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Poster
in
Workshop: Intrinsically Motivated Open-ended Learning (IMOL)

Modeling Cognitive Strategies in Teaching

Sevan Harootonian · Yael Niv · Tom Griffiths · Mark Ho

Keywords: [ Cognitive Science ] [ bayesian modeling ] [ Inverse reinforcement learning ] [ teaching ]


Abstract:

Teaching is a complex social behavior that sometimes results from goal-directed processing. However, goal-directed teaching is cognitively demanding since it requires actively assessing and correcting gaps in a learner's knowledge. When do people teach using such mentally effortful strategies versus falling back on more cognitively frugal ones? Here, we investigated this question using a combination of novel behavioral experiments and computational theory. We found robust individual differences in people's teaching strategies: some participants spontaneously teach using high-effort processing (e.g., Bayesian theory of mind and model-based planning) while others engage in low-effort processing (e.g., model-free heuristics). Our results and analyses provide a novel demonstration of how people engage in planning versus heuristics when teaching, as well as how people adapt processing to avoid mental effort in social interactions.

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