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

Robert D. Hawkins (), Andrew M. Berdahl, Alex ‘Sandy’ Pentland, Joshua B. Tenenbaum, Noah D. Goodman and P. M. Krafft
Additional contact information
Robert D. Hawkins: Stanford University
Andrew M. Berdahl: University of Washington
Alex ‘Sandy’ Pentland: MIT
Joshua B. Tenenbaum: MIT
Noah D. Goodman: Stanford University
P. M. Krafft: MIT

Nature Human Behaviour, 2023, vol. 7, issue 10, 1767-1776

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41562-023-01682-x

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