Specialization and selective social attention establishes the balance between individual and social learning

Abstract

A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual innovation or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.

Great Minds Think Alike? Spatial Search Processes Can Be More Idiosyncratic When Guided by More Accurate Information

“…Furthermore, as recently demonstrated by Wu et al. ( 2021 ), in random, dynamic environments, people are less reliant on observing others to obtain information, while balancing individual and social learning to avoid correlated social information and maladaptive information cascades. This may be beneficial if—as our results suggest—common trends seen in social information may systematically arise from the behavior of those individuals who are relatively poorly informed.…”

“…This may be related to the work of Findling et al (2019), who found computational noise to be a core feature of learning, and suggested that an increase in that noise accompanying a high learning rate could be a significant source of behavioral variability in reward-guided decisions. Furthermore, as recently demonstrated by Wu et al (2021), in random, dynamic environments, people are less reliant on observing others to obtain information, while balancing individual and social learning to avoid correlated social information and maladaptive information cascades. This may be beneficial if-as our results suggest-common trends seen in social information may systematically arise from the behavior of those individuals who are relatively poorly informed.…”

Overcoming cognitive set bias requires more than seeing an alternative strategy

“…One way that humans mitigate the risks of decision-making under uncertainty, is by using problem-solving heuristics or rules-of-thumb that are based on previous experience with similar situations 10,11 and are subject to individual differences . Specifically, in problem spaces or environments which change often or are otherwise uncertain, alternative strategies may be sampled frequently 47,48 or reliance on other sources of information, like socially-acquired strategies can increase 49 .…”

Individual exploration and selective social learning: Balancing exploration-exploitation trade-offs in collective foraging

“…Our results predict that higher explorative/innovative tendencies can improve a group's problem-solving capabilities in a rugged or complex problem-space where multiple solutions need to be discovered. However, pure exploratory strategies need to be balanced with social learning in complex spaces to focus a group's effort on the solutions already found and optimize the search (Fang et al, 2010;Miu et al, 2020;Wu et al). By contrast, when the problem spaces are 'simple'…”