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Simplifying social learning
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DOI：10.1016/j.tics.2024.01.004.png)
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摘要
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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 easybut-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.
Keyword:
MODEL
HUMANS
MEMORY
HIPPOCAMPUS
PERCEPTION
INFERENCES
EFFICIENCY
SELECTION
LANGUAGE
CHOICES
期刊
IF:
17.2
论文数:
3.6K
被引数:
3.5W
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