Proceedings of the Annual Meeting of the Cognitive Science Society

Bayesian models of cognition posit that people compute prob-ability distributions over hypotheses, possibly by construct-ing a sample-based approximation. Since people encountermany closely related distributions, a computationally efficientstrategy is to selectively reuse computations – either the sam-ples themselves or some summary statistic. We refer to thesereuse strategies as amortized inference. In two experiments,we present evidence consistent with amortization. When se-quentially answering two related queries about natural scenes,we show that answers to the second query vary systematicallydepending on the structure of the first query. Using a cog-nitive load manipulation, we find evidence that people cachesummary statistics rather than raw sample sets. These resultsenrich our notions of how the brain approximates probabilisticinference.