Proceedings of the Annual Meeting of the Cognitive Science Society

Algorithms for approximate Bayesian inference, such asMonte Carlo methods, provide one source of models of howpeople may deal with uncertainty in spite of limited cognitiveresources. Here, we model learning as a process of sequentialsampling, or ‘particle filtering’, and suggest that an individ-ual’s working memory capacity (WMC) may be usefully mod-elled in terms of the number of samples, or ‘particles’, that areavailable for inference. The model qualitatively captures twodistinct effects reported recently, namely that individuals withhigher WMC are better able to (i) learn novel categories, and(ii) flexibly switch between different categorization strategies.