Person:

Nunn, Charles

Social transmission of behavior can be realized through distinct mechanisms. Research on primate social learning typically distinguishes two forms of information that a learner can extract from a demonstrator: copying actions (defined as imitation) or copying only the consequential results (defined as emulation). We propose a decomposition of these learning mechanisms (plus pure individual learning) that incorporates the core idea that social learning can be represented as a search for an optimal behavior that is constrained by different kinds of information. We illustrate our approach with an individual-based model in which individuals solve tasks in abstract “spaces” that represent behavioral actions, results, and benefits of those results. Depending on the learning mechanisms at their disposal, individuals have differential access to the information conveyed in these spaces. We show how different classes of tasks may provide distinct advantages to individuals with different learning mechanisms and discuss how our approach contributes to current empirical and theoretical research on social learning and culture.

Experiments in captivity have provided evidence for social learning, but it remains challenging to demon- strate social learning in the wild. Recently, we developed network-based diffusion analysis (NBDA; 2009) as a new approach to inferring social learning from observational data. NBDA fits alternative models of asocial and social learning to the diffusion of a behavior through time, where the potential for social learning is related to a social network. Here, we investigate the performance of NBDA in relation to variation in group size, network heterogeneity, observer sampling errors, and duration of trait diffusion. We find that observation errors, when severe enough, can lead to increased Type I error rates in detecting social learning. However, elevated Type I error rates can be prevented by coding the observed times of trait acquisition into larger time units. Collectively, our results provide further guidance to applying NBDA and demonstrate that the method is more robust to sampling error than initially expected. Supplemental materials for this article may be downloaded from http:// lb.psychonomic-journals.org/content/supplemental.

Cultural traits spread via multiple mechanisms among individuals within social groups, including via transmission biases that occur when subordinates copy from dominants (prestige transmission), or via common cultural trait variants that are favoured over rare ones (consensus transmission). Most animal populations are subdivided into social groups where cultural learning occurs, yet theoretical studies of cultural trait transmission have tended to focus on within-group transmission dynamics. We developed an agent-based model of cultural transmission in socially structured populations in which a trait arises in one individual and either persists until a stable population equilibrium is reached, or goes extinct. With this model, we systematically varied group size, rates of dispersal among groups, mortality rates, transmission characteristics, the benefit of the cultural trait (including possibly negative benefits), and whether individuals disperse locally or randomly. We used generalized linear models to examine how changes in these parameters influence trait extinction, equilibrium prevalence and time to equilibrium. Four traits increased the probability of extinction: smaller group size, higher background mortality, lower transmission rate and more costly traits (although costly traits sometimes reached an equilibrium). Local dispersal and biased transmission mechanisms (prestige and consensus) had no significant effects on extinction probability, and similar patterns were found for equilibrium prevalence. We found that a lower dispersal rate and local dispersal slowed the time required for a trait to reach equilibrium, as did smaller groups, lower transmission rates and lower costs. Collectively, these analyses reveal that prestige and consensus transmission have weaker effects than other factors associated with demographic and social conditions.

Culture is widely thought to be beneficial when social learning is less costly than individual learning and thus may explain the enormous ecological success of humans. Rogers (1988. Does biology constrain culture. Am. Anthropol. 90: 819–831) contradicted this common view by showing that the evolution of social learning does not necessarily increase the net benefits of learned behaviours in a variable environment. Using simulation experiments, we re-analysed extensions of Rogers' model after relaxing the assumption that genetic evolution is much slower than cultural evolution. Our results show that this assumption is crucial for Rogers' finding. For many parameter settings, genetic and cultural evolution occur on the same time scale, and feedback effects between genetic and cultural dynamics increase the net benefits. Thus, by avoiding the costs of individual learning, social learning can increase ecological success. Furthermore, we found that rapid evolution can limit the evolution of complex social learning strategies, which have been proposed to be widespread in animals.

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyzes the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison to diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.