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Longstanding theory predicts that strategic flexibility in when and how to use social information can help individuals make adaptive decisions, especially when environments are temporally or spatially variable. A short-term increase in reliance on social information under these conditions has been experimentally shown in primates, including humans, but whether this occurs in other taxa is unknown. We asked whether migration between spatially variable environments affected social information use with a large-scale cultural diffusion experiment with wild great tits (Parus major) in captivity, a small passerine bird that can socially learn novel behaviors. We simulated an immigration event where knowledgeable birds were exchanged between groups with opposing preferences for a socially learned foraging puzzle, living in similar or different environments. We found evidence that both immigrants and residents were influenced by social information and attended to the rewards that others received. Our analysis supported the use of a payoff-biased social learning by immigrants when both resources and habitat features were spatially variable. In contrast, immigrants relied more-so on individual learning when payoffs or the environment were unchanged. In summary, our results suggest that great tits assess the payoffs others receive and are more influenced by socially observed differences in payoffs when environmental cues differ in their new environment. Our results provide experimental support for the hypothesis that spatial variability is a strong driver for the evolution of social learning strategies. Theory predicts that strategic flexibility in using social information can help individuals make adaptive decisions. A large-scale cultural diffusion experiment reveals that great tits (Parus major) use a payoff-biased social learning strategy when immigrating into an environment with different environmental cues and rewards. [ABSTRACT FROM AUTHOR]

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social–spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but—importantly—the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.