Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies

Decoding the Dynamics of Cultural Change: A Cultural Evolution Approach to the Psychology of Acculturation

PUBLIC ABSTRACT

Acculturation describes the cultural and psychological changes resulting from intercultural contact. Here, we use concepts from "cultural evolution" to better understand the processes of acculturation. Cultural evolution researchers view cultural change as an evolutionary process, allowing them to borrow tools and methods from biology. Cultural evolutionary mechanisms such as conformity (copying the numerical majority), anti-conformity (copying the numerical minority), prestige bias (copying famous individuals), payoff bias (copying successful people), and vertical cultural transmission (copying your parents) can cause people to adopt elements from other cultures and/or conserve their cultural heritage. We explore how these transmission mechanisms might create distinct acculturation strategies, shaping cultural change and diversity over the long-term. This theoretical integration can pave the way for a more sophisticated understanding of the pervasive cultural shifts occurring in many ethnically diverse societies, notably by identifying conditions that empower minority-group members, often marginalized, to significantly influence the majority group and society.

Revealing Decision-Making Strategies of Americans in Taking COVID-19 Vaccination

Efficient coverage for newly developed vaccines requires knowing which groups of individuals will accept the vaccine immediately and which will take longer to accept or never accept. Of those who may eventually accept the vaccine, there are two main types: success-based learners, basing their decisions on others' satisfaction, and myopic rationalists, attending to their own immediate perceived benefit. We used COVID-19 vaccination data to fit a mechanistic model capturing the distinct effects of the two types on the vaccination progress. We proved the identifiability of the population proportions of each type and estimated that 47 % of Americans behaved as myopic rationalists with a high variation across the jurisdictions, from 31 % in Mississippi to 76 % in Vermont. The proportion was correlated with the vaccination coverage, proportion of votes in favor of Democrats in 2020 presidential election, and education score.

Formalising prestige bias: Differences between models with first-order and second-order cues

Knowledge and behaviour are transmitted from one individual to another through social learning and eventually disseminated across the population. People often learn useful behaviours socially through selective bias rather than random selection of targets. Prestige bias, or the tendency to selectively imitate prestigious individuals, has been considered an important factor in influencing human behaviour. Although its importance in human society and culture has been recognised, the formulation of prestige bias is less developed than that of other social learning biases. To examine the effects of prestige bias on cultural evolution theoretically, it is imperative to formulate prestige and investigate its basic properties. We reviewed two definitions: one based on first-order cues, such as the demonstrator's appearance and job title, and the other based on second-order cues, such as people's behaviour towards the demonstrator (e.g. people increasingly pay attention to prestigious individuals). This study builds a computational model of prestige bias based on these two definitions and compares the cultural evolutionary dynamics they generate. Our models demonstrate the importance of distinguishing between the two types of formalisation, because they can have different influences on cultural evolution.

Kurvers RHJ. A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

Disentangling the contribution of individual and social learning processes in human advice-taking behavior

The study of social learning examines how individuals learn from others by means of observation, imitation, or compliance with advice. However, it still remains largely unknown whether social learning processes have a distinct contribution to behavior, independent from non-social trial-and-error learning that often occurs simultaneously. 153 participants completed a reinforcement learning task, where they were asked to make choices to gain rewards. Advice from an artificial teacher was presented in 60% of the trials, allowing us to compare choice behavior with and without advice. Results showed a strong and reliable tendency to follow advice (test-retest reliability ~0.73). Computational modeling suggested a unique contribution of three distinct learning strategies: (a) individual learning (i.e., learning the value of actions, independent of advice), (b) informed advice-taking (i.e., learning the value of following advice), and (c) non-informed advice-taking (i.e., a constant bias to follow advice regardless of outcome history). Comparing artificial and empirical data provided specific behavioral regression signatures to both informed and non-informed advice taking processes. We discuss the theoretical implications of integrating internal and external information during the learning process.

Selection and adaptation in human migration

This article reviews the ways migration shapes human biology. This includes the physiological and genetic, but also socio-cultural aspects such as organization, behavior, and culture. Across disciplines I highlight the multiple levels of cultural and genetic selection whereby individuals and groups adapt to pressures along a migration timeline: the origin, transit, and destination. Generally, the evidence suggests that selective pressures and adaptations occur at the individual, family, and community levels. Consequently, across levels there are negotiations, interactions, and feedbacks that shape migration outcomes and the trajectory of evolutionary change. The rise and persistence of migration-relevant adaptations emerges as a central question, including the maintenance of cumulative culture adaptations, the persistence of "cultures of migration," as well as the individual-level physiological and cognitive adaptations applied to successful transit and settlement in novel environments.

Flexible social inference facilitates targeted social learning when rewards are not observable

Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behaviour. We compared our social inference model against simpler heuristics in three studies of human behaviour in a collective-sensing task. Experiment 1 demonstrated that average performance improved as a function of group size at a rate greater than predicted by heuristic models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behaviour.

Conditions that favour cumulative cultural evolution

The emergence of human societies with complex language and cumulative culture is considered a major evolutionary transition. Why such a high degree of cumulative culture is unique to humans is perplexing given the potential fitness advantages of cultural accumulation. Here, Boyd & Richerson's (1996 Why culture is common, but cultural evolution is rare. Proc. Br. Acad. 88, 77-93) discrete-cultural-trait model is extended to incorporate arbitrarily strong selection; conformist, anti-conformist and unbiased frequency-dependent transmission; random and periodic environmental variation; finite population size; and multiple 'skill levels.' From their infinite-population-size model with success bias and a single skill level, Boyd and Richerson concluded that social learning is favoured over individual learning under a wider range of conditions when social learning is initially common than initially rare. We find that this holds only if the number n of individuals observed by a social learner is sufficiently small, but with a finite population and/or a combination of success-biased and conformist or unbiased transmission, this result holds with larger n. Assuming social learning has reached fixation, the increase in a population's mean skill level is lower if cumulative culture is initially absent than initially present, if population size is finite, or if cultural transmission has a frequency-dependent component. Hence, multiple barriers to cultural accumulation may explain its rarity. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Combining Conformist and Payoff Bias in Cultural Evolution : An Integrated Model for Human Decision-Making

Most research on transmission biases in cultural evolution has treated different biases as distinct strategies. Here I present a model that combines both frequency dependent bias (including conformist bias) and payoff bias in a single decision-making calculus and show that such an integrated learning strategy may be superior to relying on either bias alone. Natural selection may operate on humans' relative dependence on frequency and payoff information, but both are likely to contribute to the spread of variants with high payoffs. Importantly, the magnitude of conformist bias affects the evolutionary dynamics, and I show that an intermediate level of conformity may be most adaptive and may spontaneously evolve as it resists the invasion of low-payoff variants yet enables the fixation of high-payoff variants in the population.

Cultural diffusion dynamics depend on behavioural production rules

Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed or modelled interchangeably, yet to date no study has explored the consequences of their interaction for cultural diffusions. We present a generative model that integrates the two, and ask how variation in production rules might influence diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires may also change, and the acquisition of behaviour is conditioned on its frequency. We analyse the diffusion of a novel behaviour through social networks, yielding generalizable predictions of how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking acquisition and production might affect the performance of two commonly used inferential models for social learning; network-based diffusion analysis, and experience-weighted attraction models. We find that the influence that production rules have on diffusion dynamics has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.

Does social influence affect COVID-19 vaccination intention among the unvaccinated?

Conformist social influence is a double-edged sword when it comes to vaccine promotion. On the one hand, social influence may increase vaccine uptake by reassuring the hesitant about the safety and effectiveness of the vaccine; on the other hand, people may forgo the cost of vaccination when the majority is already vaccinated - giving rise to a public goods dilemma. Here, we examine whether available information on the percentage of double-vaccinated people affects COVID-19 vaccination intention among unvaccinated people in Turkey. In an online experiment, we divided participants (n = 1013) into low, intermediate and high social influence conditions, reflecting the government's vaccine promotion messages. We found that social influence did not predict COVID-19 vaccination intention, but psychological reactance and collectivism did. People with higher reactance (intolerance of others telling one what to do and being sceptical of consensus views) had lower vaccination intention, whilst people with higher collectivism (how much a person considers group benefits over individual success) had higher vaccination intention. Our findings suggest that advertising the percentage of double-vaccinated people is not sufficient to trigger a cascade of others getting themselves vaccinated. Diverse promotion strategies reflecting the heterogeneity of individual attitudes could be more effective.

Manipulating actions: a selective two‐option device for cognitive experiments in wild animals

Advances in biologging technologies have significantly improved our ability to track individual animals' behaviour in their natural environment. Beyond observations, automation of data collection has revolutionized cognitive experiments in the wild. For example, radio-frequency identification (RFID) antennae embedded in 'puzzle box' devices have allowed for large-scale cognitive experiments where individuals tagged with passive integrated transponder (PIT) tags interact with puzzle boxes to gain a food reward, with devices logging both the identity and solving action of visitors. Here, we extended the scope of wild cognitive experiments by developing a fully automated selective two-option foraging device to specifically control which actions lead to a food reward and which remain unrewarded. Selective devices were based on a sliding-door foraging puzzle, and built using commercially available low-cost electronics. We tested it on two free-ranging PIT-tagged subpopulations of great tits Parus major as a proof of concept. We conducted a diffusion experiment where birds learned from trained demonstrators to get a food reward by sliding the door either to the left or right. We then restricted access of knowledgeable birds to their less preferred side and calculated the latency until birds produced solutions as a measure of behavioural flexibility. A total of 22 of 23 knowledgeable birds produced at least one solution on their less preferred side after being restricted, with higher-frequency solvers being faster at doing so. In addition, 18 of the 23 birds reached their solving rate from prior to the restriction on their less preferred side, with birds with stronger prior side preference taking longer to do so. We therefore introduce and successfully test a new selective two-option puzzle box, providing detailed instructions and freely available software that allows reproducibility. It extends the functionality of existing systems by allowing fine-scale manipulations of individuals' actions and opens a large range of possibilities to study cognitive processes in wild animal populations.

Insights about the common generative rule underlying an information foraging task can be facilitated via collective search

Social learning is beneficial for efficient information search in unfamiliar environments ("within-task" learning). In the real world, however, possible search spaces are often so large that decision makers are incapable of covering all options, even if they pool their information collectively. One strategy to handle such overload is developing generalizable knowledge that extends to multiple related environments ("across-task" learning). However, it is unknown whether and how social information may facilitate such across-task learning. Here, we investigated participants' social learning processes across multiple laboratory foraging sessions in spatially correlated reward landscapes that were generated according to a common rule. The results showed that paired participants were able to improve efficiency in information search across sessions more than solo participants. Computational analysis of participants' choice-behaviors revealed that such improvement across sessions was related to better understanding of the common generative rule. Rule understanding was correlated within a pair, suggesting that social interaction is a key to the improvement of across-task learning.

Costly teaching contributes to the acquisition of spear hunting skill among BaYaka forager adolescents

Teaching likely evolved in humans to facilitate the faithful transmission of complex tasks. As the oldest evidenced hunting technology, spear hunting requires acquiring several complex physical and cognitive competencies. In this study, we used observational and interview data collected among BaYaka foragers (Republic of the Congo) to test the predictions that costlier teaching types would be observed at a greater frequency than less costly teaching in the domain of spear hunting and that teachers would calibrate their teaching to pupil skill level. To observe naturalistic teaching during spear hunting, we invited teacher-pupil groupings to spear hunt while wearing GoPro cameras. We analysed 68 h of footage totalling 519 teaching episodes. Most observed teaching events were costly. Direct instruction was the most frequently observed teaching type. Older pupils received less teaching and more opportunities to lead the spear hunt than their younger counterparts. Teachers did not appear to adjust their teaching to pupil experience, potentially because age was a more easily accessible heuristic for pupil skill than experience. Our study shows that costly teaching is frequently used to transmit complex tasks and that instruction may play a privileged role in the transmission of spear hunting knowledge.

Conformist social learning leads to self-organised prevention against adverse bias in risky decision making

Given the ubiquity of potentially adverse behavioural bias owing to myopic trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal collective behaviour. Here we show, through model analyses and large-scale interactive behavioural experiments with 585 human subjects, that conformist influence can indeed promote favourable risk taking in repeated experience-based decision making, even though many individuals are systematically biased towards adverse risk aversion. Although strong positive feedback conferred by copying the majority's behaviour could result in unfavourable informational cascades, our differential equation model of collective behavioural dynamics identified a key role for increasing exploration by negative feedback arising when a weak minority influence undermines the inherent behavioural bias. This 'collective behavioural rescue', emerging through coordination of positive and negative feedback, highlights a benefit of collective learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective behavioural flexibility under conformist influences.

What is the extent of a frequency-dependent social learning strategy space?

Models of frequency-dependent social learning posit that individuals respond to the commonality of behaviours without additional variables modifying this. Such strategies bring important trade-offs, e.g. conformity is beneficial when observing people facing the same task but harmful when observing those facing a different task. Instead of rigidly responding to frequencies, however, social learners might modulate their response given additional information. To see, we ran an incentivised experiment where participants played either a game against nature or a coordination game. There were three types of information: (a) choice frequencies in a group of demonstrators; (b) an indication of whether these demonstrators learned in a similar or different environment; and (c) an indication about the reliability of this similarity information. Similarity information was either reliably correct, uninformative or reliably incorrect, where reliably correct and reliably incorrect treatments provided participants with equivalent earning opportunities. Participants adjusted their decision-making to all three types of information. Adjustments, however, were asymmetric, with participants doing especially well when conforming to demonstrators who were reliably similar to them. The overall response, however, was more fluid and complex than this one case. This flexibility should attenuate the trade-offs commonly assumed to shape the evolution of frequency-dependent social learning strategies.

Effective population size for culturally evolving traits

Population size has long been considered an important driver of cultural diversity and complexity. Results from population genetics, however, demonstrate that in populations with complex demographic structure or mode of inheritance, it is not the census population size, N, but the effective size of a population, Ne, that determines important evolutionary parameters. Here, we examine the concept of effective population size for traits that evolve culturally, through processes of innovation and social learning. We use mathematical and computational modeling approaches to investigate how cultural Ne and levels of diversity depend on (1) the way traits are learned, (2) population connectedness, and (3) social network structure. We show that one-to-many and frequency-dependent transmission can temporally or permanently lower effective population size compared to census numbers. We caution that migration and cultural exchange can have counter-intuitive effects on Ne. Network density in random networks leaves Ne unchanged, scale-free networks tend to decrease and small-world networks tend to increase Ne compared to census numbers. For one-to-many transmission and different network structures, larger effective sizes are closely associated with higher cultural diversity. For connectedness, however, even small amounts of migration and cultural exchange result in high diversity independently of Ne. Extending previous work, our results highlight the importance of carefully defining effective population size for cultural systems and show that inferring Ne requires detailed knowledge about underlying cultural and demographic processes.

Examining acculturation in mixed-couples to test cultural transmission mechanisms

The project sought to understand the factors which underlie cultural transmission, adapting self-reported methods from cross-cultural psychology and sociology to test the external validity of several constructs from existing evolutionary models. The target population were native-foreigner mixed-couples, allowing the analyses to benefit from asymmetrical cultural inputs. Sampling took place in Italy and Portugal, with recruitment relying on social networks, online newspapers, friends, organizations, universities, parishes, and embassies. The questionnaire was personally delivered or filled online. The validated variables were: contact with a population in which the majority endorses the culture being acquired, the relative quantity of friends from that culture, the perceived relationship quality with the companion, affective ties with one's own family, and the desire and emotional components behind the culture-transmission motive (a concept similar to cultural conservatism). An unexpected strong, positive association between both cultural identities was found. Thus, it was suggested that these participants adopted an integrative orientation, allowing both cultural identities to blend, whereas most research so far focuses on assimilation scenarios. Overall, acculturation was driven by either conformity to the majority or random learning, without discarding the influence of preferred demonstrators, and the emotional bounds embedded in the individual's cultural identity. Acculturation proved to be flexible and potentially changing according to the cultural trait being examined.

Complex cognitive algorithms preserved by selective social learning in experimental populations

Many human abilities rely on cognitive algorithms discovered by previous generations. Cultural accumulation of innovative algorithms is hard to explain because complex concepts are difficult to pass on. We found that selective social learning preserved rare discoveries of exceptional algorithms in a large experimental simulation of cultural evolution. Participants (N = 3450) faced a difficult sequential decision problem (sorting an unknown sequence of numbers) and transmitted solutions across 12 generations in 20 populations. Several known sorting algorithms were discovered. Complex algorithms persisted when participants could choose who to learn from but frequently became extinct in populations lacking this selection process, converging on highly transmissible lower-performance algorithms. These results provide experimental evidence for hypothesized links between sociality and cognitive function in humans.

Negative observational learning might play a limited role in the cultural evolution of technology

Theoretical and empirical studies of the cultural evolution in technology have often focused on positive observational learning, i.e., copying a successful individual. However, negative observational learning, i.e., avoiding negative or bad exemplar behavior, is ubiquitous in humans and other animals. In this paper, we experimentally investigate whether observing negative examples can assist in tool making in the virtual arrowhead task, which has been widely applied to test the theory of cultural evolution in the technological domain. We set three conditions that differ in the kinds of social learning available to participants: (1) positive observational learning, (2) negative observational learning, and (3) pure asocial learning. The results of the positive observational and pure asocial learning conditions replicated previous studies; i.e., participants in the positive observational learning condition outperformed those in the asocial learning condition. In contrast, opportunities to observe negative examples did not increase the performance compared to pure asocial learning. Computer simulations in the same setting showed that the presence of negative exemplars is in theory beneficial to participants, providing additional pieces of information on the relationship between arrowhead designs and their performance scores. These findings together suggest that negative observational learning might play only a limited role in the cultural evolution of technologies possibly due to a cognitive bias in humans toward copying.

A preference to learn from successful rather than common behaviours in human social dilemmas

Human cooperation is often claimed to be special and requiring explanations based on gene-culture coevolution favouring a desire to copy common social behaviours. If this is true, then individuals should be motivated to both observe and copy common social behaviours. Previous economic experiments, using the public goods game, have suggested individuals' desire to sacrifice for the common good and to copy common social behaviours. However, previous experiments have often not shown examples of success. Here we test, on 489 participants, whether individuals are more motivated to learn about, and more likely to copy, either common or successful behaviours. Using the same social dilemma and standard instructions, we find that individuals were primarily motivated to learn from successful rather than common behaviours. Consequently, social learning disfavoured costly cooperation, even when individuals could observe a stable, pro-social level of cooperation. Our results call into question explanations for human cooperation based on cultural evolution and/or a desire to conform with common social behaviours. Instead, our results indicate that participants were motivated by personal gain, but initially confused, despite receiving standard instructions. When individuals could learn from success, they learned to cooperate less, suggesting that human cooperation is maybe not so special after all.

Social Learning Strategies and Cooperative Behaviour: Evidence of Payoff Bias, but Not Prestige or Conformity, in a Social Dilemma Game

Human cooperation, occurring without reciprocation and between unrelated individuals in large populations, represents an evolutionary puzzle. One potential explanation is that cooperative behaviour may be transmitted between individuals via social learning. Using an online social dilemma experiment, we find evidence that participants’ contributions were more consistent with payoff-biased transmission than prestige-biased transmission or conformity. We also found some evidence for lower cooperation (i) when exposed to social information about peer cooperation levels than without such information, and (ii) in the prisoners’ dilemma game compared to the snowdrift game. A simulation model established that the observed cooperation was more likely to be caused by participants’ general propensity to cooperate than by the effect of social learning strategies employed within the experiment, but that this cooperative propensity could be reduced through selection. Overall, our results support previous experimental evidence indicating the role of payoff-biased transmission in explaining cooperative behaviour, but we find that this effect was small and was overwhelmed by participants’ general propensity for cooperation.

Maintenance of prior behaviour can enhance cultural selection

Many cultural phenomena evolve through a Darwinian process whereby adaptive variants are selected and spread at the expense of competing variants. While cultural evolutionary theory emphasises the importance of social learning to this process, experimental studies indicate that people's dominant response is to maintain their prior behaviour. In addition, while payoff-biased learning is crucial to Darwinian cultural evolution, learner behaviour is not always guided by variant payoffs. Here, we use agent-based modelling to investigate the role of maintenance in Darwinian cultural evolution. We vary the degree to which learner behaviour is payoff-biased (i.e., based on critical evaluation of variant payoffs), and compare three uncritical (non-payoff-biased) strategies that are used alongside payoff-biased learning: copying others, innovating new variants, and maintaining prior variants. In line with previous research, we show that some level of payoff-biased learning is crucial for populations to converge on adaptive cultural variants. Importantly, when combined with payoff-biased learning, uncritical maintenance leads to stronger population-level adaptation than uncritical copying or innovation, highlighting the importance of maintenance to cultural selection. This advantage of maintenance as a default learning strategy may help explain why it is a common human behaviour.

Prestige and content biases together shape the cultural transmission of narratives

Cultural transmission biases such as prestige are thought to have been a primary driver in shaping the dynamics of human cultural evolution. However, few empirical studies have measured the importance of prestige relative to other effects, such as content biases present within the information being transmitted. Here, we report the findings of an experimental transmission study designed to compare the simultaneous effects of a model using a high- or low-prestige regional accent with the presence of narrative content containing social, survival, emotional, moral, rational, or counterintuitive information in the form of a creation story. Results from multimodel inference reveal that prestige is a significant factor in determining the salience and recall of information, but that several content biases, specifically social, survival, negative emotional, and biological counterintuitive information, are significantly more influential. Further, we find evidence that reliance on prestige cues may serve as a conditional learning strategy when no content cues are available. Our results demonstrate that content biases serve a vital and underappreciated role in cultural transmission and cultural evolution. Social media summary: Storyteller and tale are both key to memorability, but some content is more important than the storyteller's prestige.

Processing of novel food reveals payoff and rank-biased social learning in a wild primate

Social learning—learning from others—is the basis for behavioural traditions. Different social learning strategies (SLS), where individuals biasedly learn behaviours based on their content or who demonstrates them, may increase an individual’s fitness and generate behavioural traditions. While SLS have been mostly studied in isolation, their interaction and the interplay between individual and social learning is less understood. We performed a field-based open diffusion experiment in a wild primate. We provided two groups of vervet monkeys with a novel food, unshelled peanuts, and documented how three different peanut opening techniques spread within the groups. We analysed data using hierarchical Bayesian dynamic learning models that explore the integration of multiple SLS with individual learning. We (1) report evidence of social learning compared to strictly individual learning, (2) show that vervets preferentially socially learn the technique that yields the highest observed payoff and (3) also bias attention toward individuals of higher rank. This shows that behavioural preferences can arise when individuals integrate social information about the efficiency of a behaviour alongside cues related to the rank of a demonstrator. When these preferences converge to the same behaviour in a group, they may result in stable behavioural traditions.

Spatially and temporally distributed data foraging decisions in disciplinary field science

How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study-and a complementary real-world case study-to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions.

Dynamic social learning in temporally and spatially variable environments

Cultural evolution is partly driven by the strategies individuals use to learn behaviour from others. Previous experiments on strategic learning let groups of participants engage in repeated rounds of a learning task and analysed how choices are affected by individual payoffs and the choices of group members. While groups in such experiments are fixed, natural populations are dynamic, characterized by overlapping generations, frequent migrations and different levels of experience. We present a preregistered laboratory experiment with 237 mostly German participants including migration, differences in expertise and both spatial and temporal variation in optimal behaviour. We used simulation and multi-level computational learning models including time-varying parameters to investigate adaptive time dynamics in learning. Confirming theoretical predictions, individuals relied more on (conformist) social learning after spatial compared with temporal changes. After both types of change, they biased decisions towards more experienced group members. While rates of social learning rapidly declined in rounds following migration, individuals remained conformist to group-typical behaviour. These learning dynamics can be explained as adaptive responses to different informational environments. Summarizing, we provide empirical insights and introduce modelling tools that hopefully can be applied to dynamic social learning in other systems.

Detecting and quantifying social transmission using network-based diffusion analysis

Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed nbda package written in the R programming language.

Evolutionary aspects that guide the cultural transmission pathways in a local medical system in Northeast Brazil

Most of the studies with a focus on pathways and biases of cultural transmission in different domains show that vertical transmission predominates over horizontal and oblique transmission, especially in traits linked to traditions and survival skills, such as local medicine. However, overestimation of the importance of vertical transmission has been an object of methodological criticism. Therefore, a statistical analysis with diachronic perspective may obtain more accurate results. The present study uses an eight-year time frame, as well as synchronous analysis, to study evolutionary aspects that guide the transmission pathways of a local medical system in northeast Brazil. We find that even with vertical transmission being predominant in the learning of information in this cultural domain, the evolutionary implications of this predominance may not be the same as that expected by the theory of cultural evolution. There is a substantial updating of knowledge through horizontal and oblique routes, guided primarily by a model-based bias on prestige and success, which is quite adaptive. Moreover, even when the information is passed vertically, the transmission is much more diffusive than conservative. Indeed, there is a small set of information that remains over time, known as a “structural core,” but new information is aggregated continuously, preparing the system to adapt to new events. By analyzing the transmission routes of knowledge about medicinal plants, this study presents a new perspective on the evolutionary implications of cultural transmission.

The human life history is adapted to exploit the adaptive advantages of culture

Humans evolved from an ape ancestor that was highly intelligent, moderately social and moderately dependent on cultural adaptations for subsistence technology (tools). By the late Pleistocene, humans had become highly dependent on culture for subsistence and for rules to organize a complex social life. Adaptation by cultural traditions transformed our life history, leading to an extended juvenile period to learn subsistence and social skills, post-reproductive survival to help conserve and transmit skills, a dependence on social support for mothers of large-brained, very dependent and nutrient-demanding offspring, males devoting substantial effort to provisioning rather than mating, and the cultivation of large social networks to tap pools in information unavailable to less social species. One measure of the success of the exploitation of culture is that the minimum inter-birth interval of humans is nearly half that of our ape relatives. Another measure is the wide geographical distribution of humans compared with other apes, based on subsistence systems adapted to fine-scale spatial environmental variation. An important macro-evolutionary question is why our big-brained, culture-intensive life-history strategy evolved so recently and in only our lineage. We suggest that increasing spatial and temporal variation in the Pleistocene favoured cultural adaptations. This article is part of the theme issue 'Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other animals'.

Individually irrational pruning is essential for ecological rationality in a social context

Heuristics, commonly thought to violate the full rationality assumptions, are paradoxically indispensable parts of our decision-making and learning processes. To resolve this seemingly paradox, there have been several studies in the literature that aim at finding some broad daily life conditions and situations where employing heuristics are rational. However, these researches mainly focus on non-social conditions, whereas, for human beings, social and individual processes are interwoven and it would be better to study them jointly. Here, we study the role of pruning heuristic in individual reinforcement learning in a social context, where our simulated learning agents make many of their decisions relying on others' knowledge. Our simulation results suggest that the seemingly irrational pruning heuristic leads to less cost in the social settings. That is, we have a meaningfully more social outcome in the presence of this heuristic in social contexts, and social learning helps the agents to learn better where the pruning heuristic is an obstacle in the way of finding the optimal solution in the individual setting. In sum, the synergy between the pruning behavior and social learning leads to ecological rationality.

Wild primates copy higher-ranked individuals in a social transmission experiment

Little is known about how multiple social learning strategies interact and how organisms integrate both individual and social information. Here we combine, in a wild primate, an open diffusion experiment with a modeling approach: Network-Based Diffusion Analysis using a dynamic observation network. The vervet monkeys we study were not provided with a trained model; instead they had access to eight foraging boxes that could be opened in either of two ways. We report that individuals socially learn the techniques they observe in others. After having learnt one option, individuals are 31x more likely to subsequently asocially learn the other option than individuals naïve to both options. We discover evidence of a rank transmission bias favoring learning from higher-ranked individuals, with no evidence for age, sex or kin bias. This fine-grained analysis highlights a rank transmission bias in a field experiment mimicking the diffusion of a behavioral innovation.

Cultural and evolutionary dynamics with best-of-k learning when payoffs are uncertain

Social learning not only takes the form of random copying of other individuals, but also involves learners' choice of what to learn or from whom to learn. Best-of-k learning refers to a kind of success-biased social learning strategy in which a learner randomly samples k exemplars from the population and imitates the most "successful" one, or the one gaining the highest payoff. While it is intuitive that best-of-k learning can promote the spread of superior variants and thereby enable cumulative cultural evolution, a previous mathematical analysis has shown that it may sometimes result in maladaptive cultural evolution when the payoffs associated with cultural variants vary stochastically. If so, best-of-k learners may be selectively disfavored and in the long run replaced by unbiased learners, who simply copy someone chosen at random. Here we develop new mathematical models that are more simplified and mathematically tractable than the previous model to achieve a fuller analysis of cultural and evolutionary dynamics involving best-of-k learning and stochastic payoffs. We find that best-of-k learning, unlike unbiased learning, can facilitate the invasion of an on average inferior variant that sometimes gives a very high payoff, destabilize a population fixed with a variant that is on average superior but occasionally results in a very low payoff, and maintain cultural polymorphism at equilibrium. Considering gene-culture coevolution of learning rules and cultural variants, under the assumption that social learning is always faithful, it is shown that a population of best-of-k learners at the culturally polymorphic state can always be invaded by unbiased learners and eventually converges to a culturally monomorphic state. Nonetheless, we show that best-of-k learning can be stable against invasion by unbiased learning if social learning is sometimes combined with individual learning.

Majority rules: how good are we at aggregating convergent opinions?

Mathematical models and simulations demonstrate the power of majority rules, i.e. following an opinion shared by a majority of group members. Majority opinion should be followed more when (a) the relative and absolute size of the majority grow, the members of the majority are (b) competent, and (c) benevolent, (d) the majority opinion conflicts less with our prior beliefs and (e) the members of the majority formed their opinions independently. We review the experimental literature bearing on these points. The few experiments bearing on (b) and (c) suggest that both factors are adequately taken into account. Many experiments show that (d) is also followed, with participants usually putting too much weight on their own opinion relative to that of the majority. Regarding factors (a) and (e), in contrast, the evidence is mixed: participants sometimes take into account optimally the absolute and relative size of the majority, as well as the presence of informational dependencies. In other circumstances, these factors are ignored. We suggest that an evolutionary framework can help make sense of these conflicting results by distinguishing between evolutionarily valid cues - that are readily taken into account - and non-evolutionarily valid cues - that are ignored by default.

Nahua mushroom gatherers use area-restricted search strategies that conform to marginal value theorem predictions

We develop a method of analysis for testing the marginal value theorem (MVT) in natural settings that does not require an independent definition or mapping of patches. We draw on recent theoretical work on area-restricted search (ARS) that links turning-angle and step-size changes to geographically localized encounter-rates. These models allow us to estimate "giving-up times" using encounter-annotated GPS tracking data. Applied to a case study of Nahua mushroom foragers, these models identify distinct forms of intrapatch and interpatch search behavior, with intrapatch search transitioning to interpatch search after a predictable interval of time since the last encounter with a harvested mushroom. Our empirical estimate of giving-up time coincides with the theoretically optimal giving-up time derived under the MVT in the same environment. The MVT is currently underused in studies of human foraging and settlement patterns, due in large part to the difficulty of identifying discrete resource patches and quantifying their characteristics. Our methods mitigate the need to make such discrete maps of patches and thus have the potential to broaden the scope for empirical evaluations of the MVT and related theory in humans. Beyond studies of naturalistic foraging in humans and other animals, our approach has implications for optimization of search behavior in a range of applied fields where search dynamics must be adapted to shifting patterns of environmental heterogeneity affecting prey density and patchiness.

Faithfulness-boost effect: Loyal teammate selection correlates with skill acquisition improvement in online games

The problem of skill acquisition is ubiquitous and fundamental to life. Most tasks in modern society involve the cooperation with other subjects. Notwithstanding its fundamental importance, teammate selection is commonly overlooked when studying learning. We exploit the virtually infinite repository of human behavior available in Internet to study a relevant topic in anthropological science: how grouping strategies may affect learning. We analyze the impact of team play strategies in skill acquisition using a turn-based game where players can participate individually or in teams. We unveil a subtle but strong effect in skill acquisition based on the way teams are formed and maintained during time. "Faithfulness-boost effect" provides a skill boost during the first games that would only be acquired after thousands of games. The tendency to play games in teams is associated with a long-run skill improvement while playing loyally with the same teammate significantly accelerates short-run skill acquisition.

Social learning strategies regulate the wisdom and madness of interactive crowds

Why groups of individuals sometimes exhibit collective 'wisdom' and other times maladaptive 'herding' is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity among individuals, with rates of copying increasing with group size, leading to high probabilities of herding among large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated 'wisdom of the crowd' effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a long-standing puzzle in the literature.

The computational basis of following advice in adolescents

Advice taking helps one to quickly acquire knowledge and make decisions. This age-comparative study (in children [8- to 10-year-olds], adolescents [13- to 15-year-olds], and adults [18- to 22-year-olds]) investigated developmental differences in how advice, experience, and exploration influence learning. The results showed that adolescents were initially easily swayed to follow peer advice but also switched more rapidly to exploring alternatives like children. Whereas adults stayed with the advice over the task, adolescents put more weight on their own experience compared with adults. A social learning model showed that although social influence most strongly affects adolescents' initial expectations (i.e., their priors), adolescents showed higher exploration and discovered the other good option in the current task. Thus, our model resolved the apparently conflicting findings of adolescents being more and less sensitive to peer influence and provides novel insights into the dynamic interaction between social and individual learning.