The evolution of self-control
Evan L. MacLean
, Brian Hare
, Charles L. Nunn
, Elsa Addessi
, Federica Amici
, Rindy C. Anderson
, Joseph M. Baker
, Amanda E. Bania
, Allison M. Barnard
, Neeltje J. Boogert
Elizabeth M. Brannon
, Emily E. Bray
, Joel Bray
, Lauren J. N. Brent
, Judith M. Burkart
, Josep Call
Jessica F. Cantlon
, Lucy G. Cheke
, Nicola S. Clayton
, Mikel M. Delgado
, Louis J. DiVincenti
, Kazuo Fujita
, Chihiro Hiramatsu
, Lucia F. Jacobs
, Kerry E. Jordan
, Jennifer R. Laude
, Kristin L. Leimgruber
Emily J. E. Messer
, Antonio C. de A. Moura
, Ljerka Ostoji
, Alejandra Picard
, Michael L. Platt
Joshua M. Plotnik
, Friederike Range
, Simon M. Reader
, Rachna B. Reddy
, Aaron A. Sandel
, Laurie R. Santos
, Amanda M. Seed
, Kendra B. Sewall
, Rachael C. Shaw
, Katie E. Slocombe
, Yanjie Su
, Jingzhi Tan
, Ruoting Tao
, Carel P. van Schaik
, Zsófia Virányi
, Elisabetta Visalberghi
Jordan C. Wade
, Arii Watanabe
, Jane Widness
, Julie K. Young
, Thomas R. Zentall
, and Yini Zhao
Psychology and Neuroscience, and
Center for Cognitive Neuroscience,
for Brain Sciences, Duke University, Durham, NC 27708;
Istituto di Scienze e Tecnologie della Cognizione Consiglio Nazionale delle Ricerche, 00197 Rome,
Department of Developmental and Comparative Psychology, Max Planck Institute for Evolutionary Anthropology, D-04103 Leipzig, Germany;
Department of Biology, Duke University, Durham, NC 27704;
Instituto de Neuroetologia, Universidad Veracruzana, Xalapa, CP 91190, Mexico;
Centre in Evolutionary Anthropology and Palaeoecology, Liverpool John Moores University, Liverpool L3 3AF, United Kingdom;
Center for Interdisciplinary
Brain Sciences Research and
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305;
Animal Care Sciences, Smithsonian National Zoological Park, Washington, DC 20008;
Department of Brain and Cognitive Science and
Comparative Medicine, Seneca Park Zoo, University of Rochester, Rochester, NY 14620;
Department of Psychology and Neuroscience, University of
St. Andrews, St. Andrews KY16 9JP, Scotland;
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104;
Anthropological Institute and
Museum, University of Zurich, 8057 Zurich, Switzerland;
Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom;
Department of Psychology and
Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720;
Graduate School of Letters, Kyoto
University, Kyoto 606-8501, Japan; Departments of
Wildland Resources, Utah State University, Logan, UT 84322;
Psychology, University of Kentucky, Lexington, KY 40506;
Department of Psychology, Yale University, New Haven, CT 06520;
Departamento Engenharia e
Meio Ambiente, Universidade Federal da Paraiba, 58059-900, João Pessoa, Brazil;
Department of Psychology, University of York, Heslington, York YO10 5DD,
Think Elephants International, Stone Ridge, NY 12484;
Messerli Research Institute, University of Veterinary Medicine Vienna, 1210
Wolf Science Center, A-2115 Ernstbrunn, Austria;
Department of Biology, McGill University, Montreal, QC, Canada H3A 1B1;
of Anthropology, University of Michigan, Ann Arbor, MI 48109; and
Department of Psychology, Peking University, Beijing 100871, China
Edited by Jon H. Kaas, Vanderbilt University, Nashville, TN, and approved March 21, 2014 (received for review December 30, 2013)
Cognition presents evolutionary research with one of its greatest
challenges. Cognitive evolution has been explained at the proxi-
mate level by shifts in absolute and relative brain volume and at
the ultimate level by differences in social and dietary complexity.
However, no study has integrated the experimental and phyloge-
netic approach at the scale required to rigorously test these ex-
planations. Instead, previous research has largely relied on various
measures of brain size as proxies for cognitive abilities. We ex-
perimentally evaluated these major evolutionary explanations by
quantitatively comparing the cognitive performance of 567 indi-
viduals representing 36 species on two problem-solving tasks
measuring self-control. Phylogenetic analysis revealed that abso-
lute brain volume best predicted performance across species and
accounted for considerably more variance than brain volume con-
trolling for body mass. This result corroborates recent advances in
evolutionary neurobiology and illustrates the cognitive consequen-
ces of cortical reorganization through increases in brain volume.
Within primates, dietary breadth but not social group size was a
strong predictor of species differences in self-control. Our results
implicate robust evolutionary relationships between dietary breadth,
absolute brain volume, and self-control. These findings provide a sig-
nificant first step toward quantifying the primate cognitive phenome
and explaining the process of cognitive evolution.
Since Darwin, understanding the evolution of cognition has
been widely regarded as one of the greatest challenges for
evolutionary research (1). Although researchers have identified
surprising cognitive flexibility in a range of species (2–40) and
potentially derived features of human psychology (41–61), we know
much less about the major forces shaping cognitive evolution (62–
71). With the notable exception of Bitterman’s landmark studies
conducted several decades ago (63, 72–74), most research com-
paring cognition across species has been limited to small taxonomic
samples (70, 75). With limited comparable experimental data on
how cognition varies across species, previous research has largely
relied on proxies for cognition (e.g., brain size) or metaanalyses
when testing hypotheses about cognitive evolution (76–92). The
lack of cognitive data collected with similar methods across large
samples of species precludes meaningful species comparisons that
can reveal the major forces shaping cognitive evolution across
species, including humans (48, 70, 89, 93–98).
Although scientists have identified surprising cognitive flexi-
bility in animals and potentially unique features of human
psychology, we know less about the selective forces that favor
cognitive evolution, or the proximate biological mechanisms
underlying this process. We tested 36 species in two problem-
solving tasks measuring self-control and evaluated the leading
hypotheses regarding how and why cognition evolves. Across
species, differences in absolute (not relative) brain volume best
predicted performance on these tasks. Within primates, dietary
breadth also predicted cognitive performance, whereas social
group size did not. These results suggest that increases in ab-
solute brain size provided the biological foundation for evo-
lutionary increases in self-control, and implicate species dif-
ferences in feeding ecology as a potential selective pressure
favoring these skills.
Author contributions: E.L.M., B.H., and C.L.N. designed research; E.L.M., B.H., E.A.,
F. Amici, R.C.A., F. Aureli, J. M. Baker, A.E.B., A.M.B., N.J.B., E.M.B., E.E.B., J.B., L.J.N.B.,
J. M. Burkart, J.C., J.F.C., L.G.C., N.S.C., M.M.D., L.J.D., K.F., E.H., C.H., L.F.J., K.E.J., J.R.L.,
K.L.L., E.J.E.M., A.C.d.A.M., L.O., A.P., M.L.P., J.M.P., F.R., S.M.R., R.B.R., A.A.S., L.R.S., K.S.,
A.M.S., K.B.S., R.C.S., K.E.S., Y.S., A.T., J.T., R.T., C.P.v.S., Z.V., E.V., J.C.W., A.W., J.W., J.K.Y.,
T.R.Z., and Y.Z. performed research; E.L.M. and C.L.N. analyzed data; and E.L.M., B.H., and
C.L.N. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1323533111 PNAS Early Edition
To address these challenges we measured cognitive skills for
self-control in 36 species of mammals and birds (Fig. 1 and
Tables S1–S4) tested using the same experimental procedures, and
evaluated the leading hypotheses for the neuroanatomical under-
pinnings and ecological drivers of variance in animal cognition. At
the proximate level, both absolute (77, 99–107) and relative brain
size (108–112) have been proposed as mechanisms supporting
cognitive evolution. Evolutionary increases in brain size (both ab-
solute and relative) and cortical reorganization are hallmarks of the
human lineage and are believed to index commensurate changes in
cognitive abilities (52, 105, 113–115). Further, given the high
metabolic costs of brain tissue (116–121) and remarkable variance
in brain size across species (108, 122), it is expected that the ener-
getic costs of large brains are offset by the advantages of improved
cognition. The cortical reorganization hypothesis suggests that se-
lection for absolutely larger brains—and concomitant cortical
reorganization—was the predominant mechanism supporting cog-
nitive evolution (77, 91, 100–106, 120). In contrast, the encephali-
zation hypothesis argues that an increase in brain volume relative to
body size was of primary importance (108, 110, 111, 123). Both of
these hypotheses have received support through analyses aggre-
gating data from published studies of primate cognition and
reports of “intelligent”behavior in nature—both of which cor-
relate with measures of brain size (76, 77, 84, 92, 110, 124).
With respect to selective pressures, both social and dietary
complexities have been proposed as ultimate causes of cognitive
evolution. The social intelligence hypothesis proposes that in-
creased social complexity (frequently indexed by social group size)
was the major selective pressure in primate cognitive evolution (6,
44, 48, 50, 87, 115, 120, 125–141). This hypothesis is supported by
studies showing a positive correlation between a species’typical
group size and the neocortex ratio (80, 81, 85–87, 129, 142–145),
cognitive differences between closely related species with different
group sizes (130, 137, 146, 147), and evidence for cognitive con-
vergence between highly social species (26, 31, 148–150). The
foraging hypothesis posits that dietary complexity, indexed by field
reports of dietary breadth and reliance on fruit (a spatiotemporally
distributed resource), was the primary driver of primate cognitive
evolution (151–154). This hypothesis is supported by studies
linking diet quality and brain size in primates (79, 81, 86, 142, 155),
and experimental studies documenting species differences in cog-
nition that relate to feeding ecology (94, 156–166).
Although each of these hypotheses has received empirical sup-
port, a comparison of the relative contributions of the different
proximate and ultimate explanations requires (i) a cognitive dataset
covering a large number of species tested using comparable exper-
imental procedures; (ii) cognitive tasks that allow valid measure-
ment across a range of species with differing morphology, percep-
tion, and temperament; (iii) a representative sample within each
species to obtain accurate estimates of species-typical cognition; (iv)
phylogenetic comparative methods appropriate for testing evolu-
tionary hypotheses; and (v) unprecedented collaboration to collect
these data from populations of animals around the world (70).
Here, we present, to our knowledge, the first large-scale col-
laborative dataset and comparative analysis of this kind, focusing
on the evolution of self-control. We chose to measure self-con-
trol—the ability to inhibit a prepotent but ultimately counter-
productive behavior—because it is a crucial and well-studied
component of executive function and is involved in diverse de-
cision-making processes (167–169). For example, animals re-
quire self-control when avoiding feeding or mating in view of
a higher-ranking individual, sharing food with kin, or searching
for food in a new area rather than a previously rewarding for-
aging site. In humans, self-control has been linked to health,
economic, social, and academic achievement, and is known to be
heritable (170–172). In song sparrows, a study using one of the
tasks reported here found a correlation between self-control and
song repertoire size, a predictor of fitness in this species (173).
In primates, performance on a series of nonsocial self-control
control tasks was related to variability in social systems (174),
illustrating the potential link between these skills and socio-
ecology. Thus, tasks that quantify self-control are ideal for
comparison across taxa given its robust behavioral correlates,
heritable basis, and potential impact on reproductive success.
In this study we tested subjects on two previously implemented
self-control tasks. In the A-not-B task (27 species, n=344),
subjects were first familiarized with finding food in one location
(container A) for three consecutive trials. In the test trial, sub-
jects initially saw the food hidden in the same location (container
A), but then moved to a new location (container B) before they
were allowed to search (Movie S1). In the cylinder task (32
species, n=439), subjects were first familiarized with finding
a piece of food hidden inside an opaque cylinder. In the fol-
lowing 10 test trials, a transparent cylinder was substituted for
the opaque cylinder. To successfully retrieve the food, subjects
needed to inhibit the impulse to reach for the food directly
(bumping into the cylinder) in favor of the detour response they
had used during the familiarization phase (Movie S2).
Thus, the test trials in both tasks required subjects to inhibit
a prepotent motor response (searching in the previously rewarded
location or reaching directly for the visible food), but the nature of
the correct response varied between tasks. Specifically, in the A-
not-B task subjects were required to inhibit the response that was
previously successful (searching in location A) whereas in the
cylinder task subjects were required to perform the same response
as in familiarization trials (detour response), but in the context of
novel task demands (visible food directly in front of the subject).
Fig. 1. A phylogeny of the species included in this study. Branch lengths are
proportional to time except where long branches have been truncated by
parallel diagonal lines (split between mammals and birds ∼292 Mya).
www.pnas.org/cgi/doi/10.1073/pnas.1323533111 MacLean et al.
Across species and accounting for phylogeny, performance on
the two tasks was strongly correlated (r=0.53, n=23, P<0.01).
Thus, species that participated in both cognitive tasks were
assigned a composite score averaging performance across tasks
(Table S5). Because the two tasks assessed complementary but
not identical abilities, the composite score serves as a broader
index of self-control across tasks. Phylogenetic analyses revealed
that scores were more similar among closely related species, with
the maximum likelihood estimate of λ, a measure of phylogenetic
signal, significantly greater than zero in most cases (Table 1). For
both tasks, scores from multiple populations of the same species
(collected by different researchers at different sites) were highly
correlated (cylinder task: r=0.95, n=5, P=0.01; A-not-B task:
r=0.87, n=6, P=0.03; SI Text and Table S6). To control for the
nonindependence of species level data, we used phylogenetic
generalized least squares (PGLS) to test the association between
performance on the cognitive tasks and the explanatory variables
associated with each hypothesis. Our neuroanatomical predictors
included measures of absolute brain volume [endocranial volume
(ECV)], residual brain volume [residuals from a phylogenetic
regression of ECV predicted by body mass (ECV residuals)], and
Jerrison’s (108) encephalization quotient (EQ) (Methods).
Across species, absolute brain volume (measured as ECV) was
a robust predictor of performance (Fig. 2 and Table 2), sup-
porting the predictions of the cortical reorganization hypothesis.
ECV covaried positively with performance on the cylinder task
and the composite score and explained substantial variance in
=0.43–0.60; Table 2). This association was
much weaker for the A-not-B task, reflecting that the largest-
brained species (Asian elephant) had the lowest score on this
measure (Fig. 2 and Table 2). The same analysis excluding the
elephant yielded a strong and significant positive association
between ECV and scores on the A-not-B task (Fig. 2 and Table
2). Across the entire sample, residual brain volume was far less
predictive than absolute brain volume: it explained only 3% of
variance in composite scores, and was a significant predictor of
performance in only one of the tasks (Table 2, SI Text, and Fig.
2). EQ was positively related to composite scores across species
=3.23, P<0.01, λ=0, r
=0.33) but again
explained far less variance than absolute brain volume.
We conducted the same analyses using only primates (23 species,
309 subjects), the best-represented taxonomic group in our dataset.
Within primates, absolute brain volume was the best predictor of
performance across tasks and explained substantial variation across
=0.55–0.68; Fig. 3 and Table 2). In contrast to the
analysis across all species, residual brain volume was predictive of
performance on both tasks within primates, although it explained
much less variance than absolute brain volume (r
3 and Table 2). Within primates the analysis using EQ as a pre-
dictor of composite scores was similar to that using ECV residuals
=1.65, P=0.06, λ=0.66, r
We also restricted the analyses to only the nonprimate species
in our sample (13 species, 258 subjects). Within the nonprimate
species, ECV was again the best predictor of self-control, and
was significantly and positively associated with composite scores
and scores on the cylinder task, but not the A-not-B task (Table 2).
Removing the Asian elephant from the analysis of the A-not-B
task did not change this result (β=0.09, t
=1.37, P=0.11, λ=0,
=0.24). Residual brain volume was not a significant predictor of
any of these measures (Table 2), and EQ was unrelated to com-
posite scores (β=−0.01, t
=−0.08, P=0.53, λ=0.28, r
We used the experimentally derived measures of self-control
to investigate the two leading ecological hypotheses that have
been proposed as catalysts of primate cognitive evolution. We
focused on primates because these species are best represented
in our dataset, and the ecological data have been systematically
compiled and related to neuroanatomical proxies for cognition
in these species. As a measure of social complexity, we tested the
hypothesis that social group size, which covaries with the neo-
cortex ratio in anthropoid primates (129), would predict per-
formance in the self-control tasks. To explore multiple variants
of this hypothesis, we investigated both species-typical pop-
ulation group size and foraging group size as predictor variables.
Neither measure of group size was associated with task perfor-
mance (Fig. 3, Table 2, and Table S7), echoing findings using
observational data on behavioral flexibility (92). We tested the
foraging hypotheses by examining whether the degree of frugi-
vory (percent fruit in diet) or dietary breadth (number of dietary
categories reported to have been consumed by each species) (92)
predicts performance. The percent of fruit in a species’diet was
not a significant predictor of any of the cognitive measures (Fig.
3, Table 2, and Table S7). However, dietary breadth covaried
strongly with our measures of self-control (Fig. 3, Table 2, and
Table S7). Supplemental analyses involving home range size, day
journey length, the defensibility index, and substrate use revealed
no significant associations (SI Text and Fig. S1).
To provide an integrated test of variance explained by absolute
brain volume and dietary breadth, we fit a multiple regression
including both terms as predictors of primates’composite cog-
nitive scores. This model explained 82% of variance in perfor-
mance between species with significant and positive coefficients
for both absolute ECV and dietary breadth, controlling for the
effects of one another (ECV: t
=3.30, P<0.01; dietary
=3.02, P<0.01; λ=0.00, r
=0.82). Thus, while
correlated with one another (t=3.04, P<0.01, λ=0, r
both brain volume and dietary complexity account for unique
components of variance in primate cognition, together explain-
ing the majority of interspecific variation on these tasks. In this
model the independent effect for dietary breadth (r
comparable to that for ECV (r
We also assessed the extent to which our experimental data
corroborate species-specific reports of intelligent behavior in
nature (92). Controlling for observational research effort, our
experimental measures covaried positively with reports of in-
novation, extractive foraging, tool use, social learning, and tac-
tical deception in primates (Table 2, Table S7, and SI Text). Our
experimental measure also covaried with a “general intelligence”
(92), derived from these observational measures (Table
2, Table S7,Fig. S2, and SI Text).
Lastly, we used data from the extant species in our dataset to
reconstruct estimated ancestral states in the primate phylogeny.
Maximum likelihood reconstruction of ancestral states implies
gradual cognitive evolution in the lineage leading to apes, with
a convergence between apes and capuchin monkeys (Fig. 4 and
SI Text). Thus, in addition to statistical inferences about ancestral
species, this model reveals branches in the phylogeny associated
Table 1. Phylogenetic signal in the cognitive data
Data source Dependent measure λ,ML* λ=ML λ=0P
All species Cylinder score 0.83 −2.14 −4.13 0.05
A-not-B score 0.72 −12.60 −14.90 0.03
Composite score 0.76 −2.00 −3.47 0.09
Primates Cylinder score 0.95 −0.62 −3.63 0.01
A-not-B score 0.48 −6.05 −7.54 0.08
Composite score 0.86 −0.98 −3.32 0.03
*The maximum likelihood estimate for λ, a statistical measure of phyloge-
netic signal (201).
Pvalues are based on a likelihood ratio test comparing the model with the
maximum likelihood estimate of λto a model where λis fixed at 0 (the null
alternative representing no phylogenetic signal).
MacLean et al. PNAS Early Edition
with rapid evolutionary change, convergence and divergence, and
the historical contexts in which these events occurred.
Our phylogenetic comparison of three dozen species supports the
hypothesis that the major proximate mechanism underlying the
evolution of self-control is increases in absolute brain volume. Our
findings also implicate dietary breadth as an important ecological
correlate, and potential selective pressure for the evolution of
these skills. In contrast, residual brain volume was only weakly
related, and social group size was unrelated, to variance in self-
control. The weaker relationship with residual brain volume and
lack of relationship with social group size is particularly surprising
given the common use of relative brain volume as a proxy for
cognition and historical emphasis on increases in social group size
as a likely driver of primate cognitive evolution (85).
Why might absolutely larger brains confer greater cognitive
advantages than relatively larger brains? One possibility is that
as brains get absolutely larger, the total number of neurons
increases, and brains tend to become more modularized, perhaps
facilitating the evolution of new cognitive networks (91, 101,
102). Indeed, recent data suggest that human brains are notable
mainly for their absolute volume, and otherwise conform to the
(re)organizational expectations for a primate brain of their vol-
ume (99, 100, 104–107, 175). Due to limited comparative data on
more detailed aspects of neuroanatomy (e.g., neuron counts, re-
gional volumes, functional connectivity) our analyses were re-
stricted to measures derived from whole brain volumes. However,
an important question for future research will be whether finer
measures of the neuroanatomical substrates involved in regulat-
ing self-control (e.g., prefrontal cortex) explain additional varia-
tion in cognition across species. For example, the best performing
species in our sample were predominantly anthropoid primates,
species that have evolved unique prefrontal areas that are thought
to provide a cognitive advantage in foraging decisions that rely on
executive function (176–178). Nonetheless, other species without
these neuroanatomical specializations also performed well, rais-
ing the possibility that the cognitive skills required for success in
these tasks may be subserved by diverse but functionally similar
neural mechanisms across species (e.g., ref. 179). Thus, although
evolutionary increases in brain volume create the potential for
new functional areas or cognitive networks, more detailed data
from the fields of comparative and behavioral neuroscience will
be essential for understanding the biological basis of species dif-
ferences in cognition (e.g., refs. 180–183).
Within primates we also discovered that dietary breadth is
strongly related to levels of self-control. One plausible ultimate
explanation is that individuals with the most cognitive flexibility
may be most likely to explore and exploit new dietary resources
or methods of food acquisition, which would be especially im-
portant in times of scarcity. If these behaviors conferred fitness
benefits, selection for these traits in particular lineages may have
been an important factor in the evolution of species differences
in self-control. A second possibility is that dietary breadth rep-
resents an ecological constraint on brain evolution, rather than
a selective pressure per se (116, 155, 184, 185). Accordingly,
species with broad diets may be most capable of meeting the
metabolic demands of growing and maintaining larger brains,
with brain enlargement favored through a range of ecological
selective pressures (86). Nonetheless, after accounting for shared
variance between dietary breadth and brain volume, dietary breadth
was still strongly associated with performance on self-control tasks.
Thus, it is likely that dietary breadth acts both as a selective pressure
and a metabolic facilitator of cognitive evolution. Given that
foraging strategies have also been linked to species differences in
cognition in nonprimate taxa (94, 156–159, 161, 162, 166), it re-
mains an important question whether dietary breadth will have
similar explanatory power in other orders of animals.
The data reported here likely represent relatively accurate esti-
mates of species-typical cognition because we collected data from
large samples within each species (mean n=15.3 ±2.0 subjects per
species, range =6–66), scores from multiple populations of the
same species were highly correlated, and performance was not as-
sociated with previous experience in cognitive tasks (SI Text). Thus,
Fig. 2. Cognitive scores as a function of log endocranial volume (ECV) and residual brain volume (ECV residuals). In both tasks and in the composite measure,
ECV was a significant predictor of self-control. Relative brain volume universally explained less variance. Plots show statistically transformed data
(see Methods for details). The gray dashed line shows an alternate model excluding the elephant from analysis. NW, New World; OW, Old World.
www.pnas.org/cgi/doi/10.1073/pnas.1323533111 MacLean et al.
although populations may vary to some extent (e.g., due to differ-
ences in rearing history or experimental experience), these differ-
ences are small relative to the interspecific variation we observed.
The relationship between our experimental measures of self-control
and observational measures of behavioral flexibility also suggest that
our measures have high ecological validity, and underscore the
complementary roles of observational and experimental approaches
for the study of comparative cognition.
Our tasks could be flexibly applied with a range of species
because all species we tested exhibited the perceptual, motiva-
tional, and motoric requirements for participation. Thus, despite
the fact that these species may vary in their reliance on vision,
visual acuity, or motivation for food rewards, all species met the
same pretest criteria, assuring similar proficiency with basic task
demands before being tested. Nonetheless, in any comparative
cognitive test it is possible that features of individual tasks are
more appropriate for some species than others. One mechanism
to overcome this challenge is through the approach implemented
here, in which (i) multiple tasks designed to measure the same
underlying construct are used, (ii) the correlation between tasks
is assessed across species, and (iii) a composite score averaging
performance across tasks is used as the primary dependent
measure. In cases where data are limited to a single measure from
a species, the results must be interpreted extremely cautiously
(e.g., performance of the Asian elephant on the A-not-B task).
The relationship between self-control and absolute brain vol-
ume is unlikely to be a nonadaptive byproduct of selection for
increases in body size for several reasons. First, a comparison of
models using only body mass or ECV as the predictor of com-
posite scores yielded stronger support for the ECV model both
in an analysis across all species [change in the Akaike informa-
tion criterion (Δ
)=0.77], and within primates (Δ
However, it is only within primates that the change in AICc
between the body mass and ECV models exceeded the two-unit
convention for meaningful difference (186). Second, the number
of neurons in primate brains scales isometrically with brain size,
indicating selection for constant neural density and neuron size,
a scaling relationship that contrasts with other orders of animals
(100). Thus, the relationship between absolute brain volume and
self-control may be most pronounced in the primate species in
our sample, and may not generalize to all other large-brained
animals (e.g., whales, elephants), or taxa whose brains are
organized differently than primates (e.g., birds). Nonetheless,
even when removing primate species from the analysis, absolute
brain volume remained the strongest predictor of species dif-
ferences in self-control. Third, ancestral state reconstructions
indicate that both absolute and relative brain volume have
Table 2. The relationship between brain volume, socioecology, observational measures of
cognition, and performance on the cognitive tasks
Data source Explanatory variable Dependent measure tdf Pr
All species Absolute brain volume Cylinder 4.79 30 <0.01 0.43 0.00
Absolute brain volume A-not-B 1.03 25 0.16 0.04 0.69
Absolute brain volume A-not-B (no elephant) 5.44 24 <0.01 0.55 0.00
Absolute brain volume Composite 5.67 21 <0.01 0.60 0.00
Residual brain volume Cylinder 2.31 30 0.01 0.15 0.98
Residual brain volume A-not-B 0.05 25 0.96 <0.01 0.72
Residual brain volume A-not-B (no elephant) 0.33 24 0.37 <0.01 0.58
Residual brain volume Composite 0.78 21 0.22 0.03 0.67
Nonprimates Absolute brain volume Cylinder 3.30 10 <0.01 0.52 0.00
Absolute brain volume A-not-B −0.59 7 0.71 0.05 0.00
Absolute brain volume Composite 2.54 6 0.02 0.52 0.00
Residual brain volume Cylinder 1.12 10 0.14 0.11 0.69
Residual brain volume A-not-B −1.83 7 0.95 0.32 0.00
Residual brain volume Composite −0.58 6 0.71 0.05 0.25
Primates Absolute brain volume Cylinder 5.01 18 <0.01 0.58 0.00
Absolute brain volume A-not-B 4.39 16 <0.01 0.55 0.00
Absolute brain volume Composite 5.27 13 <0.01 0.68 0.00
Residual brain volume Cylinder 2.26 18 0.02 0.22 0.93
Residual brain volume A-not-B 2.64 16 0.01 0.30 0.00
Residual brain volume Composite 1.69 13 0.06 0.18 0.60
Primates Population group size Composite −0.75 13 0.77 0.04 0.83
Foraging group size Composite −0.33 13 0.63 0.01 0.82
Percent fruit in diet Composite 0.11 13 0.46 <0.01 0.85
Dietary breadth Composite 4.99 12 <0.01 0.68 0.69
Social learning Composite 2.63 9 0.03 0.44 0.00
Innovation Composite 1.99 9 0.08 0.31 0.00
Extractive foraging Composite 3.10 9 0.01 0.52 0.00
Tool use Composite 3.12 9 0.01 0.52 0.00
Tactical deception Composite 4.06 9 <0.01 0.65 0.00
Composite 3.61 9 <0.01 0.59 0.00
PCA 1 Composite 3.61 9 <0.01 0.59 0.00
The sign of the tstatistic indicates the direction of the relationship between variables. Data regarding social
learning, innovation, extractive foraging, tool use, tactical deception (all of which covary), and primate g
were adjusted for research effort and obtained from Reader et al. (92) and Byrne and Corp (124). PCA 1 is
equivalent to the g
score calculated by Reader et al. (92) restricted to species in this dataset. We used the arcsine
square-root transformed mean proportion of correct responses for each species as the dependent measure in all
analyses, as this best met the statistical assumptions of our tests. Socioecological data were log transformed
(group size) or arcsine square root transformed (proportion fruit in diet) for analysis.
MacLean et al. PNAS Early Edition
increased over time in primates, whereas body mass has not
(187). Lastly, although not as predictive as absolute brain vol-
ume, residual brain volume was a significant predictor of self-
control in several of our analyses. Thus, multiple lines of evi-
dence implicate selection for brain volume (and organization)
independent of selection for body size, and our data illustrate the
cognitive consequences of these evolutionary trends.
With the exception of dietary breadth we found no significant
relationships between several socioecological variables and mea-
sures of self-control. These findings are especially surprising given
that both the percentage of fruit in the diet and social group size
correlate positively with neocortex ratio in anthropoid primates
(86, 142). Our findings suggest that the effect of social and eco-
logical complexity may be limited to influencing more specialized,
and potentially domain-specific forms of cognition (188–196). For
example, among lemurs, sensitivity to cues of visual attention used
to outcompete others for food covaries positively with social group
size, whereas a nonsocial measure of self-control does not (146).
Therefore, our ability to evaluate the predicted relationships be-
tween socioecology and cognition will depend on measures designed
to assess skills in specific cognitive domains (e.g., visual perspective-
taking or spatial memory). In addition, more nuanced measures of
social and ecological complexity (e.g., coalitions or social networks)
may be necessary to detect these relationships (197).
Overall, our results present a critical step toward understanding
the cognitive implications of evolutionary shifts in brain volume and
dietary complexity. They also underscore the need for future cog-
nitive studies investigating how ecological factors drive cognitive
evolution in different psychological domains. These experimental
measures will be particularly important given that even the most
predictive neuroanatomical measures failed to account for more
than 30% of cognitive variance across species in this study. With
a growing comparative database on the cognitive skills of animals,
we will gain significant insights into the nature of intelligence itself,
and the extent to which changes in specific cognitive abilities have
evolved together, or mosaically, across species. This increased
knowledge of cognitive variation among living species will also set
the stage for stronger reconstructions of cognitive evolutionary
history. These approaches will be especially important given that
cognition leaves so few traces in the fossil record. In the era of
comparative genomics and neurobiology, this research provides
a critical first step toward mapping the primate cognitive phenome
and unraveling the evolutionary processes that gave rise to the
In the A-not-B task, subjects were required to resist searching for food in
a previous hiding place when the food reward was visibly moved to a novel
location. Subjects watched as food was hidden in one of three containers
positioned at the exterior of a three-container array and were required to
correctly locate the food in this container on three consecutive familiariza-
tion trials before advancing to the test. In the test trial, subjects initially saw
the food hidden in the same container (container A), but then watched as
the food was moved to another container at the other end of the array
(container B; Movie S1). Subjects were then allowed to search for the hidden
food, and the accuracy of the first search location was recorded. This pro-
cedure differs slightly from the original task used by Piaget (198) in which
test trials involved the immediate hiding of the reward in location B, with-
out first hiding the reward in location A. Our method followed the pro-
cedure of Amici et al. (174), and similarly we conducted one test trial per
subject. For the A-not-B task, our dependent measure was the percentage of
individuals that responded correctly on the test trial within each species.
In the cylinder task, subjects were first familiarized with finding a piece of
food hidden inside an opaque cylinder. Subjects were required to successfully
find the food by detouring to the side of the cylinder on four of five con-
secutive trials before advancing to the test. In the following 10 test trials,
a transparent cylinder was substituted for the opaque cylinder. To success-
fully retrieve the food, subjects needed to inhibit the impulse to reach for
the food directly (bumping into the cylinder) in favor of the detour response
they had used during the familiarization (Movie S2). Although subjects may
have initially failed to perceive the transparent barrier on the first test trial,
they had ample opportunity to adjust their behavior through visual, audi-
tory, and tactile feedback across the 10 test trials. For the cylinder task our
dependent measure was the percentage of test trials that a subject per-
formed the correct detour response, which was averaged across individuals
within species to obtain species means.
In both tasks, all species were required to meet the same pretest criteria,
demonstrating a basic understanding of the task, and allowing meaningful
comparison of test data across species. Although the number of trials
required to meet these criteria varied between species, we found no sig-
nificant relationship between thenumber of pretest trials andtest performance
on either task (A-not-B: t
=−1.83, λ=0.52, P=0.08; cylinder task: t
λ=0.69, P=0.26). For analyses involving brain volume, log ECV was used as the
measure of absolute brain volume and we extracted residuals from a PGLS
Fig. 3. Cognitive scores for primates as a function of (A) absolute and re-
sidual endocranial volume (ECV), (B) foraging and population social group
size, and (C) frugivory and dietary breadth. Absolute ECV, residual ECV, and
dietary breadth covaried positively with measures of self-control. Plots show
statistically transformed data (see Methods and Table 2 for details).
Fig. 4. Ancestral state reconstruction of cognitive skills for self-control. We
generated the maximum likelihood estimates for ancestral states along the
primate phylogeny using data from the composite measure (average score across
tasks for species that participated in both tasks). The red circles along the tips of
the phylogeny are proportional to the extant species’composite scores (larger
circles represent higher scores). The blue circles at the internal nodes of the
phylogeny represent the estimated ancestral states for the composite score, with
the estimated value indicated within circles at each node.
www.pnas.org/cgi/doi/10.1073/pnas.1323533111 MacLean et al.
model of log ECV predicted by log body mass as our primary measure of rel-
ative brain volume (ECV residuals; SI Text). As an additional measure of relative
brain size we incorporated Jerrison’s (108) EQ, calculated as EQ =brain mass/
0.12 ×body mass
. Although EQ and a residuals approach both measure
deviation from an expected brain-to-body scaling relationship, they differ in
that EQ measures deviation from a previously estimated allometric exponent
using a larger dataset of species, whereas ECV residuals are derived from the
actual scaling relationship within our sample, while accounting for phylogeny.
To control for the nonindependence of species level data, we used PGLS to
test the association between performance on the cognitive tasks and the
explanatory variables associated with each hypothesis. We predicted that
brain volume, group size, and measures of dietary complexity would covary
positively with cognitive performance. Thus, each of these predictions was
evaluated using directional tests following the conventions (δ=0.01, γ=
0.04) recommended by Rice and Gaines (199), which allocates proportionally
more of the null distribution in the predicted direction, while retaining
statistical power to detect unexpected patterns in the opposite direction.
We incorporated the parameter λin the PGLS models to estimate phylo-
genetic signal and regression parameters simultaneously, using a maximum
likelihood procedure (200, 201). This research was approved by the Duke
University Institutional Animal Care and Use Committee (protocol numbers
A303-11-12, A199-11-08, and A055-11-03).
ACKNOWLEDGMENTS. We thank Natalie Cooper and Sunil Suchindran for
statistical advice; Jeff Stevens and two anonymous reviewers for comments
on drafts of this manuscript; and Ikuma Adachi, Nathan Emery, Daniel Haun,
Marc Hauser, Ludwig Huber, Al Kamil, Chris Krupenye, Luke Matthews, Collin
McCabe, Alexandra Rosati, Kara Schroepfer, Jeff Stevens, Tara Stoinski,
Michael Tomasello, and Victoria Wobber for their helpful discussion during
the workshops from which this research emerged. F. Aureli and F. Amici
thank Iber Rodriguez Castillo, Roberto Pacheco Mendez, Fernando Victoria
Arceo, Liesbeth Sterck, Barbara Tiddi, and all the animal keepers at the
facilities where the data were collected for support and cooperation. K.E.S.
and A.P. thank Steve Nichols and the staff at The Parrot Zoo. This work was
supported by the National Evolutionary Synthesis Center (NESCent) through
support of a working group led by C.L.N. and B.H. NESCent is supported by
the National Science Foundation (NSF) EF-0905606. For training in phyloge-
netic comparative methods, we thank the AnthroTree Workshop (supported
by NSF BCS-0923791). Y.S. thanks the National Natural Science Foundation of
China (Project 31170995) and National Basic Research Program (973 Program:
2010CB833904). E.E.B. thanks the Duke Vertical Integration Program and the
Duke Undergraduate Research Support Office. J.M.P. was supported by
a Newton International Fellowship from the Royal Society and the British
Academy. L.R.S. thanks the James S. McDonnell Foundation for Award
220020242. L.J.N.B. and M.L.P. acknowledge the National Institutes of
Mental Health (R01-MH096875 and R01-MH089484), a Duke Institute for
Brain Sciences Incubator Award (to M.L.P.), and a Duke Center for Interdis-
ciplinary Decision Sciences Fellowship (to L.J.N.B.). E.V. and E.A. thank the
Programma Nazionale per la Ricerca–Consiglio Nazionale delle Ricerche
(CNR) Aging Program 2012–2014 for financial support, Roma Capitale–
Museo Civico di Zoologia and Fondazione Bioparco for hosting the Istituto
di Scienze e Tecnologie della Cognizione–CNR Unit of Cognitive Primatology
and Primate Centre, and Massimiliano Bianchi and Simone Catarinacci
for assistance with capuchin monkeys. K.F. thanks the Japan Society for
the Promotion of Science (JSPS) for Grant-in-Aid for Scientific Research
20220004. F. Aureli thanks the Stages in the Evolution and Development
of Sign Use project (Contract 012-984 NESTPathfinder) and the Integrating
Cooperation Research Across Europe project (Contract 043318), both funded
by the European Community’s Sixth Framework Programme (FP6/2002–
2006). F. Amici was supported by Humboldt Research Fellowship for Post-
doctoral Researchers (Humboldt ID 1138999). L.F.J. and M.M.D. acknowledge
NSF Electrical, Communications, and Cyber Systems Grant 1028319 (to L.F.J.)
and an NSF Graduate Fellowship (to M.M.D.). C.H. thanks Grant-in-Aid for
JSPS Fellows (10J04395). A.T. thanks Research Fellowships of the JSPS for
Young Scientists (21264). F.R. and Z.V. acknowledge Austrian Science Fund
(FWF) Project P21244-B17, the European Research Council (ERC) under the
European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant
Agreement 311870 (to F.R.), Vienna Science and Technology Fund Project
CS11-026 (to Z.V.), and many privatesponsors, including Royal Canin for finan-
cial support and the Game Park Ernstbrunn for hosting the Wolf Science Cen-
ter. S.M.R. thanks the Natural Sciences and Engineering Research Council
(Canada). J.K.Y. thanks the US Department of Agriculture–Wildlife Services–
National Wildlife Research Center. J.F.C. thanks the James S. McDonnell Foun-
dation and Alfred P. Sloan Foundation. E.L.M. and B.H. thank the Duke
Lemur Center and acknowledge National Institutes of Health Grant 5 R03
HD070649-02 and NSF Grants DGE-1106401, NSF-BCS-27552, and NSF-BCS-
25172. This is Publication 1265 of the Duke Lemur Center.
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