Simplifying social learning

• Published in: Trends in cognitive sciences 2024-05, Vol.28 (5), p.428-440
• Main Authors: Hackel, Leor M., Kalkstein, David A., Mende-Siedlecki, Peter
• Format: Article
• Language: English
• Subjects: abstraction; expertise; Humans; Models, Psychological; reinforcement learning; Reinforcement, Psychology; semantic concepts; social cognition; Social Learning - physiology
• ISSN: 1364-6613; 1879-307X; 1879-307X
• DOI: 10.1016/j.tics.2024.01.004

Social interactions present complex learning challenges.Recent computational models of learning assume people negotiate a tradeoff between complex-but-difficult behavior and easy-but-simple behavior.Humans have social expertise that lets them simplify difficult learning problems, reducing detailed scenarios to familiar social concepts that can subsequently guide learning.People can therefore use learning strategies that achieve flexible social behavior with easy cognition, using the earlier work of concept development to simplify the later work of complex choice.Accounting for pre-existing conceptual knowledge and expertise can enrich models of reinforcement learning in familiar environments. Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning. Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.