Capturing mental state reasoning with influence diagrams


People have a keen ability to reason about others' mental states, which is central for communication and cooperation. A core question for cognitive science is what mental representations support this ability. We offer one proposal based on the framework of influence diagrams, an extension of Bayes nets that is suited for representing intentional goal-directed agents. We evaluate this framework in two experiments that require participants to make inferences about what another person knows or values. In both experiments, participants' judgments were better predicted by our influence diagrams account than by several alternative accounts.

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