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Abstract

Successful avoidance of recurrent threats depends on inferring threatening agents’ preferences and predicting their movement
patterns accordingly. However, it remains largely unknown how humans achieve this, despite the fact that many natural
threats are posed by complex, dynamic agents that act according to their own goals. Here, we propose that humans exploit
an interactive cognitive map of the social environment to infer threatening agents’ preferences and also to simulate their
future behavior, providing for flexible, generalizable avoidance strategies. We tested this proposal across three preregistered
experiments (total n=510) using a task in which participants collected rewards while avoiding one of several possible virtual
threatening agents. A novel, model-based, hypothesis-testing inverse reinforcement learning computational model best explained
participants’ inferences about threatening agents’ latent preferences, with participants using this inferred knowledge
to enact generalizable, model-based avoidance strategies across different environments. Using tree-search planning models,
we found that participants’ behavior was best explained by a planning algorithm that incorporated simulations of the threat’s
goal-directed behavior, and that prior expectations about the threat’s predictability were linked to individual differences in
avoidance. Together, our results indicate that humans use a cognitive map to determine threatening agents’ preferences, in
turn facilitating generalized predictions of the threatening agent’s behavior and enabling flexible and effective avoidance.