Nick Chater and Ulrike Hahn

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Abstract
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Psychological theories of similarity are typically defined over evry
limited classes of representations. Geometric models represent objects
as points in a multi dimensional space. Set-theoretic models represent
objects as sets of features. We have recently developed an account of
similarity, representational distortion, which can deal with arbitrary
representations, and which includes geometric and set-theoretic
accounts as special cases. The similarity between two representations
is defined by the amount of distortion required to transform one
representation into the other. This can be quantified using the
mathematical theory of Kolmogorov complexity (Li & Vitanyi,
1993). This theory has a range of psychologically interesting
properties. In particular, we show that the Universal Law of
Generalization can be derived.
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