-
tiansen (64) explained that the memory bottleneck cannot be
viewed as a genetically variable trait that can respond freely to
selection because it “emerges from the computational architecture
7916 |www.pnas.org/cgi/doi/10.1073/pnas.1620742114 Lotem et al.
of the brain. ”This answer, however, merely kicks the can down the
road, by moving the problem from the domain of memory to that of
the computational architecture of the brain. The same arguments
hold: although constrained by many factors, the computational
architecture of the brain is shaped by the sum of selective pressures
arising from the need to accommodate the multiple functions of the
brain that influence the organism ’s fitness. Even if the memory
bottleneck emerges as a product of this architecture, it can still
evolve as long as this architecture evolves. If the challenge of
processing and using language, which affects individuals ’fitness and
has been in place for thousands of generations, has played a role in
shaping this architecture and its e mergent properties, then, as one
of these emergent properties, me mory should have been affected.
Specifically, as we proposed in the past (59) and explain further
below, the selective pressure exer ted by language learning may have
acted to limit the working-memory buffer, as this may be useful for
coping with the computational challenges involved in data seg-
mentation and network construction.
Our two examples —social learning and memory bottleneck —
suggest that, excluding very recent cultural innovations, it is
unlikely that culture could have evolved without shaping learning
and cognition. This forces us to think more specifically about
how culture shapes cognition, which requires, as we claim next,
adopting a process-level approach to cognitive evolution: that is,
a mechanistic model that explains a behavior or an ability as the
outcome of a process. Such a model may also help provide a useful
structure for reexamining the two problems outlined above.
Why Do We Need a Process-Level Approach to Theorize
About Culture and Cognition?
Whereas it is relatively easy to see how natural selection acts on
clearly defined morphological traits, such as limbs, bones, or
coloration, with cognitive traits that are not well understood, it is
difficult to tell what is actually evolving. Cognition is not a
physical trait, but an emergent property of processes that are
carried out by multiple mechanisms, most of which involve
learning. Thus, to consider how culture shapes the evolution of
cognition, we must explain how such mechanisms work and how
they can be modified by natural selection. The importance of
using mechanistic models in the study of behavioral evolution is
increasingly recognized (65 –68), but most attempts to integrate
evolutionary theory and cognition are still based on modeling the
evolution of learning rules that are far too simple to capture
complex cognition (69 –74). To understand how culture shapes
the evolution of cognitive mechanisms, such as those serving
imitation, theory of mind, or language acquisition, it is necessary
to have models that explain how such mechanisms work and how
they could evolve.
Clearly, given the immense complexity of the brain, any at-
tempt to propose a general process-level model of advanced
cognition would be ambitious. However, we believe that it is
possible and necessary to start by constructing models that cap-
ture some of the key working principles of advanced cognitive
mechanisms in a manner that suffices to explain their evolution.
An analogy that may clarify our approach is the apparent chal-
lenge in explaining the evolution of the eye. The vertebrate eye is
highly complex; it is initially hard to see how it could have
evolved. However, with a minimal understanding of how the eye
works, the “magic ”is removed (75). The basic eye model is a
layer of photosensitive cells; the visual acuity it provides can
gradually improve as it buckles into a ball-like shape, looking
(and working) more and more like a pinhole camera. This sketch
ignores many details, and is far from explaining everything about
eyes and vision, but is sufficient to resolve the puzzle.
This is the kind of modeling approach that we seek for explaining
cognitive evolution. Specifically, we do not seek a fully detailed
neuronal-level model of brain and cognition. Instead, we want a
minimal set of principles that suffice to explain how simple oper-
ational units, capable of only the most basic forms of learning, can
jointly and gradually create the much more sophisticated mecha-
nisms of advanced cognition. [Powerful algorithms using deeplearning (76, 77), with less emphasis on biological and behavioral
realism (78), are being developed and applied to challenging
tasks, including: perceptual parsing, associative learning, and the
learning of conceptual contingencies (79, 80).]
Once we have such a minimal model, we can consider how
small variations in the basic operational units and their param-
eters can build better, or different, cognitive mechanisms and
how this evolutionary process can be shaped by culture. Over the
past few years, we have developed such a model and explored its
ability to explain a range of phenomena. In the following sec-
tions, we briefly describe this model and use it to consider how
culture may shape the evolution of cognition and, in particular,
how such a model may help resolve the abovementioned con-
troversies regarding social-learning mechanisms and memory
constraints on language.
A Process-Level Model of Cognitive Evolution
The model presented here has already been described in several
of our previous papers (81 –87). Some of the main aspects of the
model were implemented in a set of computer simulations,
demonstrating a gradual evolutionary trajectory, from simple
associative learning, to chaining, to seldom-reinforced continu-
ous learning [in which a network model of the environment is
constructed (84)], to complex hierarchical sequential learning
that can support advanced cognitive abilities of the kind needed
for language acquisition and for creativity (85, 87). For the latter,
our modeling framework had to go well beyond chaining through
second-order conditioning (84, 88). The success of a computer
program, originally developed to simulate the behavior of ani-
mals learning to forage for food in structured environments (85),
in reproducing a range of findings in human language (86) sug-
gests that the model may be useful in the study of cognitive
evolution. Thus far the model ’s implementation has been lim-
ited, for simplicity, to an unsupervised learning mode with a
learning phase, and then a test phase during which the learners
act based on what they have learned. Its extension to accom-
modate iterated cycles of learning and action, which is necessary
to capture the learning of behavioral contingencies through trial
and error, is straightforward. Detailed pseudocode for our model
is found in the supplementary material of refs. 85 and 86.
The model is based on coevolving mechanisms of learning and
data acquisition that jointly construct a complex network that
represents the environment and is used for computing adaptive
responses to challenges in the environment. In particular, the
network is used for search, prediction, decision making, and
generating behavioral sequences (including language utterances,
when applicable). The extent to which learners ’use of the net-
work produced adaptive behaviors was measured in our imple-
mentation by foraging success [in the context of animal foraging
(84, 85, 87)] and by a set of language performance scores [in the
context of language learning (86)]. Although the production of
adaptive behaviors depends on the structure of the network, this
structure is not directly coded by genes and therefore cannot
evolve directly. The components that may evolve over genera-
tions are the parameters of the learning and data-acquisition
mechanisms that construct the network through interaction
with the environment, and whose coordinated action, as we show
below, is critical for building the network appropriately. [This
coevolution is very much in the spirit of the notions of con-
structive development and reciprocal causation in the recently
proposed “extended evolutionary synthesis ”(89).]
Constructing a Network. We now briefly sketch the main principles
that govern how the network is constructed. (This technical de-
scription may become clearer and more intuitive after reading
the simplified example outlined in the next subsection and il-
lustrated by Fig. 1). We assume that what data are acquired by
the learner is determined by its “data-acquisition mechanisms ”:
the collection of sensory, attentional, and motivational mecha-
nisms that direct the learner to process and acquire whatever is
deemed relevant. These mechanisms [also referred to as “input
Lotem et al. PNAS |July 25, 2017 |vol. 114 |no. 30 |7917
EVOLUTION PSYCHOLOGICAL AND
COGNITIVE SCIENCESCOLLOQUIUM
PAPER
mechanisms ”(33)] determine the content and the distribution of
different data items in the input. For simplicity, we assume that
the input takes the form of strings of symbols (i.e., linear se-
quences of discrete items), which are then processed through a
limited working-memory buffer, similar to the “phonological loop ”
in humans (90, 91), and tested for familiar segments and statistical
regularities among their components. This is done by the learning
mechanisms in a sequence of steps.
A data sequence is scanned for subsequences that recur within
it and for previously learned subsequences, and is segmented
accordingly. This results in a series of chunks, which are either
previously known or are incorporated at this point into a network
of nodes that represents the world as it has been learned so far
(nodes stand for objects or other meaningful units; the links in
the network represent their association in time and space).
Weights are assigned to the nodes and to the links to reflect their
frequency of occurrence: links between nodes are established
whenever two nodes follow one another in the input. The weight
of a node or a link is increased whenever it is encountered in the
input; the weight also decreases with time if it is not encoun-
tered. This process ensures that only those units and relations
that are potentially meaningful are retained in memory, and
spurious occurrences are forgotten. If a node ’s or link ’s weight
increases above a fixation threshold, its decay becomes highly
improbable. The probability that a data item is learned is thus
determined by how frequently it is encountered in the data, andby the parameters of weight increase and decrease. These pa-
rameters create a window for learning, during which data can be
either retained or discarded from the network.
We assume that if a data sequence reaches the threshold weight
for memory fixation, it remains in memory and is not segmented
any further. An intuitive example from language learning is a word
such as “backpack, ”which would be fixed in memory if it were
heard repeatedly without prior exposure to instances of “back”or
“pack”(not even within other sequences, such as “on my back ”or
“in the pack ”). If “back”or“pack”are heard often, then their
partial commonality with “backpack ”would result in “backpack ”
being segmented into “back”and “pack”(with a directed link
between them; i.e., back →pack). The fixation of long sequences
may have a positive or a negative impact on a learner ’s success, as
discussed below and in refs. 81 and 87. Note that the fixation of
“backpack ”does not prevent the formation of separate nodes for
“back”and“pack”following later observations.
In addition to breaking up segments to form smaller segments,
a node can be formed by the concatenation of smaller segments
after they are repeatedly observed in succession. Thus, nodes can
be formed “top-down ”directly from the raw input by segmen-
tation, or “bottom-up ”through concatenation of previously
learned units, creating a hierarchical structure, with potentially
multiple hierarchies that can be perceived as “sequences of
shorter sequences ”(see refs. 85 and 86 for more details). In both
cases, the effects that memory parameters have on learning
amount to a test of statistical significance: natural and mean-
ingful patterns are likely to recur and thus pass the test, whereas
spurious patterns decay and are forgotten.
A Simplified Example. To better understand the process of data
segmentation and network construction, a simplified example is
illustrated in Fig. 1 A. This example shows a network that is
constructed as the result of acquiring three specific strings of
data, under the assumption that the weight-increase parameter is
0.4, the fixation threshold is 1.0 (which means that a data item
reaches fixation after three successive observations, because 3 ×
0.4>1), and the weight-decrease (decay) parameter is 0.01 (i.e.,
the weight of a data item that is not yet fixated decreases
by 0.01 with each symbol that enters the input). It is also as-
sumed that the working-memory buffer can accommodate up to
24 symbols (which is the length of one data string in the example
in Fig. 1 A), and that the strings in this illustration are separated
by 30 additional (irrelevant) characters that prevent parts of any
two of these strings from being processed simultaneously in the
memory buffer. The figure demonstrates that repeated se-
quences within each string (highlighted by shades of gray for
clarity) are segmented based on their similarity and become the
data units that form the nodes of the network. Directed links
represent past association between these units; thus, they rep-
resent statistical regularities of the environment. For example,
98 always follows 756 and precedes 136, whereas 756 leads to
48361, 98, and 28 with equal probability. Despite the simplicity of
this network, we can already observe that 98 and 28 have a
similar link structure: both are preceded by 756 and followed by
136. In our earlier work (85, 86), we showed how such similarity
in link structure can be used for generalization, for the con-
struction of hierarchical representations, and for creativity (87).
We stressed earlier that, according to our model, the co-
ordinated action of learning and data acquisition mechanisms
and their evolution in response to typical input characteristics
are critical for building an effective network. This point is illus-
trated by Fig. 1 B, where the same data as in Fig. 1 Aare now
distributed differently, leading to a radically different network
representation (although the learning parameters and the
working-memory buffer size remain the same). The distribution
of the data input in Fig. 1 Bleads to the fixation of large idio-
syncratic data sequences and to poor link structure, which may
hamper further learning and generalization (81, 87). For exam-
ple, no generalization can now be drawn for 98 and 28 because
each of them is “locked ”within another segment. Recognizing75648361 756981367562813675648361 7569813675628136
75648361 7569813675628136
756          48361
98        136
28A
75648361          6 7562813675648361 75648361 75648361
756981367569813675698136
756281367562813675628136B
75698136
Fig. 1. Data input in the form of three strings, and the network that is
constructed as a result of acquiring and processing this input using the
learning mechanisms and parameter set described in the text. ( A) Each data
string of 24 characters is composed of three nonidentical subsequences of
eight characters that share some common segments (highlighted using the
same shade of gray). The three strings are identical in this case, so labeling
each subsequence of eight characters as A, B, and C, respectively, allows
describing the structure of the input as ABC ABC ABC. ( B) The same input as
inAis distributed differently over time, which can be described in short as
AAA BBB CCC. This input leads to a completely different network structure
due to fixation of A, B, and C as long eight-character chunks. The weights of
the nodes and the links of the networks are not shown in the figure, but all
of them exceed the fixation threshold of 1.0, as the weight-increase pa-
rameter was set to 0.4 per occurrence.
7918 |www.pnas.org/cgi/doi/10.1073/pnas.1620742114 Lotem et al.
segments in novel input becomes more difficult (i.e., is less
likely) if the memory representation is based on large idiosyn-
cratic units. The learner is then less likely to place novel data in
context and to perform further segmentation.
Note that if we change only the weight-increase parameter
from 0.4 to 0.3, then the data input of Fig. 1 Bwould result in
exactly the same network as in Fig. 1 Aand all of the problems
that we have just described would disappear. This is because the
segment “75648361 ”would not reach fixation after the first data
string and would not decay completely before the second string is
acquired, so it would be segmented when 756 is encountered in
the second string and again in the third. A similar result can be
achieved if we extend the working-memory buffer to include the
beginning of the next string (so that the fourth occurrence of
“756”can split the “75648361 ”and so on). This example shows
how different combinations of data distribution and memory
parameters can generate quite different or quite similar net-
works. It also demonstrates how relatively small modifications to
the learning parameters or to the distribution of data input can
lead to major changes in the network. As we discussed elsewhere
(82, 86), it is important to bear in mind that not only may the
learning parameters vary across individuals or species, but they
may also evolve to differ across different sensory modalities (or
different learning mechanisms) to better respond to the different
distributions of data types in nature. Similarly, the learning pa-
rameters may also be modulated by physiological and emotional
state, giving higher increase in memory weight to important but
relatively rare observations (82).
The learning mechanism described so far is sensitive to the
order in which elements appear in the data. For example,
756 and 576 are viewed as different data sequences. This may be
important for some data types, such as the sequence of actions of
a particular hunting technique, the phrases in a birdsong, or
human speech. But for some other types of data it may be suf-
ficient (or even better) to classify two sequences as the same by
merely recognizing some of their similar components. For exam-
ple, two instances of the same salad in a salad bar or a stand of
mango trees in the forest may be recognized based on a combi-
nation of stimuli, ignoring their exact serial order (which may
actually vary across instances). It is therefore possible that the
learning mechanisms may also differ in the set of parameters that
determine how sensitive they are to the exact serial order of data
items. Clearly, a change in these parameters can also influence the
segmentation process and the structure of the network, an issue
that will become relevant again when we discuss language and
tool-making, for which serial order is critically important.
Finally, as explained earlier, our model does not pretend to
capture cognitive mechanisms at the neuronal level. The nodes
and the links in our network do not correspond to neurons and
synapses. Nevertheless, the processes described in our model at
the computational level can be realized by neuronal structures
and activities, and a representation of the proposed network may
exist in the brain. We can assume that the neuronal structures
and brain circuits that realize the network are ultimately affected
by constraints of size and morphology that are at least partly
determined genetically. That is, adaptive changes in the data
acquisition and the learning mechanisms that can potentially
lead to the construction of an extensive network in the acoustic
domain, for example, may be subject to physical constraints that
are also genetically determined. Over generations, genetic vari-
ants that are better in relaxing these physical constraints and in
meeting the demand for larger or more appropriate neuronal
structures will be favored by selection. This view of brain evo-
lution is consistent with the “Baldwin effect ”view (92, 93),
according to which genes may be selected based on how well they
support adaptive plastic processes, such as learning. Using this
approach to address the question of how culture shapes the
evolution of the brain would imply that culture exerts selective
pressure that shapes learning and data-acquisition parameters,
which in turn shape the structure of the constructed network.
Consequently, over evolutionary time scales, brain anatomy maybe selected to better accommodate the physical requirements of
the constructed network. In the next two sections we consider
how this may have happened in the case of human language and
stone-tool production, reexamining in this light the two problems
discussed earlier regarding memory constraints and social-learning
mechanisms.
The Case of Human Language and Memory Constraints
Although the question of how language has evolved is in itself
the focus of extensive research (e.g., refs. 94 –96), here we focus
on a more specific question: Given that language has evolved,
how can it shape the evolution of cognition? According to the
process-level approach described above, we should address this
question in terms of how the need to learn a language, or to use
it, selects for possible changes in data-acquisition or learning
mechanisms. The first expected change, which is quite obvious, is
in the data-acquisition mechanisms: we would expect that at-
tention to human speech, as well as to human gaze and gestures
that can help to learn the meaning of spoken words, would be-
come even more important than before. Indeed, these manifes-
tations of social attention are very typical of human infants and
young children (97, 98); impairments in such social attention
skills are known to lead to problems in language learning, as in
the case of autism (e.g., ref. 99). Perhaps the most significant
expected impact of changes to the data distribution is on the
segmentation process, and consequently on the construction of
the network: because we expect the data-acquisition and learning
parameters to coevolve, the evolution of language should also
affect the memory parameters of the learning mechanisms. Note
that this consideration takes us back to the problem of language
and memory constraints discussed earlier in the paper. It is
highly unlikely that the memory and learning parameters that
evolved before language existed were best suited for processing
linguistic data. Although certain plastic adjustment of these
memory parameters on the basis of the learner ’s individual ex-
perience cannot be ruled out, it is unlikely that adaptation to
language learning and use over hundreds of generations did not
also play a role in shaping the genetic basis of these parameters ’
values. This claim is supported by the known genetic heritability
component in various types of memory (60 –63). The question to
address next is how these parameters evolved as a result of
language evolution.
Intuitively, one would expect the challenge of language ac-
quisition to require and to select for a better working memory,
leading to a view of a memory limit as a constraint rather than as
an adaptation (see problem 2, above). However, according to our
model, there are at least two reasons why a limited working
memory may actually be adaptive. First, as explained earlier, the
parameters of weight increase and decrease create a window for
learning that serves as a test of statistical significance: natural
and meaningful patterns are likely to recur and thus to pass the
test, whereas spurious patterns decay and are forgotten.
According to this view, the reason that it is typically difficult to
learn a novel input from a single encounter is that the mecha-
nism of learning has evolved to expect more evidence before
deciding whether an item should be learned or ignored. The
evolution of learning parameters that allow data items to reach
fixation in memory after a single encounter should be possible.
There are in fact examples for such “one-trial learning, ”that,
interestingly, occurs when rapid learning seems to be adaptive, as
in the context of fear learning and enemy recognition (100, 101)
or in the case of word “fast-mapping ”in young children (102).
However, in the case of large quantities of sequential data, where
all items may be equally important, proximity of repeated occur-
rences in time and frequency of recurrence are the best first-resort
tests of meaningfulness. The selective pressure of language in the
direction of smaller buffer sizes and moderate fixation rates may
explain why people are not better at memorizing sequential data
verbatim (58), which would require larger buffer sizes and more
rapid fixation.
Lotem et al. PNAS |July 25, 2017 |vol. 114 |no. 30 |7919
EVOLUTION PSYCHOLOGICAL AND
COGNITIVE SCIENCESCOLLOQUIUM
PAPER
Second, the limited memory buffer can be viewed as repre-
senting an adaptive trade-off between memory and computation.
[Recall our earlier discussion of Christiansen and Chater ’s
memory bottleneck (58).] We assume that a larger buffer that
can accommodate more data can evolve, but whether or not it
will be adaptive depends on the kind of computations needed to
process the data in the buffer. In the case of linguistic input,
serial order is important; the data must be segmented into words
or chunks based on recurring segments. This means that the
learner has to search and compare all possible chunks within the
buffer, and to find possible matches between these chunks and
those represented already as nodes in the network (so as to put
the incoming data in context). The number of possible chunks in
a linear sequence grows as the square of the length of the se-
quence [there are N(N−1)/2 possible chunks in a linear se-
quence of Ndata items]. Thus, in a relatively small buffer of
5 data items, the learner has to find and compare only 10 possi-
ble chunks, whereas in larger buffers of, say 10 or 15 items, the
learner has to find and compare 45 or 105 possible chunks, re-
spectively. Therefore, increasing the buffer size leads to consid-
erable computational cost that may not be justifiable. Depending
on the number of items that comprise a typical meaningful chunk
in the language, it might be better to process a sequence of data
items by gradually scanning it with a small buffer of 5 items
rather than with a large buffer of 15 items.
The computational burden is likely to be much smaller if data
input does not need to be segmented accurately, but merely
recognized and classified based on some characteristic features.
As we mentioned earlier, recognizing a particular salad in a salad
bar or a typical fruit tree in a forest may not require paying
attention to the exact serial order of the data. In this case, a
larger memory buffer may not lead to such a sharp increase in
computation. A simple illustration of this phenomenon is given
by the three nonsegmented sentences presented in Fig. 2. En-
glish speakers who cannot read Hebrew or Japanese would most
certainly try to segment (almost automatically and subcon-
sciously) the first sentence that is in English but not the next two
sentences that are in Hebrew and Japanese. Those would be
classified quickly as “Gibberish in a foreign language ”or, more
specifically, as “two sentences in Hebrew and Japanese that I
can’t read ”(based on some distinctive features of Hebrew and
Japanese letters). Clearly, readers who know Hebrew or Japa-
nese will segment those sentences automatically. The point of
this example is to demonstrate that the “ecological ”context and
the cultural background of a learner can determine the level of
computation applied to processing incoming data.
Some level of data segmentation was clearly required even
before the evolution of language; for example, when animals
forage for food in structured environments (85) or need to learn
or interpret observed behavioral sequences (103). However, the
evolution of language almost certainly increased the proportion
of data input that must be accurately segmented, thereby im-
posing more significant computational requirements for a given
memory buffer size. Individuals with a genetic predisposition to
use a smaller buffer may have been selected, which may explain
why the memory bottleneck is indeed so small. This hypothesis
predicts that some of the humans ’close relatives that do not
possess language may be endowed with a larger working-memory
buffer. Indeed, a notable study on working memory for numerals
in chimpanzees (104) shows that young chimpanzees have a
better capacity for numerical recollection than human adults.This ability may be useful for fast recognition of objects or
structures in the field that does not depend on serial order. It
may also improve rote learning of recent actions that might be
helpful in systematically searching for food without returning to
a place that was just visited. Interestingly, exceptional ability to
retain in memory an accurate, detailed image of a complex scene
or pattern (also known as “eidetic imagery ”) is more common
among young children (105) and autistic savants (106). Such an
ability may facilitate rote learning at the expense of effective
segmentation and network representation (81). Regardless of
whether or not we understand this phenomenon correctly, it
clearly shows that having a larger working-memory buffer is bi-
ologically feasible and that genetic variants that possess this
ability are already present in human populations. The fact that
they do not spread and become the norm suggests that a small
memory bottleneck is somehow more adaptive.
The Case of Tool-Making and the Evolution of Social-
Learning Mechanisms
Cultural transmission of tool-making techniques depends on
social-learning mechanisms. Whereas learning some advanced
techniques may involve teaching and verbal instruction (107), the
ability to make stone tools probably depended, initially at least,
on social-learning mechanisms of the type needed to facilitate
imitation or emulation (108 –110). How, then, could the evolu-
tion of culturally transmitted techniques for making tools (i.e.,
the culture of “tool-making ”) affect the genetic evolution of such
learning mechanisms? This question is a specific instance of the
more general questions addressed earlier ( Problem 1 , above) of
how using learning mechanisms for social functions shapes their
evolution. To answer this question, we should first consider how
imitation or emulation works. Here we try to explain it in terms
of our model. We assume that for imitation, the coupling be-
tween perception and action is developed through experience
(see also ref. 2). That is, when an individual repeatedly observes
its own actions, it gradually —and quite automatically —asso-
ciates the perception and the motor experience of those actions.
Eventually, seeing another individual performing those actions
activates the observer ’s representation of the motor experience
of performing those actions [because the observed actions are
perceived as being similar to the (perceptual representation of)
the individual ’s own actions, which are already coupled with the
relevant motor experience]. Thus, the first expected effect, which
may be described in terms of data-acquisition mechanisms (33,
82), is an increase in attention to the behavioral patterns of other
individuals (in imitation) or to the outcomes of their actions (in
emulation). For imitation, it is also important to acquire much
information on self-actions to create the coupling between the
perception of these actions and their motor experience.
The next question is how to organize the acquired data in
memory. In our framework, this amounts to asking how the
network should be constructed. In the case of imitation, there
seem to be two possibilities. One is to represent long sequences
of observed actions or sensory experiences as large chunks: exact
copies of entire sequences that can then be executed. The other
possibility is to segment the observed behavior or sensory expe-
rience into smaller basic units, just as in the process of language
acquisition, and then compose them again into larger sequences
in the production process. The first possibility of exact imitation
fits the notion of specialized imitation ability that allows copying
and executing complex behaviors accurately and almost auto-
matically. However, this approach leads to three problems. First,
the expected effect of exact imitation on the evolution of
learning is that both the “working-memory buffer ”and the
weight-increase parameter should increase in size. The working-
memory buffer should be large enough to capture the long
sequences of observed behaviors, and the weight-increase pa-
rameter should be high enough to allow rapid fixation in mem-
ory. This prediction does not seem to hold. As discussed earlier,
the human working-memory buffer is typically small, and com-
plex patterns require repeated encounters to be learned. TheFig. 2. The sentence: “The number of possible chunks in a linear sequence =
N(N−1)/2”written in a nonsegmented form in three different languages,
English, Hebrew, and Japanese. See the explanation in the main text.
7920 |www.pnas.org/cgi/doi/10.1073/pnas.1620742114 Lotem et al.
second problem with exact imitation of novel sequences is
whether or not it can work within the framework of the asso-
ciative account. To create the coupling between perception and
action, the learner must also produce the action, and this cannot
be done before successful imitation (because the perception of a
unique novel sequence does not yet have a match in motor
representation that can be executed). Finally, the learning of
long fixed-action sequences reduces flexibility. It would require,
for example, that the initial raw stone from which a tool is to be
produced be nearly identical to the raw stone that was used in
the original learned sequence, a highly unlikely occurrence.
The alternative possibility, which involves sequence segmen-
tation and reassembly, is consistent with the associative account
and is also feasible. The learner first explores and practices a
large repertoire of simple behavioral actions, thereby creating
the necessary coupling between perception and action in a large
repertoire of basic behavioral units. It can then concatenate
these basic units in many possible ways; for the purpose of imi-
tation, it can concatenate them to gradually match the complex
behavior demonstrated by other individuals. The demonstrated
behavior is also segmented into familiar units, which can then be
associated with familiar actions, which helps in producing an
imitation. This scenario is quite consistent with mounting evi-
dence and recent views of experience-based imitation and em-
ulation (110, 111). It also suggests that similar processes are
involved both in language learning and in complex imitation,
which is in line with recent views according to which tool-making
possibly preadapted the brain to language learning (27).
Finally, our process-level approach may also help to explain
recent new studies linking neuroanatomical changes in the brain
to Paleolithic tool-making ability. These studies found that
the acquisition of tool-making abilities by experimental subjects
involved specific structural changes in the brain (27) and that
these structures and regions in the brain are more developed in
humans than in chimpanzees (28). This evidence for a short-term
plastic response colocalized with structures that underwent re-
cent evolutionary change strongly suggests a process akin to the
Baldwin effect, in which genetic variants are selected based on
how well they support the required plastic changes (92, 93). It is
yet to be explained, however, how the observed plastic changes
improve tool-making abilities. As we suggested earlier, in our
view, such neuroanatomical changes have to do with the path-
ways and the representational systems that are recruited to serve
the construction of the network. The result should be a rich
network that represents sensory and perceptual experiences ofvarious hand movements, stone tools in various stages of com-
pletion, and the association of all these images and segments in
time and space. According to our model, having a rich, well-
segmented, and well-connected network helps to put new ob-
servations in context and to produce effective actions (81, 82,
87). Moreover, the ability to create a well-segmented and well-
connected network likely depends on appropriate settings of the
parameters of the data acquisition and the learning mechanisms
that govern the dynamic process of network construction. Thus,
the fine-tuning of these mechanisms by natural selection to
produce the most effective network for the purpose of learning
to make tools would be precisely the manner in which the culture
of tool-making shapes the evolution of cognition.
Conclusions
In this paper we embraced the view that cognitive mechanisms
have evolved to accommodate —among other tasks —the rela-
tively new challenges of learning cultural constructs, such as
language and tool-making techniques or, simply put, that culture
shaped cognition. We claim, however, that to study how such
cultural constructs shape cognitive evolution, a computationally
explicit process-level mechanistic model of learning may be re-
quired. We described such a model, one that is based on coevolving
mechanisms of learning and data acquisition that jointly construct a
complex network, capable of supporting a range of cognitive abil-
ities. The effect of culture on cognitive evolution is captured
through small modifications of these coevolving learning and data-
acquisition mechanisms, whose coordinated action improves the
network ’s ability to support the learning processes that are involved
in cultural phenomena, such as language or tool-making. Finally, we
proposed that culture exerts selective pressure that shapes learning
and data acquisition parameters, which in turn shape the structure
of the representation network, so that over evolutionary time scales,
brain anatomy may be selected to better accommodate the physical
requirements of the learned processes and representations.
ACKNOWLEDGMENTS. We thank the organizers and funders of the Arthur
M. Sackler Colloquium on “The Extension of Biology Through Culture. ”We
also thank two anonymous reviewers for highly constructive comments.
Funding was provided by the Israel Science Foundation Grant 871/15 (to
A.L.) and by NSF Grant CCF-1214844, Air Force Office of Scientific Research
Grant FA9550-12-1-0040, and Army Research Office Grant W911NF-14-1-0017
(to J.Y.H.). O.K. was supported by the John Templeton Foundation Grant ID
47981 and by the Stanford Center for Computational, Evolutionary, and
Human Genomics.
1. Heyes C, Pearce JM (2015) Not-so-social learning strategies. Proc Biol Sci 282:20141709.
2. Cook R, Bird G, Catmur C, Press C, Heyes C (2014) Mirror neurons: From origin to
function. Behav Brain Sci 37:177 –192.
3. Leadbeater E (2015) What evolves in the evolution of social learning? J Zool (Lond) 295:
4–11.
4. Iacoboni M, et al. (1999) Cortical mechanisms of human imitation. Science 286:
2526 –2528.
5. Rizzolatti G, Fogassi L, Gallese V (2001) Neurophysiological mechanisms underlying
the understanding and imitation of action. Nat Rev Neurosci 2:661 –670.
6. Leslie AM (1987) Pretense and representation: The origins of “theory of mind. ”
Psychol Rev 94:412 –426.
7. Baron-Cohen S (2000) Theory of mind a nd autism: A fifteen year review. Understanding
Other Minds , eds Baron-Cohen S, Tagar-Flusberg H, Cohen DJ (Oxford Univ Press,
Oxford), Vol A, pp 3 –20.
8. Cosmides L, Tooby J, Fiddick L, Bryant GA (2005) Detecting cheaters. Trends Cogn Sci
9:505 –506, author reply 508 –510.
9. Chomsky N (1965) Aspects of the Theory of Syntax (MIT Press, Cambridge, MA).
10. Tooby J, Cosmides L, Barrett HC (2005) Resolving the debate on innate ideas. The Innate
Mind: Structure and Content , eds Carruthers P, Laurence S, Stich S (Oxford Univ Press,
New York), pp 305 –337.
11. Anderson ML (2010) Neural reuse: A fundamental organizational principle of the
brain. Behav Brain Sci 33:245 –266, discussion 266 –313.
12. Bates E (1993) Modularity, domain specificity and the development of language.
Discuss Neurosci 10:136 –148.
13. Scott-Phillips TC, Laland KN, Shuker DM, Dickins TE, West SA (2014) The niche con-
struction perspective: A critical appraisal. Evolution 68:1231 –1243.
14. Laland KN, Odling-Smee FJ, Feldman MW (1999) Evolutionary consequences of
niche construction and their implications for ecology. Proc Natl Acad Sci USA 96:
10242 –10247.15. Odling-Smee FJ, Laland KN, Feldman MW (2003) Niche Construction: The Neglected
Process in Evolution (Princeton Univ Press, Princeton, NJ).
16. Iriki A, Taoka M (2012) Triadic (ecological, neural, cognitive) niche construction: A
scenario of human brain evolution extrapolating tool use and language from the
control of reaching actions. Philos Trans R Soc Lond B Biol Sci 367:10 –23.
17. Laland KN, Hoppitt W (2003) Do animals have culture? Evol Anthropol 12:
150–159.
18. Laland KN, Janik VM (2006) The animal cultures debate. Trends Ecol Evol 21:542 –547.
19. Stout D, Hecht EE (2017) Evolutionary neuroscience of cumulative culture. Proc Natl
Acad Sci USA 114:7861 –7868.
20. Solan Z (2005) Unsupervised learning of natural languages. Proc Natl Acad Sci USA
102:11629 –11634.
21. Laland KN, Odling-Smee J, Myles S (2010) How culture shaped the human genome:
Bringing genetics and the human sciences together. Nat Rev Genet 11:137 –148.
22. Williamson SH, et al. (2007) Localizing recent adaptive evolution in the human ge-
nome. PLoS Genet 3:e90.
23. de Magalhães JP, Matsuda A (2012) Genome-wide patterns of genetic distances
reveal candidate loci contributing to human population-specific traits. Ann Hum
Genet 76:142 –158.
24. Somel M, Liu X, Khaitovich P (2013) Human brain evolution: Transcripts, metabolites
and their regulators. Nat Rev Neurosci 14:112 –127.
25. Cáceres M, et al. (2003) Elevated gene expression levels distinguish human from non-
human primate brains. Proc Natl Acad Sci USA 100:13030 –13035.
26. Somel M, Rohlfs R, Liu X (2014) Transcriptomic insights into human brain evolution:
Acceleration, neutrality, heterochrony. Curr Opin Genet Dev 29:110 –119.
27. Hecht EE, et al. (2015) Acquisition of Paleolithic toolmaking abilities involves
structural remodeling to inferior frontoparietal regions. Brain Struct Funct 220:
2315 –2331.
Lotem et al. PNAS |July 25, 2017 |vol. 114 |no. 30 |7921
EVOLUTION PSYCHOLOGICAL AND
COGNITIVE SCIENCESCOLLOQUIUM
PAPER
28. Hecht EE, Gutman DA, Bradley BA, Preuss TM, Stout D (2015) Virtual dissection and
comparative connectivity of the superior longitudinal fasciculus in chimpanzees and
humans. Neuroimage 108:124 –137.
29. Creanza N, Fogarty L, Feldman MW (2016) Cultural niche construction of repertoire
size and learning strategies in songbirds. Evol Ecol 30:285 –305.
30. Shettleworth SJ (2010) Cognition, Evolution, and Behavior (Oxford Univ Press, New York).
31. Heyes C (2010) Where do mirror neurons come from? Neurosci Biobehav Rev 34:575 –583.
32. Heyes C (2016) Homo imitans ? Seven reasons why imitation couldn ’t possibly be
associative. Philos Trans R Soc Lond B Biol Sci 371:20150069.
33. Heyes C (2012) What ’s social about social learning? J Comp Psychol 126:193 –202.
34. Laland KN (2004) Social learning strategies. Learn Behav 32:4–14.
35. Catmur C, Press C, Cook R, Bird G, Heyes C (2014) Authors ’response: Mirror neurons:
Tests and testability. Behav Brain Sci 37:221 –241.
36. Felsenstein J (1983) Parsimony in systematics: Biological and statistical issues. Annu
Rev Ecol Syst 14:313 –333.
37. Lotem A (1993) Secondary sexual ornaments as signals: The handicap approach and
three potential problems. Etologia 3:209 –218.
38. Mery F, Belay AT, So AK, Sokolowski MB, Kawecki TJ (2007) Natural polymorphism
affecting learning and memory in Drosophila .Proc Natl Acad Sci USA 104:13051 –13055.
39. Dunlap AS, Stephens DW (2014) Experimental evolution of prepared learning. Proc
Natl Acad Sci USA 111:11750 –11755.
40. Garcia J, Kimeldorf DJ, Koelling RA (1955) Conditioned aversion to saccharin re-
sulting from exposure to gamma radiation. Science 122:157 –158.
41. Finkel D, Pedersen NL, McGue M, McClearn GE (1995) Heritability of cognitive abilities
in adult twins: comparison of Minnesota and Swedish data. Behav Genet 25:421 –431.
42. Plomin R, Spinath FM (2002) Genetics and general cognitive ability (g). Trends Cogn
Sci6:169 –176.
43. Briley DA, Tucker-Drob EM (2013) Explaining the increasing heritability of cognitive
ability across development: A meta-analysis of longitudinal twin and adoption
studies. Psychol Sci 24:1704 –1713.
44. Pearson-Fuhrhop KM, Minton B, Acevedo D, Shahbaba B, Cramer SC (2013) Genetic
variation in the human brain dopamine system influences motor learning and its
modulation by L-Dopa. PLoS One 8:e61197.
45. Mery F (2013) Natural variation in learning and memory. Curr Opin Neurobiol 23:52 –56.
46. Stephens DW, Krebs JR (1986) Foraging Theory (Princeton Univ Press, Princeton, NJ).
47. Leadbeater E, Dawson EH (2017) A social insect perspective on the evolution of social
learning mechanisms. Proc Natl Acad Sci USA 114:7838 –7845.
48. Lotem A, Kolodny O (2014) Reconciling genetic evolution and the associative
learning account of mirror neurons through data-acquisition mechanisms. Behav
Brain Sci 37:210 –211.
49. Thompson B, Kirby S, Smith K (2016) Culture shapes the evolution of cognition. Proc
Natl Acad Sci USA 113:4530 –4535.
50. Christiansen MH, Chater N (2008) Language as shaped by the brain. Behav Brain Sci
31:489 –508, discussion 509 –558.
51. Barbujani G, Sokal RR (1990) Zones of sharp genetic change in Europe are also lin-
guistic boundaries. Proc Natl Acad Sci USA 87:1816 –1819.
52. Laland KN (2017) The origins of language in teaching. Psychon Bull Rev 24:225 –231.
53. Belfer ‐Cohen A, Goren ‐Inbar N (1994) Cognition and communication in the Le-
vantine Lower Palaeolithic. World Archaeol 26:144 –157.
54. d ’Errico F, et al. (2003) Archaeological evi dence for the emergence of language, sym-
bolism, and music —An alternative multidisciplinary perspective. J World Prehist 17:1–70.
55. Mellars P (2006) Why did modern human populations disperse from Africa ca.
60,000 years ago? A new model. Proc Natl Acad Sci USA 103:9381 –9386.
56. Chater N, Reali F, Christiansen MH (2009) Restrictions on biological adaptation in
language evolution. Proc Natl Acad Sci USA 106:1015 –1020.
57. Chater N, Christiansen MH (2010) Language acquisition meets language evolution.
Cogn Sci 34:1131 –1157.
58. Christiansen MH, Chater N (2016) The Now-or-Never bottleneck: A fundamental
constraint on language. Behav Brain Sci 39:e62.
59. Lotem A, Kolodny O, Halpern JY, Onnis L, Edelman S (2016) The bottleneck may be
the solution, not the problem. Behav Brain Sci 39:e83.
60. Mueller ST, Krawitz A (2009) Reconsidering the two-second decay hypothesis in
verbal working memory. J Math Psychol 53:14 –25.
61. Cui J, Gao D, Chen Y, Zou X, Wang Y (2010) Working memory in early-school-age
children with Asperger ’s syndrome. J Autism Dev Disord 40:958 –967.
62. Blokland GAM, et al. (2011) Heritability of working memory brain activation.
J Neurosci 31:10882 –10890.
63. Vogler C, et al. (2014) Substantial SNP-based heritability estimates for working
memory performance. Transl Psychiatry 4:e438.
64. Chater N, Christiansen MH (201 6) Squeezing through the Now -or-Never bottleneck: Re-
connecting language processing, acquisition, change, and structure. Behav Brain Sci 39:e91.
65. McNamara JM, Houston AI (2009) Integrating function and mechanism. Trends Ecol
Evol 24:670 –675.
66. Fawcett TW, Hamblin S, Giraldeau LA (2013) Exposing the behavioral gambit: The
evolution of learning and decision rules. Behav Ecol 24:2–11.
67. Kacelnik A, Bateson M (1997) Risk-sensitivity: Crossroads for theories of decision-
making. Trends Cogn Sci 1:304 –309.
68. van den Berg P, Weissing FJ (2015) The importance of mechanisms for the evolution
of cooperation. Proc Biol Sci 282:20151382.
69. Trimmer PC, McNamara JM, Houston AI, Marshall JA (2012) Does natural selection
favour the Rescorla-Wagner rule? J Theor Biol 302:39 –52.
70. Mcnamara JM, Trimmer PC, Houston AI (2012) The ecological rationality of state-
dependent valuation. Psychol Rev 119:114 –119.
71. Arbilly M, Motro U, Feldman MW, Lotem A (2010) Co-evolution of learning com-
plexity and social foraging strategies. J Theor Biol 267:573 –581.72. Lange A, Dukas R (2009) Bayesian approximations and extensions: Optimal decisions
for small brains and possibly big ones too. J Theor Biol 259:503 –516.
73. Katsnelson E, Motro U, Feldman MW, Lotem A (2011) Evolution of learned strategy
choice in a frequency-dependent game. Proc Biol Sci 279:1176 –1184.
74. Hamblin S, Giraldeau L-A (2009) Finding the evolutionarily stable learning rule for
frequency-dependent foraging. Anim Behav 78:1343 –1350.
75. Nilsson DE, Pelger S (1994) A pessimistic estimate of the time required for an eye to
evolve. Proc Biol Sci 256:53 –58.
76. Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw
61:85 –117.
77. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436 –444.
78. Edelman S (2016) The minority report: Some common assumptions to reconsider in
the modelling of the brain and behaviour. J Exp Theor Artif Intell 28:751 –776.
79. Mnih V, et al. (2015) Human-level control through deep reinforcement learning.
Nature 518:529 –533.
80. Gu S, Lillicrap T, Sutskever I, Levine S (2016) Continuous deep Q-learning with model-
based acceleration. Proceedings of The 33rd International Conference on Machine
Learning , eds Balcan MF, Weinberger KQ (Proceedings of Machine Learning Re-
search, New York), Vol 48, pp 2829 –2838.
81. Lotem A, Halpern JY (2008) A data-acquisition model for learning and cognitive
development and its implications for autism. Cornell University Computing and In-
formation Science Technical Reports . Available at https://ecommons.cornell.edu/
handle/1813/10178. Accessed May 11, 2017.
82. Lotem A, Halpern JY (2012) Coevolution of learning and data-acquisition mecha-
nisms: A model for cognitive evolution. Philos Trans R Soc Lond B Biol Sci 367:
2686 –2694.
83. Goldstein MH, et al. (2010) General cognitive principles for learning structure in time
and space. Trends Cogn Sci 14:249 –258.
84. Kolodny O, Edelman S, Lotem A (2014) The evolution of continuous learning of the
structure of the environment. J R Soc Interface 11:20131091.
85. Kolodny O, Edelman S, Lotem A (2015) Evolution of protolinguistic abilities as a by-
product of learning to forage in structured environments. Proc Biol Sci 282:
20150353.
86. Kolodny O, Lotem A, Edelman S (2015) Learning a generative probabilistic grammar
of experience: A process-level model of language acquisition. Cogn Sci 39:227 –267.
87. Kolodny O, Edelman S, Lotem A (2015) Evolved to adapt: A computational approach
to animal innovation and creativity. Curr Zool 61:350 –367.
88. Enquist M, Lind J, Ghirlanda S (2016) The power of associative learning and the
ontogeny of optimal behaviour. R Soc Open Sci 3:160734.
89. Laland KN, et al. (2015) The extended evolutionary synthesis: Its structure, assump-
tions and predictions. Proc Biol Sci 282:20151019.
90. Baddeley A, Gathercole S, Papagno C (1998) The phonological loop as a language
learning device. Psychol Rev 105:158 –173.
91. Burgess N, Hitch GJ (1999) Memory for serial order: A network model of the pho-
nological loop and its timing. Psychol Rev 106:551 –581.
92. Baldwin JM (1896) A new factor in evolution. Am Nat 30:441 –451.
93. Weber BH, Depew DJ (2003) Evolution and Learning: The Baldwin Effect Reconsidered
(MIT Press, Cambridge, MA).
94. Dunbar R (1998) Grooming, Gossip, and the Evolution of Language (Harvard Univ
Press, Cambridge, MA).
95. Pinker S (2003) Language as an adaptation to the cognitive niche. Stud Evol Lang 3:
16–37.
96. Premack D (1985) “Gavagai! ”or the future history of the animal language contro-
versy. Cognition 19:207 –296.
97. Butterworth G, Jarrett N (1991) What minds have in common is space: Spatial
mechanisms serving joint visual attention in infancy. Br J Dev Psychol 9:55–72.
98. Scaife M, Bruner JS (1975) The capacity for joint visual attention in the infant. Nature
253:265 –266.
99. Klin A, Lin DJ, Gorrindo P, Ramsay G, Jones W (2009) Two-year-olds with autism
orient to non-social contingencies rather than biological motion. Nature 459:
257–261.
100. Curio E, Ernst U, Vieth W (1978) Cultural transmission of enemy recognition: One
function of mobbing. Science 202:899 –901.
101. Dunsmoor JE, Murty VP, Davachi L, Phelps EA (2015) Emotional learning selectively
and retroactively strengthens memories for related events. Nature 520:345 –348.
102. Medina TN, Snedeker J, Trueswell JC, Gleitman LR (2011) How words can and cannot
be learned by observation. Proc Natl Acad Sci USA 108:9014 –9019.
103. Byrne RW (1999) Imitation without intentionality. Using string parsing to copy the
organization of behaviour. Anim Cogn 2:63–72.
104. Inoue S, Matsuzawa T (2007) Working memory of numerals in chimpanzees. Curr
Biol 17:R1004 –R1005.
105. Conway ARA, Jarrold C, Kane MJ, Miyake A, Towse JN (2007) Variation in working
memory: An introduction. Variation in Working Memory , eds Conway ARA, Jarrold C,
Kane MJ, Miyake A, Towse JN (Oxford Univ Press, Oxford), pp 3 –17.
106. Snyder AW, Mitchell DJ (1999) Is integer arithmetic fundamental to mental pro-
cessing?: The mind ’s secret arithmetic. Proc Biol Sci 266:587 –592.
107. Morgan TJH, et al. (2015) Experimental evidence for the co-evolution of hominin
tool-making teaching and language. Nat Commun 6:6029.
108. Whiten A (2000) Primate culture and social learning. Cogn Sci 24:477 –508.
109. Heyes CM, Galef BG, Jr (1996) Social Learning in Animals: The Roots of Culture
(Academic, San Diego).
110. Galef BG (2015) Laboratory studies of imitation/field studies of tradition: Towards a
synthesis in animal social learning. Behav Processes 112:114 –119.
111. Truskanov N, Lotem A (2017) Trial-and-error copying of demonstrated actions re-
veals how fledglings learn to ‘imitate ’their mothers. Proc Biol Sci 284:20162744.
7922 |www.pnas.org/cgi/doi/10.1073/pnas.1620742114 Lotem et al.

Arnon Lotem
Joseph Y
Halpern
Shimon Edelman
Oren Kolodny