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#algorithmic-level #cognitive-science #computational-level #marrs-levels-of-analysis #rationality #resource-rational-analysis
Vul et al. (2014) analyzed an abstract computational architecture whose elementary operation was to sample from the posterior distribution on whether or not an action will yield a reward. The analysis found that as the cost of sampling increases, the resource-rational algorithm changes from expected utility maximization (i.e., infinitely many samples) to probability matching (i.e., using a single sample). For a wide range of realistic computational costs, the optimal number of samples was surprisingly close to one, indicating that—in contrast to expected-utility theory—resource-rationality is consistent with probability matching (Herrnstein & Loveland, 1975)
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owner: rappatoni - (no access) - Griffiths_et_al-2015-Topics_in_Cognitive_Science.pdf, p8


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