An Attention-Driven Computational Model of Human Causal Reasoning

AbstractHerein we describe CRAMM, a framework for Causal Reasoning via Attention and Mental Models. CRAMM develops and extends assumptions made by a previously developed counterfactual simulation model of human causal judgment. We implement CRAMM computationally and demonstrate how it robustly captures human causal judgments about simple two-object interactions at the level of underlying cognitive and perceptual processes, including data on eye-movements that serve as direct evidence for the role of counterfactuals in causal judgment.

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