CATEGORY LEARNING & INDUCTION

I have two current strands of research related to understanding how we learn to categorize and make inductions.

The first strand examines the popular notion that category learning can be explained through the operation of distinct ‘implicit’ and ‘explicit’ systems. In work initiated with David Shanks and David Lagnado we have investigated probabilistic categorization tasks in an attempt to find evidence for the operation of such separate systems. In a similar vein, work with John Dunn and Mike Kalish, is attempting to go beyond the ‘systems’ debate by utilizing a technique known as state-trace analysis to uncover the inherent dimensionality of category learning (see Newell et al., 2011).

The second strand (with Brett Hayes and Oren Griffiths) looks at induction, and specifically the mechanisms underlying induction on the basis of uncertain categorization judgments. This so-called ‘as-if’ reasoning (reasoning as-if a given judgment is true when there is uncertainty associated with it) appears to be prevalent. We are attempting to document some of the factors which affect the tendency to reason in this way (e.g. Griffiths et al., 2012).

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