Proceedings of the Annual Meeting of the Cognitive Science Society

Human activity requires an ability to generalise beyond theavailable evidence, but when examples are limited – as theynearly always are – the problem of how to do so becomes par-ticularly acute. In addressing this problem, Shepard (1987)established the importance of representation, and subsequentwork explored how representations shift as new data is ob-served. A different strand of work extending the Bayesianframework of Tenenbaum and Griffiths (2001) established theimportance of sampling assumptions in generalisation as well.Here we present evidence to suggest that these two issuesshould be considered jointly. We report two experiments whichreveal replicable qualitative patterns of individual differencesin the representation of a single category, while also showingthat sampling assumptions interact with these to drive gener-alisation. Our results demonstrate that how people shift theircategory representation depends upon their sampling assump-tions, and that these representational shifts drive much of theobserved learning.