Interactive cognitive maps support flexible behavior under threat

TY - JOUR

T1 - Interactive cognitive maps support flexible behavior under threat

AU - Wise, Toby

AU - Charpentier, Caroline J.

AU - Dayan, Peter

AU - Mobbs, Dean

N1 - Publisher Copyright: © 2023 The Authors

PY - 2023/8/29

Y1 - 2023/8/29

N2 - In social environments, survival can depend upon inferring and adapting to other agents’ goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents’ preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants’ inferences about threatening agents’ preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents’ preferences, facilitating generalized predictions of their behavior and effective responses.

AB - In social environments, survival can depend upon inferring and adapting to other agents’ goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents’ preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants’ inferences about threatening agents’ preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents’ preferences, facilitating generalized predictions of their behavior and effective responses.

KW - avoidance

KW - cognitive maps

KW - CP: Neuroscience

KW - decision-making

KW - learning

KW - planning

KW - social inference

UR - https://www.scopus.com/pages/publications/85168543816

U2 - 10.1016/j.celrep.2023.113008

DO - 10.1016/j.celrep.2023.113008

M3 - Article

C2 - 37610871

AN - SCOPUS:85168543816

SN - 2211-1247

VL - 42

JO - Cell Reports

JF - Cell Reports

IS - 8

M1 - 113008

ER -