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Adaptive decision making often entails learning to approach things that lead to positive outcomes while avoiding things that are negative.The decision to avoid something removes the risk of a negative experience but also forgoes the opportunity to obtain information, specifically that a seemingly negative option is actually positive.This paper explores how people learn to approach or avoid objects with uncertain payoffs.We provide a computational-level analysis of optimal decision making in this problem which quantifies how the probability of encountering an object in the future should impact the decision to approach or avoid it.A large experiment conducted online shows that most people intuitively take into account both their uncertainty and the value of information when deciding to approach seemingly bad things.
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Stored only when both papers are in the corpus — 5 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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