royalsocietypublishing.org/journal/rspb
Research
Cite this article: Morgan TJH, Suchow JW,
Griffiths TL. 2022 The experimental evolutionof human culture: flexibility, fidelity andenvironmental instability. Proc. R. Soc. B 289:
20221614.https://doi.org/10.1098/rspb.2022.1614
Received: 19 August 2022
Accepted: 7 October 2022
Subject Category:
Behaviour
Subject Areas:
behaviour, cognition, evolution
Keywords:
evolution, cultural evolution, cognition,experimental evolution, social learning,conformist transmission
Author for correspondence:
Thomas J. H. Morgane-mail: thomas.j.h.morgan@asu.edu
†These authors contributed equally to this
work.
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.c.6251631.The experimental evolution of human
culture: flexibility, fidelity andenvironmental instability
Thomas J. H. Morgan1,2,†, Jordan W. Suchow3,†and Thomas L. Griffiths4,5
1School of Human Evolution and Social Change, Arizona State University, 900 S. Cady Mall, Tempe,
AZ 85281, USA
2Institute of Human Origins, Arizona State University, 777 E University Drive, Tempe, AZ 85287, USA
3School of Business, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA
4Department of Psychology, Princeton University, 320 Peretsman Scully Hall, Princeton, NJ 08540, USA
5Department of Computer Science, Princeton University, 417 Computer Science, Princeton, NJ 08540, USA
The past 2 Myr have seen both unprecedented environmental instability and
the evolution of the human capacity for complex culture. This, along with
the observation that cultural evolution occurs faster than genetic evolution,has led to the suggestion that culture is an adaptation to an unstable
environment. We test this hypothesis by examining the ability of human
social learning to respond to environmental changes. We do this by insertinghuman participants ( n= 4800) into evolutionary simulations with a changing
environment while varying the social information available to individuals
across five conditions. We find that human social learning shows somesigns of adaptation to environmental instability, including critical social
learning, the adoption of up-and-coming traits and, unexpectedly, contrari-
ness. However, these are insufficient to avoid significant fitness declineswhen the environment changes, and many individuals are highly confor-
mist, which exacerbates the fitness effects of environmental change. We
conclude that human social learning reflects a compromise between the com-peting needs for flexibility to accommodate environmental change and
fidelity to accurately transmit valuable cultural information.
1. Introduction
For most species, adaptation occurs through a combination of short-term plas-ticity and long-term genetic change. Although many animals also learn from
each other [1], the products of such social learning rarely accumulate overtime or exceed the capacity of any one individual [2]. Our species, however,
is an exception: we extensively adapt through culture [3,4], which routinely
involves complex beliefs, technologies and practices that evolved across gener-ations. Cultural adaptation is a population-level process that in many ways
mirrors genetic evolution [5,6], with innovation and social learning being the
cultural analogues of genetic mutation and transmission. However, there aremany important differences. For one, whereas genetic mutations are random
with respect to fitness, cultural innovations can be guided by reasoning [7].
Moreover, whereas genetic transmission occurs across generations, from parentsto offspring at the moment of conception, cultural transmission can occur
both across and within generations, between any individuals, and at any
time. These differences allow cultural evolution to proceed far faster than gen-etic change [8]. Moreover, the lengthening of human life-history magnified this
discrepancy by slowing genetic evolution while supporting complex cognition
that facilitated cultural evolution [9,10].
Given the fast pace of cultural evolution, it has been argued that culture itself
is an adaptation allowing us to keep pace with an increasingly unstable environ-
ment [11]. Indeed, the past 2 Myr, during which human brain size grewtremendously [12] and cumulative cultural capacities emerged [13], have been a
time of unprecedented temporal environmental variation. For instance, a 41 kyr
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temperature oscillation began 1.5 Ma, being replaced with a
more severe 100 kyr cycle around 650 ka [14]. Temperature
change has also been observed on faster timescales, including
a series of sudden temperature increases of up to 7°C duringthe last glacial period (approx. 115 –12 ka) that lasted just a
few hundred years [15], and sudden warming followed by gra-
dual cooling, likely caused by the collapse, and subsequentregrowth, of North Atlantic icecaps [16]. Similar fluctuations
are also seen in the previous inter-glacial (approx. 125 –
115 ka) with temperature dropping up to 15°C, but recoveringwithin as few as 70 years [17]. Such rapid change may continue
further back in time, however, datasets with sufficient resol-
ution to test this hypothesis are not currently available.Collectively, these results suggest that our species, including
our capacity for cultural inheritance, evolved during a period
of intense climatic instability.
The hypothesized mechanism by which climactic instabil-
ity generates selection for culture is that instability creates
environmental novelties which require behavioural inno-vations and culture is an effective means to acquire
innovations. There is evidence from multiple taxa supporting
the link between brain size and success in novel environments[18–20], and the mediating cause has been identified as inno-
vation rate in the case of birds [21]. Moreover, fossil evidence
suggests that brain size increases during times of environ-mental instability , for instance in elephants [22]. Thus, the
link between environmental instability , innovation and brain
size appears widespread. In addition to this, humans uniquelyevolved the additional capacity for complex culture, which
appears to have coevolved with brain size in primates [10].
While the hypothesis that culture is an adaptation to an
unstable environment is compelling, it has nonetheless run
against a body of cultural evolutionary theory that shows
that rapid environmental change can favour asocial, asopposed to social, learning. This is because, as with any
system of inheritance, culture risks transmitting out-of-date
information, and this risk grows as environments becomeincreasingly unstable. For instance, a model of the evolution
of social learning in an otherwise asocial population found
that the equilibrium frequency of social learners is inverselyproportional to the frequency of environmental change [23].
Further work found that pure social learning can persist
(albeit at low frequencies) provided that, for every generationin which the environment changes, there are at least two in
which it does not; and that a mixed strategy including some
social learning is always stable, but the amount of sociallearning decreases with the rate of environmental change
[24]. As such, the most rapid temperature changes discussed
above, which occur suddenly and revert within 70 years, maybe so fast that selection favoured individual plasticity as
opposed to the inheritance of cultural practices that
developed over multiple generations.
One way this tension would be resolved is if historical
environmental variability fell in a middle ground: fast
enough for genetic evolution to be insufficient, but slowenough for multi-generational cultural processes to keep up.
Though possible, this would seem to depend on a remarkably
well-calibrated rate of environmental change to favour theextreme reliance on social learning characteristic of our species.
Moreover, such a possibility is hard to test and is challenged by
evidence of decadal and centennial environmental changementioned above, as well as the fact that while many species
experienced environmental instability, only humans evolvedcomplex culture. Thus, social learning per se is unlikely to
have been favoured by environmental instability.
A tweak to this hypothesis is that strategic use of social
learning broadened the conditions under which social learningis favoured and allowed it to remain effective in the face of
high-frequency environmental change. Theory clearly shows
this is possible: the success of social learning in an unstableenvironment increases when individuals collect and evaluate
both asocial and social information [25] or if social learners
additionally collect asocial information when social infor-mation is inadequate (referred to as ‘critical social learning ’
[26]). Theory also suggests that as different social learning
rules compete to parasitize the information produced by aso-cial learners, selection will favour whatever social learning
psychology relies on the lowest frequency of asocial learning
to be effective, thereby maximizing the frequency of sociallearning in the population [27]. Consistent with this, an evol-
utionary tournament that pitted different learning strategies
against each other was won by a strategy that engaged in agreat deal of social learning, was a highly effective information
parasite, and timed its social learning to avoid periods of
environmental change [28]. Experimental data on this topic islimited, although humans have been observed to respond to
environmental instability by selectively adopting ‘up-and-
coming ’options, presumably on the assumption that these
traits are being favoured by novel conditions [29].
While such transmission biases canrender social learning
adaptive against high-frequency environmental change, thisis not necessarily the case and, in fact, other biases have been
documented to decrease the fitness of social learning following
environmental change. One example is conformist transmission ,
the tendency to disproportionately adopt majority beliefs
[5,30]. Because conformist transmission blocks the spread of
low-frequency traits, and because innovations necessarilystart at low frequencies, conformist transmission prevents
populations from tracking environmental change. Nonethe-
less, theory shows that conformist transmission is broadlyfavoured by selection [31,32], even to the extent that it causes
catastrophic fitness decreases after environmental change
[33]. There is also empirical evidence for conformist trans-mission in humans [34 –39]. Therefore, while human social
learning is clearly sensitive to a range of factors, it is not clear
whether it accommodates environmental change any better
than random social learning. As a result, how the evolution
of culture proceeded against Pleistocene climactic instability
remains uncertain.
Here, we present a series of experimental evolutionary
simulations [40] that directly assess how humans use social
learning to respond to environmental change. An experimentalevolutionary simulation can be understood via its relation to
theoretical agent-based simulations: computer programs in
which simulated agents, with programmed AI, inhabit andevolve within a virtual world. Such simulations give insights
into real-world processes, however, they necessitate many sim-
plifying assumptions concerning the agents, their cognition,and the world they inhabit. Experimental evolutionary simu-
lations differ in that agents do not need any AI because
human participants make decisions on their behalf. As such,while the agents inhabit a simulated world, and have a simu-
lated genome, their decisions, and the effect of those decisions
on evolutionary dynamics, reflect real human psychology.
Across 180 simulations, we consider five different forms of
social information. These range from seeing only the decisionroyalsocietypublishing.org/journal/rspb Proc. R. Soc. B 289: 202216142
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of a single randomly chosen demonstrator (as per [23]), to seeing
the decisions of multiple individuals from multiple generations
with the option of additionally receiving asocial information.These richer forms of social information enable conformist trans-
mission [5,30], the identification of up-and-coming traits [29]
and critical social learning [26]. Across these conditions, wefind that human social learning exhibits features that both help
and hinder its evolution in an unstable environment: On the
one hand, individuals use social consensus to strategically timecontrariness and critical social learning, which improves the
adaptive value of culture. Moreover, a minority of persistently
contrary participants create a beneficial reservoir of cultural vari-ation. On the other hand, most individuals are highly
conformist, which creates cultural inertia that causes more
severe decreases in fitness following environmental change.We conclude that human social learning is a compromise
between the competing needs of flexibility and fidelity.
2. Methods
Data were collected online, using the experiment-automation
platform Dallinger (https://dallinger.readthedocs.io). Data werecollected in three periods between 25 September 2015 and 25 Octo-ber 2015, with the first period focused on condition 1, the secondconditions 2 and 3, and the third conditions 4 and 5.
1In total, 4800
participants took part (1600 in each period) recruited from Ama-zon’s Mechanical Turk in batches of 40 (participants who took
part in one period could not take part in the others). Within eachperiod, each participant took part in multiple evolutionary simu-lations (in series, randomly ordered), and within each simulationthey took on the role of a single agent. In total, 180 simulationswere conducted, each with a population size of 40 agents and lasting40 generations, totalling 1600 agents per simulation.
All simulations involved an unstable simulated environment
that changed between two states. An agent ’s fitness depended
on the correct identification of the current state by their associatedparticipant. Within each simulation, a simulated genome markedeach agent as either an asocial or social learner and this affectedthe stimuli presented to the corresponding participant. All popu-lations were initially composed entirely of asocial learners, butthe social learning allele could arise via mutation and, if favoured,spread via selection. Across conditions, we varied the richness ofthe stimuli associated with the social learning allele (table 1).
Below we describe the methods, first focusing on the partici-
pant experience, involving (1) recruitment, (2) the task and (3)the experimental procedure. We then describe the larger simu-lation of which this was a part, including (4) the environmentand (5) genetics, fitness and reproduction.
(a) Recruitment
Participants were recruited via Amazon ’s Mechanical Turk to take
part in a computer-based binary choice task. Participants wererecruited in batches of 40 (enough to complete a single
generation of a simulation), with another batch being recruitedonce all participants in the current batch had completed the exper-iment. Participants who failed attention checks (described below),dropped out during the experiment, or submitted incomplete ormalformed data were automatically replaced. Recruitment waslimited to US participants for whom at least 95% of their previoustasks on Mechanical Turk had been approved.
(b) The task
Within each simulation, participants completed a single trial of atask where they were asked to determine whether an array of 80blue or yellow dots contained more blue or more yellow dots.After receiving relevant information, participants made theirdecision by clicking one of two buttons, labelled ‘blue’and
‘yellow ’, respectively, and moved on to the next simulation
without receiving feedback.
Participants who were assigned the asocial allele saw the
array of dots for 1 s (figure 1 a). The dots were non-overlapping,
with radii drawn from a uniform distribution ranging from 10 to20 pixels. Fifty-two dots were of the majority colour (versus aminority of 28), this value was chosen based on pilot work thatfound participants answered correctly on greater than 95% ofsuch trials, making asocial information highly reliable.
Participants who were assigned the social allele received social
information , accurate information about the decision made by one
or more other participants. Across simulations, the social infor-mation took five forms, as follows. (1) ‘Poor ’: the decision of a
single participant randomly selected from the previous generationof the same simulation (figure 1 b). (2) ‘Moderate ’: the decisions of
all 40 participants from the previous generation of the same simu-lation (figure 1 b). (3) ‘Rich ’: the decisions of all 120 participants
from the previous three generations of the same simulation,broken down by generation. (4) ‘Critical-moderate ’: moderate
social information with the option to additionally receive asocialinformation (i.e. the dot presentation). (5) ‘Critical-rich ’: rich
social information with the option to additionally receive asocialinformation. As such, in the first three conditions, social learnerscould not see the dots directly.
(c) Procedure
Upon agreeing to take part, participants were given instructionsand completed five practice trials in which they received asocialinformation (i.e. they saw the dots). These trials were extremelyeasy (64 versus 16 dots) and served to familiarize participantswith the task. Participants then took part in multiple simulationsin a random order, completing a single trial in each simulation.However, randomly placed among the simulation trials were12 trials that served to check that participants were paying atten-tion. These trials involved only asocial information and were alsovery easy (60 versus 20 dots). If a participant responded correctlyin fewer than 10 of these 12 trials, they were deemed to be inat-tentive, their data was discarded, and another participant wasTable 1. Summary of experimental conditions.
period condition number of simulations social information critical social learning
1(n= 1600) 1 36 poor no
2(n= 1600) 2 moderate
3 rich
3(n= 1600) 4 moderate yes
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automatically recruited to replace them. Upon completing all
trials, participants were thanked, debriefed and paid.
Experimental sessions lasted around 10 min and participants
were paid $1 for taking part, with a bonus of up to $1 dependingon their performance. Participants earned 1 point for each simu-lation trial they answered correctly but paid 0.15 points to viewthe dots when taking part as a social learner in the critical sociallearning conditions. Their bonus b(in US dollars) was calculated as
b¼max 2
p
t/C00:5/C16/C17
,0/C16/C17
,
where pis the number of points earned and tis the number of trials.
Thus, performance at chance or below did not earn a bonus, whileengaging in critical social learning came at a small cost.
(d) The environment
Each simulation took place within a simulated environment whichwas in one of two states, ‘blue’or‘yellow ’. The environmental state
accurately determined the colour of the majority of dots. The initialstate of the environment was randomized for each simulation,and it changed every 10 generations, switching to the other state.Such changes rendered social information (which comes fromparticipants in prior generations) out of date.
In the ‘poor ’condition, participants were not informed that
the environment could change (as per [23]), but in all otherconditions they were (although they were not told when orhow often this occurred). In the ‘poor ’condition, the environ-
ment changed after the 10th, 20th, 30th and 40th generations.In the other conditions, environmental change was randomlystaggered such that environments were not in sync across simu-lations, although they still changed every 10 generations withineach simulation. This was to prevent participants applyingtheir experience from one simulation to another.
(e) Genetics, fitness and reproduction
Within each simulation, each agent was assigned a haploidgenome containing a single locus with two possible alleles; ‘aso-
cial’and ‘social ’. Participants whose agent had the asocial allele
(henceforth ‘asocial learners ’) received asocial information, while
participants whose agent had the social allele (henceforth ‘social
learners ’) received social information.
In all simulations, the first generation of agents were all aso-
cial learners (the first three generations in the case of simulationswith ’rich’social information); however, after that the social allele
was allowed to invade via mutation, with a mutation rate of 0.1.After making a decision, agents were assigned a fitness score
fas follows: Agents started with a baseline fitness of 0.3, the
payoff from being correct was 1, while the cost of collecting aso-cial information was 0.3. Thus, asocial information provides acostly, but reliable, cue, while social information is cheap, butpotentially out of date. Note that social learners in the ‘critical
social learning ’conditions (4 and 5) who chose to additionally
view asocial information also paid this cost. These values werethen squared, thereby increasing the strength of selection, andallowing the production of robust evolutionary dynamics over40 generations. The resulting values are
f¼1, asocial or critical, and right
0, asocial or critical, and wrong1:69, social and right
0:09, social and wrong8
>><
>>::
Once all 40 participants within a generation had completed
all trials, a new group of 40 participants was recruited. Withineach simulation, each new participant was assigned to a newagent that inherited the allele of an agent from the previous gen-eration of the same simulation (subject to mutation), with theparent agent selected with probability proportional to their fit-ness (see schematic in figure 1 d). Note that while this led to
differential reproduction, fitness did not affect the probabilitythat a participant was chosen to be copied by social learners.As such, selection acted directly on genes, but not cultural traits.
3. Analyses
We conducted four analyses looking at population-level pat-
terns across generations, and two further analyses examininghow individuals responded to social information. Unless
stated otherwise, all data were subject to Bayesian analysis
using Markov chain Monte Carlo (MCMC) methods via the Rpackage rjags [41] to generate posterior samples and, thus, par-
ameter estimates. All models involved three parallel chains for
each parameter, the Gelman –Rubin statistic was used to check
for convergence (upper C.I. < 1.01), and all estimated values
are based on an effective sample size of at least 3000.
(a) Population-level analyses
To describe the population dynamics, we modelled four
outcomes across generations and experimental conditions:
(a) (b)( d)
(c)
Figure 1. Experimental design. ( a) The task interface seen by asocial learners: participants were presented with a display showing 80 blue or yellow dots and were
asked to decide which colour was the most numerous. ( b,c) The task interface seen by social learners in the ( b) poor and ( c) moderate conditions: participants did
not see the dots themselves but were instead told what a previous participant, or the previous generation of participants, had decided. Rich informat ion was the
same, but covered the three most recent generations (e.g. ‘Three batches ago, X people decided BLUE and Y people decided YELLOW. Two batches ago …’ and so
on). Note that, within the social information, generations are referred to as ‘batches ’. This word was chosen as it was considered more comprehensible to par-
ticipants who did not know they were in an evolutionary simulation. ( d) A diagram of the simulation structure: Human participants take part as agents, which are
arranged into 40 non-overlapping generations of 40 individuals. As the simulation proceeds, genetic and cultural inheritance occurs between gener ations. (Online
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(1) whether an agent had the asocial or social allele ( n= 216
000); (2) whether a social learner answered correctly, given
they did not choose to see the dots if in the critical-moderate
or critical-rich conditions ( n= 120 377); (3) whether a social
learner was contrary, given they did not choose to see
the dots if in the critical-moderate or critical-rich conditions
(n= 112 410, contrariness is defined below); and (4) whether
participants on social trials (where critical social learning
was possible) additionally chose to view asocial information
(n= 49 079). Collectively, these outcomes characterize the
frequency, success and behaviour of the social learners
over time. In addition to accounting for generation and
experimental condition, we also included hierarchically mod-elled effects for each repeat-simulation or participant, as
appropriate (see full model descriptions below).
Contrariness was defined as follows. With poor social
information, contrariness is making the opposite decision to
the lone demonstrator. With moderate social information, it
is making the opposite decision to the majority of the 40demonstrators (cases where the demonstrators were split
evenly were excluded from the analysis). Due to the genera-
tional structure of rich social information, there is no singleway to define contrariness in this case, but we adopted the
definition as going against what the majority of the 120
demonstrators do (cases where the demonstrators were splitevenly were excluded from the analysis).
In all cases, time was measured not in terms of the
number of generations for which the simulation had beenrunning (ranging from 1 to 40), but instead as the number
of generations since the environment changed (ranging
from 0 to 9), henceforth referred to as the ‘effective gener-
ation ’. In addition, estimates were fit separately for each
effective generation, meaning we did not assume any particu-
lar pattern in the dynamics over time. Data from the first 10generations were excluded to allow simulations to reach equi-
librium, after which the remaining data shows the response
of the population to multiple bouts of environmentalchange. Pooling the data into effective generations allows
more accurate inferences to be drawn about the evolutionary
dynamics following each instance of change.
The analysis of gene frequency is as follows:
S/differenceBðpÞ;
and logit( p)¼
bG,Cþ1N,
where Sis whether each agent had the social allele (1 = they
did, 0 = they did not), Gis their effective generation (an inte-
ger from 0 to 9), Cis their experimental condition (an integer
label, from 1 to 5) and Nis the numeric ID of the repeat-
simulation (an integer from 1 to 180). In addition, Bis the
Bernoulli distribution, while band1are parameters to be
estimated. The priors were as follows:
b0:9,1:5/differenceN (0, 10),
11:180/differenceN (0,s),
and s/differenceE(4),
where sis the standard deviation of the repeat-simulation
level effects and Eis the exponential distribution.
The other three analyses of population dynamics had
the same structure, but with the following differences:
In the analysis of decision accuracy the outcome variable is
whether a participant made the right decision on a giventrial (1 = they did, 0 = they didn ’t). In the analysis of contrari-
ness, the outcome variable is whether a participant was
contrary on a given trial (1 = they were, 0 = they weren ’t),
and instead of effects of each repeat-simulation, the modelincluded hierarchically modelled effects for each participant
as follows:
logit( p)¼
bG,Cþ1Q,
11:3578/differenceN (0,s),
and s/differenceE(1),
where Qis the numeric ID of the participant. In the analysis of
critical social learning, the outcome variable is whethera participant with the social allele additionally chose to view
asocial information (1 = they did, 0 = they didn ’t), and effects
for each repeat-simulation were again replaced with hierarchicaleffects for each participant (with 1200 total participants).
(b) Individual-level analyses
To further understand how individuals used social infor-mation we conducted two analyses testing for conformist
transmission. In the first, we examined the response to mod-
erate social information, in the absence of critical sociallearning (i.e. data from condition 2). The analysis modelled
whether a social learner chose blue, given the proportion of
demonstrators who chose blue. We used an established func-tion to test for conformist transmission [35,42] and included
hierarchical participant effects to account for individual vari-
ation in sensitivity to social information. The model structureis as follows:
B/differenceB(p),
logit( p)¼
b1þ(b2þ1Q)S,
and S¼Rb3
Rb3þ(1/C0R)b3/C00:5,
where Bis whether the participant chose blue (1 = they did, 0 =
they didn ’t),Qis the numeric ID of the participant (an integer
from 1 to 1600) and Ris the proportion of demonstrators choos-
ing blue. The parameter b1is the baseline propensity to choose
blue, b2is the sensitivity to social influence and b3determines
whether social learning is conformist. Values of b3over 1
guarantee conformist transmission, although, given the
logit function, lower values can also indicate conformist
transmission if b2is high enough. The priors are as follows:
b1,2/differenceN (0, 5),
b3/differenceG(2, 1),
11:1600/differenceN (0,s),
and s/differenceE(0:1),
where Gis the Gamma distribution.
We also examined the response to rich social information
(i.e. data from condition 3). The analysis was similar to that
above; however, because rich social information presented
the decisions of three generations of prior participants,we modelled each generation as having a separate additive
influence. The model structure is as follows:
B/differenceB(p),
logit( p)¼
b1þ(b21Q)(S1þb4S2þb5S3),
and Sg¼Rb3g
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where Rgis the proportion of demonstrators who chose blue
ggenerations ago. Note that: the intensity of conformist trans-
mission ( b3) is shared across all three generations; the
individual level effects are multiplicative instead of additive
(this change was made to improve MCMC sampling effi-
ciency); and b2determines the influence of the most recent
generation, while b4andb5determine the relative influence
of the second and third most recent generations, respectively.
Unless the same as above, the priors are as follows:
b4,5/differenceN (1, 1),
11:1480/differenceN (0,s),
and s/differenceE(1) :
4. Results
Parameter estimates are presented as posterior medians and
the 95% central posterior interval. For full estimates of the fre-
quency of the social learning allele, the accuracy of social
learners, the rate of contrariness and the rate of conditionalsocial learning for each condition and each generation after
environmental change, see the electronic supplementary
material. Due to the sheer number of possible contrastsbetween conditions and/or generations (minimally 1075,
maximally 14 535), such contrasts are not provided, but
they can be readily computed from the model output.(a) Gene frequency
The frequency of the social learning allele dropped markedlyafter environmental change in all conditions (figure 2 a). The
drop was smallest with poor social information (dropping by
0.16, [0.14, 0.18] over two generations following environmentalchange), but bigger with moderate or rich social information
(dropping by 0.46, [0.44, 0.47] and 0.46, [0.45, 0.48], respect-
ively). Moreover, rich social information slowed the recoveryof the social learning allele following change; with moderate
social information there was a marked recovery from two to
three generations after change (increasing by 0.07, [0.05,0.09]), but not so in with rich social information ( −0.01,
[−0.03, 0.01]). With rich social information, the social learning
allele began to spread between the third and fourth generationsafter change (0.08, [0.06, 0.1]).
The magnitude of the drop was lower when critical social
learning was permitted, both with critical-moderate (0.34,[0.32, 0.36]) and critical-rich (0.36, [0.34, 0.38]) social infor-
mation. However, critical social learning did not prevent
the delay in the recovery of the social learning allele withrich social information (change between 2 and 3 generations
after change with critical-rich information: −0.02, [ −0.04, 0]).
Once the environment had been stable for an extended
period (i.e. 10 generations after change), the social allele
was less prevalent with poor social information (0.67,
[0.65, 0.69]) than in any other condition (moderate: 0.74,(a)( b)( c)
(d)( e)( f)024680.20.30.40.50.60.7
generations since environmental change generations since environmental change generations since environmental changefrequency of social learning alleleC1: poor
C2: moderate
C3: rich
C4: moderate
       + critical
C5: rich + critical
024680.20.40.60.81.0accuracy of social learnersC3: rich
C4: moderate
       + critical
C5: rich + criticalC1: poor
C2: moderate
0246800.050.100.15probability of critical social learning
0246800.050.100.150.200.250.30
generations since environmental changeprobability of being contraryC3: rich
C4: moderate
       + critical
C5: rich + criticalC1: poorC2: moderate
0 0.2 0.4 0.6 0.8 1.0
frequency of blue 0 0.2 0.4 0.6 0.8 1.000.20.40.60.81.0
frequency of blue in prior generation probability of choosing blue
00.20.40.60.81.0probability of choosing blue
Figure 2. The evolution of social learning in a changing environment. ( a–d) Intervals show the 50%, 80%, 90% and 95% credible intervals. Shaded regions link
the 95% credible intervals to aid reading. ( a) The social learning allele became less frequent following environmental change. This drop was exacerbated by the
richness of social information, but attenuated by the option of gathering asocial information. ( b) The accuracy of social learners falls immediately after environmental
change, but recovers thereafter. Again, this drop was exacerbated by the richness of social information, but attenuated by the option of gathering as ocial infor-
mation. ( c) Where critical social learning was permitted, social learners were more likely to gather asocial information shortly after environmental change i n response
to lowered consensus. ( d) Contrariness was overall quite low, but with rich social information it peaked two generations after environmental change as participants
adopted up-and-coming traits. A much smaller peak is present with moderate social information. ( e,f) The red line and shaded area (barely wider than the line)
shows the expected behaviour of the average participant with the 95% credible interval. The grey lines show the median estimate for each individual pa rticipant.
With both moderate ( e) and rich ( f) social information the typical response is highly conformist, although there is great inter-individual variation. ( f) specifically
shows the response to the most recent generation of participants. The response to earlier generations was weaker, particularly the second most recen t generation.
(Online version in colour.)royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 289: 202216146
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[0.72, 0.76], rich: 0.73, [0.71, 0.75], critical-moderate: 0.74,
[0.72, 0.76], critical-rich: 0.74, [0.72, 0.76]).
(b) Social accuracy
In all conditions, the accuracy of social learners was at a mini-mum immediately after environmental change (figure 2 b).
After such change, social learners fared best with poor infor-
mation (accuracy of 0.29, [0.27, 0.3]), and worst withmoderate or rich social information (0.11, [0.1, 0.12] and 0.11,
[0.1, 0.13], respectively). Critical social learning improved per-
formance after environmental change, but not to the level seenwith poor information (critical-moderate: 0.19, [0.18, 0.21],
critical-rich: 0.21, [0.2, 0.23]).
Subsequently, performance increased in all conditions.
With poor social information it increased rapidly at first,
but soon after slowed (increasing by 0.22, [0.19, 0.24] between
0 and 1 generations after change, but by only 0.07, [0.05, 0.1]two generations later). With moderate and rich social infor-
mation, the increase was very small in the first generation
(0.07, [0.05, 0.09] and 0.04, [0.02, 0.06], respectively), butaccelerated thereafter (0.5, [0.47, 0.53] and 0.16, [0.13, 0.19],
respectively, in the next generation), although note that the
rise in accuracy was slower with rich social informationthan with moderate social information.
Critical social learning increased both minimum accuracy
(critical-moderate: 0.19, [0.18, 0.21], critical-rich: 0.21, [0.2,0.23]) and the rate at which accuracy increased (increasing by
0.25, [0.23, 0.28] and 0.13, [0.11, 0.16] by the first generation
after change with critical-moderate and critical-rich infor-mation, respectively), greatly reducing the initial delay.
However, as with rich information, accuracy with critical-rich
information was slower to recover than with critical-moderatesocial information.
Long after environmental change, the accuracy of social
learners was much lower with poor information than inany other condition, and critical social learning increased
accuracy further (poor: 0.7, [0.68, 0.72], moderate: 0.9, [0.89,
0.91], rich: 0.89, [0.88, 0.91], critical-moderate: 0.93, [0.92,0.94], critical-rich: 0.93, [0.92, 0.94]). While social accuracy
was stable five generations after change in most conditions,
it slowly declined with poor information (0.75, [0.73, 0.77]five generations after change, versus 0.7, [0.68, 0.72] nine
generations after change).
(c) Critical social learning
The use of critical social learning (paying to receive asocial
information in addition to social information) was generallylow, but spiked shortly after environmental change
(figure 2 c). In the generations first experiencing a novel
environment, critical social learning was quite rare (critical-moderate: 0.01, [0.01, 0.02], critical-rich: 0.02, [0.02, 0.02]), but
it increased rapidly thereafter. With critical-moderate infor-
mation critical social learning peaked one generation afterenvironmental change (0.15, [0.13, 0.17]), returning to baseline
levels two generations later. With critical-rich information,
critical social learning grew more slowly , peaking in thesecond and third generations after environmental change
(0.11, [0.09, 0.13] and 0.11, [0.1, 0.14], respectively), returning
to baseline levels three generations later. Thus, the patternwas similar with both kinds of information, but the spike
was slower (and longer-lasting) with critical-rich information
than with critical-moderate information.There was considerable individual variation in critical
social learning (participant standard deviation: 3.26, [3.07,
3.47]). While 40% of participants never engaged in critical
social learning, 10% of participants did so on the majority oftrials, and the remaining 50% did so on a minority of trials.
This skewed distribution causes an apparent mismatch
between model estimates and raw-data averages: the modelestimates the rate of critical social learning one generation
after change and with critical-moderate information to be
approximately 0.15, while the proportion of trials on whichcritical social learning occurred is approximately 0.25. This is
because the model estimates are based on the behaviour of
the typical participant (typical meaning ‘average on the logit
scale ’), while the raw-data average pools data from all partici-
pants and does not use the logit scale. As such, the raw-data
average is more greatly affected by the minority of participantswho engage heavily in critical social learning than is the model
estimate. Nonetheless, the general pattern of a spike in critical
social learning following environmental change is present inboth the raw-data and model predictions.
(d) Contrariness
Contrariness (doing the opposite of what the social infor-mation suggests, assuming critical social learning is not
used, see definition above) was somewhat unusual, but far
from rare (figure 2 d). With poor information, contrariness
was steady over time, with model estimates for the rate of
contrariness ranging from a low of 0.04, [0.03, 0.06] (one gen-
eration after change) to 0.07, [0.05, 0.1] (eight generationsafter change). With moderate or rich information, model esti-
mates suggest baseline contrariness is similar (0.04, [0.03,
0.04] and 0.04, [0.03, 0.05], respectively, nine generationsafter change), although it was lower with critical-moderate
and critical-rich information (0.02, [0.02, 0.02] and 0.02,
[0.02, 0.03], respectively, nine generations after change).
With rich and critical-rich information there was a large
spike in contrariness peaking two generations after environ-
mental change (reaching 0.22, [0.19, 0.25] and 0.23, [0.2, 0.27],respectively). With critical-rich information the spike was
short-lived, and contrariness returned to baseline levels
two generations later. With rich information, the spike waslonger-lasting, and baseline levels were not regained until
three generations later. Similar, but much smaller contrariness
spikes are seen with moderate and critical-moderate infor-mation, with contrariness peaking one generation after change
(0.07, [0.05, 0.08] and 0.04, [0.03, 0.04]). While these spikes are
small, the statistical evidence for them is strong (i.e. 95% centralposterior intervals for the difference in contrariness between 0
and 1 generations after change exclude 0).
There was considerable individual variation in contrari-
ness (participant standard deviation: 2.00, [1.92, 2.07]).
Around 40% of participants were never contrary; however,
a minority of around 5% were contrary more often thannot, while the remaining 45% of participants were contrary
on a minority of trials. Again, this skewed distribution
causes a mismatch between model estimates (which corre-spond to an average participant on the logit scale) and raw-
data averages (which average across participants, not on the
logit scale) wherein the model ’s estimates are lower (e.g.
the raw average rate of contrariness with poor information
is 0.14, above the model estimate of approx. 0.05). Thus,
actual rates of contrariness are higher than the modelroyalsocietypublishing.org/journal/rspb Proc. R. Soc. B 289: 202216147
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output might suggest, but the model nonetheless captures
generational dynamics accurately.
(e) Conformist transmission
With moderate social information, participants showedno prior preference for choosing blue or yellow (
b1: 0.01,
[−0.03, 0.05]), but a great deal of social influence ( b2: 7.9 [7.5,
8.4]). Moreover, the response to consensus was highly confor-mist (
b3: 3.6, [3.0, 4.3]; figure 2 e). There was a great deal of
individual variation (participant standard deviation: 5.1 [4.7,
5.5]) with most participants being highly conformist, but aminority showing little social influence or being contrary.
With rich social information, the pattern was similar: Par-
ticipants showed a minor preference for choosing yellow asopposed to blue (
b1:−0.05, [ −0.09,−0.01]), but a great deal
of social influence ( b2: 3.7 [3.3, 4.0]). Moreover, the response
to consensus was highly conformist ( b3: 2.3, [1.7, 3.2];
figure 2 f). Relative to social information from the most
recent prior generation, social information from two gener-
ations ago had a much smaller influence ( b4: 0.20 [0.09,
0.33]), while information from three generations ago fell
between the two ( b5: 0.83 [0.76, 0.90]). As with moderate
social information, there was a great deal of individual vari-ation (participant standard deviation: 0.66 [0.63, 0.70]), with
most participants being highly conformist, but a minority
showing little social influence or even being highly contrary.
5. Discussion
We have presented results from a series of experimental evol-
utionary simulations in which a social learning allele invaded
an asocial population in a changing environment. Across fivedifferent forms of social information, we find that environ-
mental change causes the social learning allele to drop in
frequency and decreases the accuracy of social learners, butalso leads to spikes of critical social learning and contrariness.
Perhaps counterintuitively, the drop in the social learning
allele is smallest with poor social information. In part thisreflects the inferior performance of social learning with
poor information during periods of environmental stability:
this low performance means that the social learning alleledoes not spread as far as in the other conditions and the
increased prevalence of asocial learning makes the population
more adaptable to change. The result that moderate and richsocial information exacerbate the negative effects of environ-
mental change fits with other work that found conformist
transmission can allow social learning to spread to such adegree that populations go extinct following environmental
change [33].
Another cross-condition result is that rich social infor-
mation, containing the decisions of individuals from the
prior three generations, creates a lag in the evolutionary
dynamics. The drop in the frequency of the social allele, thedrop in the accuracy of the social learners and the spike in
critical social learning all last longer with rich information
than with moderate information (which was drawn fromonly the single most recent prior generation). This result
can be imagined as multi-generational social information
creating cultural inertia that prevents rapid cultural adap-tation following environmental change. Indeed, the analysis
of conformist transmission with rich information found that
participants weighted information from three generationsago almost as heavily as information from the most recent
prior generation. The possibility of cultural inertia has impli-
cations for the evolution of the post-reproductive phase of
human life history [9], and the role of post-reproductive indi-viduals in conserving and transmitting information [43].
While it may well be true that post-reproductive individuals
can recall and share valuable information with other individ-uals, unless this information is readily updated it may in fact
increase the risk posed by environmental change.
A final general result is that critical social learning [26] is
broadly beneficial: despite the cost it brings, it nonetheless
reduces the drop in the social learning allele and improves
decision-making accuracy. Thus, our results support theoryarguing for critical social learning as an adaptive decision-
making mechanism, although the adaptive value of critical
social learning may decrease if asocial information collectionis prohibitively costly.
Concerning the extent to which human social learning is
adapted to a changing environment, we find signs of this,but also clear indications of adaptations for high-fidelity
transmission that exacerbate the problems caused by environ-
mental change. The largest indication of such high-fidelitytransmission is conformism. As noted above, conformist
transmission can be so problematic following environmental
change that populations can collapse [33]. Similar problemshave been noted elsewhere [44]. One possible solution to
this comes from theory suggesting that an adaptive confor-
mist tendency is likely to be weak, such that rare-but-adaptive traits are not prevented from spreading [45,46].
However, this is not what we find: despite being aware of
the possibility of environmental change, the conformist pat-tern in participants ’decision-making was strong and led to
severe declines in the frequency of the social learning allele.
Alongside these signs of high-fidelity transmission, how-
ever, were clear indications of attempts to account for
environmental change. For instance, when participants were
permitted to engage in critical social learning, they weremuch more likely to do so immediately following environ-
mental change that during periods of stability. This is
adaptive assuming that asocial learning is costly, and soshould be used only when necessary, for instance, when
culture is misleading due to environmental change. In this
experiment, the only cue of environmental change was theconsensus among demonstrators, and so participants must
have noted the decrease in consensus, taken this as an
indication of environmental change and responded bygathering their own information.
Similar patterns are seen in rates of contrariness, which
spiked after change, dramatically so with rich or critical-richinformation. Again, such contrariness is adaptive in cases
where culture is misleading, and, if timed right, is doubly
adaptive because it avoids the costs of asocial informationcollection. As with critical social learning, participants must
have used consensus to guide their contrariness. With moder-
ate, or critical-moderate, information, a small minority ofparticipants responded to decreased consensus by gambling
on the idea that the environment had recently changed and
siding with the minority. With rich, and critical-rich, infor-mation participants could additionally observe the change
in consensus over time. As the minority trait began to spread
following change, a larger group of participants picked upon this and adopted the up-and-coming trait. Such behaviour
has been noted elsewhere [29] and facilitated culturalroyalsocietypublishing.org/journal/rspb Proc. R. Soc. B 289: 202216148
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adaptation in our experiments. Nonetheless, such measures
only partially offset the negative effects of environmental
change, an observation that highlights the precariousness of
humanity in the Anthropocene: we inhabit a culturally con-structed world that developed across the remarkable
climactic stability of the Holocene, and its fate is unclear
should instability (anthropogenic or otherwise) return.
Contrariness more generally buffered culture against the
effects of environmental change. For instance, even with poor
social information where contrariness cannot be strategicallytimed, participants showed unexpectedly high rates of contra-
riness (14% of trials, theory typically assumes individuals are
never contrary). This contrariness effectively created a reservoirof cultural exaptations that benefited the population following
environmental change. Despite these benefits, the evolutionary
basis for this behaviour is unclear, because the contrariansthemselves paid a cost to do so (by going against social infor-
mation that is typically accurate). Given the group-beneficial
aspect of this behaviour, it is possible that it is an example ofhuman prosociality [3,47 –49], although it is not clear why the
burden of such altruism is distributed so unevenly. Alterna-
tively , such behaviour may in fact be individually beneficialby helping the contrarian stand out and attract a following
[50]. Contrariness, or a more general disregard for social infor-
mation, has repeatedly been observed in experimental studiesof human social learning, and has typically been attributed
to participants not trusting the experiment or not being suffi-
cient motivated to maximize their accuracy [34,39,49,51].However, by highlighting the potential value of contrariness,
this work suggests that it should be considered as a possible
group-level adaptation. Moreover, even if it was not a targetof selection, our work suggests it nonetheless has beneficial
effects at the population level, and impacts cultural and
genetic evolution.
One unexpected result was the scale of individual variation
in social information use, contrariness and critical social learn-
ing. Many studies of social learning have documentedindividual variation in sensitivity to social information
[34,52]. However, along with other similar results [39], this
work suggests the scale of the variation is such that it spansqualitatively different behaviours. Most individuals follow
the social information, but a minority are broadly indifferent
to the decisions of others, while an even smaller groupare deliberately contrary. Similarly , most participants occasion-
ally use critical social learning, while a sizable minority
never do so, and an even smaller minority do so most of thetime. Consideration of the origin of variation on this scale
raises further questions about the sensitivity of human social
learning to contextual variables, individual states, culturalnorms and developmental factors [53 –56], which in turn
questions the nature of what has evolved to facilitate human
culture [57].
While this work suggests considerable limitations in the
ability of human groups to culturally adapt to unstable
environments, as with all experiments, our experimental set-ting was impoverished relative to the real-world. In
particular, we excluded forms of social learning that may
have further helped the population adapt, including forinstance payoff-biased transmission, which has been shown
to contribute to complex cultural adaptations both in theory
and experimental settings [5,58 –61]. Thus, the expansion of
this work to include payoff-biased transmission would be a
valuable step. However, in many cases, information aboutpayoffs may be opaque, noisy or unavailable until long
after a decision is made, meaning payoff-biased transmission
may not always be effective and, in some cases, may not be
possible. Another alternative not included in this experimentis prestige-biased transmission [62], which accounts for the
difficulty of perceiving payoffs by having individuals copy
those who are generally successful, and/or deferred to bythird parties, under the assumption that these are proxies
for payoffs. Such behaviour has been documented exper-
imentally [63 –65] although ethnographic evidence is more
mixed [66 –68]. Nonetheless, allowing the possibility of pres-
tige-biased transmission would be a valuable extension of
this work.
One final factor missing in this work is the possibility
of cumulative cultural evolution. In our experiment, popu-
lations adapted by switching between different options,with no possibility of refining the option to further increase
payoffs. Such cumulative cultural evolution is clearly an
important factor in the adaptive value of culture [25,69]and so its omission here is potentially important. Tasks
open to cumulative improvement have been employed in
largescale online experiments [61] although not yet in achanging environment.
The problem of adapting to a changing environment
highlights two opposing functions of culture. One is to usecollective decision-making to flexibly switch between options
as the local environment demands. The other is to pass on
valuable information from prior generations during periodsof stability between bouts of environmental change. Such
information may also be beyond the scope of any one individ-
ual, with high-fidelity transmission thus being critical tocumulative cultural change [70 –72]. These two purposes
are, seemingly, competing; the greater the fidelity of trans-
mission, due to processes like conformist transmission, theharder it is for populations to keep up with environmental
changes. Nonetheless, this work suggests human psychology
represents a compromise between the two: transmission istypically conformist and high-fidelity, but there is also a con-
siderable deal of contrariness, critical social learning and the
adoption of up-and-coming traits all of which support sociallearning in a changing environment. The challenges in balan-
cing these two needs, and in maintaining the adaptive value
of culture in a changing environment, may explain whyextensive cultural adaptation is unique to our species. Further
work will provide a more comprehensive understanding of
how human psychology meets the competing needs offidelity and flexibility.
Ethics.
Recruitment and testing were approved by the Committee for
Protection of Human Subjects at University of California, Berkeley
(protocol ID 2015-12-8227).
Data accessibility. Data and analysis code is available at https://osf.io/
krux5/.
Supplementary results are provided in the electronic supplemen-
tary material [73].
Authors ’contributions. T.J.H.M.: conceptualization, data curation, formal
analysis, funding acquisition, investigation, methodology, software,
validation, visualization, writing —original draft, writing —review
and editing; J.W.S.: investigation, methodology, software, writing —
review and editing; T.L.G.: conceptualization, funding acquisition,
project administration, resources, supervision, writing —review
and editing.
All authors gave final approval for publication and agreed to be
held accountable for the work performed therein.
Conflict of interest declaration. We declare we have no competing interests.royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 289: 202216149
 Downloaded from https://royalsocietypublishing.org/ on 10 November 2022

Funding. This work was funded by NSF grants 1456709 and 1408652.
Acknowledgements. We thank J. Hamrick, M. Pacer, S. Meylan, C. Ewing
and A. Mitchell for their assistance in designing the experimental
software, and R. Boyd, F. Reali and C. Cross for advice on the
manuscript.Endnote
1The data collected in these periods were originally intended to be
analysed as three distinct experiments, but because they were not
designed in light of each other ’s results we present the analyses
together.
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