Social learning, where information is acquired from others, is taxonomically widespread. There is growing evidence that animals selectively employ ‘social learning strategies', which determine e.g. when to copy others instead of learning asocially and whom to copy. Furthermore, once animals have acquired new information, e.g. regarding profitable resources, it is beneficial for them to commit it to long-term memory (LTM), especially if it allows access to profitable resources in the future. Research into social learning strategies and LTM has covered a wide range of taxa. However, otters (subfamily Lutrinae), popular in zoos due to their social nature and playfulness, remained neglected until a recent study provided evidence of social learning in captive smooth-coated otters ( Lutrogale perspicillata ), but not in Asian short-clawed otters ( Aonyx cinereus ). We investigated Asian short-clawed otters' learning strategies and LTM performance in a foraging context. We presented novel extractive foraging tasks twice to captive family groups and used network-based diffusion analysis to provide evidence of a capacity for social learning and LTM in this species. A major cause of wild Asian short-clawed otter declines is prey scarcity. Furthering our understanding of how they learn about and remember novel food sources could inform key conservation strategies.

Selectively learning from specific types of individuals may be adaptive if demonstrator characteristics can be used to identify more beneficial sources of social information.Such "social learning biases" have been experimentally demonstrated in a number of species, but these experiments generally involve restricted laboratory conditions using a limited number of potential demonstrators and tend to consider only the characteristics of demonstrators rather than the importance of pairwise relationships on information transfer between individuals. In this study, we presented a novel foraging task to a large population of zebra finches (Taeniopygia guttata) housed in a free-flying aviary and used multinetwork Network-Based Diffusion Analysis (NBDA) to establish whether birds learned from individuals they shared particular relationships with. Specifically, we investigated whether task solves followed social learning pathways representing the following relationships between individuals: feeding associations, aggressive interactions, positive associations (e.g. grooming) and mating pairs. We found strong evidence that zebra finches learn from their aggressors, irrespective of the outcome of that aggressive encounter. This has been previously suggested in laboratory-based studies on zebra finches, but never conclusively documented in a freely interacting population. We also found some weaker evidence to suggest that zebra finches learn from their mates-a social learning bias that has previously received little to no attention. However, we found that mates-based learning occurred infrequently and was secondary to aggression-based social learning biases. Our results therefore additionally highlight the importance of including combinations of multiple potential information pathways in social learning analyses to account for secondary learning pathways that may otherwise be missed.

Coordinated responses in eusocial insect colonies arise from worker interaction networks that enable collective processing of ecologically relevant information. Previous studies have detected a structural motif in these networks known as the feed-forward loop, which functions to process information in other biological regulatory networks (e.g. transcriptional networks). However, the processes that generate feed-forward loops among workers and the consequences for information flow within the colony remain largely unexplored. We constructed an agent-based model to investigate how individual variation in activity and movement shaped the production of feed-forward loops in a simulated insect colony. We hypothesized that individual variation along these axes would generate feed-forward loops by driving variation in interaction frequency among workers. We found that among-individual variation in activity drove over-representation of feed-forward loops in the interaction networks by determining the directionality of interactions. However, despite previous work linking feed-forward loops with efficient information transfer, activity variation did not promote faster or more efficient information flow, thus providing no support for the hypothesis that feed-forward loops reflect selection for enhanced collective functioning. Conversely, individual variation in movement trajectory, despite playing no role in generating feed-forward loops, promoted fast and efficient information flow by linking together otherwise unconnected regions of the nest.

Social learning (learning from others) can be a cost-effective way of gaining information compared to asocial (independent) learning. However, learning from others indiscriminately can lead to the acquisition of maladaptive behaviours or outdated information. Evolutionary theory therefore predicts that individuals will use social information adaptively through the use of 'social learning strategies'. Restrictive laboratory conditions, however, make studying human learning strategies problematic. Abstract tasks, unrealistic sources of social information and methodologies that do not take into account the influence of physical location over large spaces make it difficult to ascertain if previous findings are representative of the way we would use social information in reality. Here I describe a novel platform for studying human social behaviour within immersive virtual environments: 'Virtual Environments for Research into Social Evolution' (VERSE). Through the use of gaming technology, VERSE allows researchers to build realistic, three-dimensional, open world environments where participants can complete ecologically relevant tasks while actively observing computer-controlled artificial intelligence agents (AIs) that act as realistic yet controllable sources of social information. This methodological article begins by exploring what social learning strategies are and the problems with studying social learning behaviour in humans (compared to animal populations, for example). I then discuss how gaming technology can be used in behavioural research and follow on with a detailed account of the specific functionalities available in VERSE. I conclude with a worked example of how VERSE can be used to construct a novel behavioural experiment. Altogether, VERSE has great potential to give us insight into how human individuals learn within novel environments in a way that has never before been possible.

Social learning strategies describe what, when, and from whom individuals choose to learn. Evidence suggests that both humans and animals are capable of strategic social learning. However, human research generally lacks ecological and spatial realism, making it difficult to understand the importance of our use of social information in an evolutionary context. In this study, we use virtual reality to simulate three novel tasks inspired by the animal literature (Container, Route Choice and Foraging tasks) within complex, three-dimensional environments. In each experiment, combinations of demonstrators with different characteristics gave opposing solutions to the task to determine from whom participants preferentially learned. Importantly, participants were able to freely navigate the environment and attempt the task in any way they chose by using or ignoring social information. We found that participants displayed an overall bias towards learning asocially (independently) rather than socially. Asocial learning was favoured more strongly during complex tasks that spanned larger spatial scales, potentially due to the difficulties in keeping track of social information in such scenarios. When learning from others, participants displayed a bias towards learning from the majority over the minority (positive frequency-dependent social learning) and towards learning from the most successful demonstrators (payoff-based social learning), which supports the findings of previous, lab-based experiments. There was no apparent bias with respect to demonstrator dominance status, gender and body size. Our findings are the first to show a variation in the use of social learning across task and environmental complexities in humans, to carry out a comprehensive evaluation of hypothetical human learning biases, and to provide a methodological link between non-human and human social learning experiments. As demonstrated here, immersive virtual environments have great potential for research into human social evolution and we strongly encourage future research to adopt a similar approach.