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Date: 05.05.21 15:20-17:00

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Abstract:

Bayesian models of cognition and behavior compare human performance to the performance of an ideal decision maker. Such models are particularly promising when they are used in reverse-engineering explanations: explanations that descend from Marr's computational level of analysis to the algorithmic and the implementational level. Unfortunately, it remains unclear exactly how Bayesian models constrain and influence these lower levels of analysis. In several examples ranging from signal detection tasks to inductive reasoning I sketch how Bayesian models are used in cognitive science.