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.

Both the CD4-CD8- (double negative) and CD4-CD8+ T cell lineages have been shown to contain T cells which recognize microbial lipid and glycolipid Ags in the context of human CD1 molecules. To determine whether T cells expressing the CD4 coreceptor could recognize Ag in the context of CD1, we derived CD4+ T cell lines from the lesions of leprosy patients. We identified three CD4+ Mycobacterium leprae-reactive, CD1-restricted T cell lines: two CD1b restricted and one CD1c restricted. These T cell lines recognize mycobacterial Ags, one of which has not been previously described for CD1-restricted T cells. The response of CD4+ CD1-restricted T cells, unlike MHC class II-restricted T cells, was not inhibited by anti-CD4 mAb, suggesting that the CD4 coreceptor does not impact positive or negative selection of CD1-restricted T cells. The CD4+ CD1-restricted T cell lines produced IFN-gamma and GM-CSF, the Th1 pattern of cytokines required for cell-mediated immunity against intracellular pathogens, but no detectable IL-4. The existence of CD4+ CD1-restricted T cells that produce a Th1 cytokine pattern suggests a contributory role in immunity to mycobacterial infection.