Social Concepts Simplify Complex Reinforcement Learning

Abstract

Humans often generalize rewarding experiences across abstract social roles. Theories of reward learning suggest that people generalize through model-based learning, but such learning is cognitively costly. Why do people seem to generalize across social roles with ease? Humans are social experts who easily recognize social roles that reflect familiar semantic concepts (e.g., “helper” or “teacher”). People may associate these roles with model-free reward (e.g., learning that helpers are rewarding), allowing them to generalize easily (e.g., interacting with novel individuals identified as helpers). In four online experiments with U.S. adults ( N = 577), we found evidence that social concepts ease complex learning (people generalize more and at faster speed) and that people attach reward directly to abstract roles (they generalize even when roles are unrelated to task structure). These results demonstrate how familiar concepts allow complex behavior to emerge from simple strategies, highlighting social interaction as a prototype for studying cognitive ease in the face of environmental complexity.

Old strategies, new environments: Reinforcement Learning on social media

“…Describing behaviour in such quantitative, abstract terms allows researchers to rigorously model the generative process behind social actions, facilitating precise predictions about how humans will behave in different conditions. Importantly, RL effectively characterises human and animal reward pursuit at both behavioural and neural levels (24), including in the social domain (6,25,26) -suggesting that a rewardpursuing process developed across evolution partly motivates social behaviour. In what follows, we explore how online environments might interact with, and at times exploit, features of RL at each stage: update, valuation and action selection.…”