Similarity, typicality, and categorization
Similarity and analogical reasoning
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Artificial Intelligence Review
On Prediction by Data Compression
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Unifying Computing and Cognition
Unifying Computing and Cognition
A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING
Applied Artificial Intelligence
Occam and Bayes in predicting category intuitiveness
Artificial Intelligence Review
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With this paper we wish to present a simplicity (informally `simple explanations are the best') formalism that is easily and directly applicable to modeling problems in cognitive science. While simplicity has been extensively advocated as a psychologically relevant principle, a general modeling formalism has been lacking. The Simplicity and Power model (SP) is a particular simplicity-based framework, that has been supported in machine learning (Wolff, Unifying computing and cognition: the SP theory and its applications, 2006). We propose its utility in cognitive modeling. For illustration, we provide SP demonstrations of the trade-off between encoding with whole exemplars versus parts of stimuli in learning and the effect of wide versus narrow distributions in categorization. In both cases, SP computations show how simplicity can account for these contrasts, in terms of how the frequency of individual exemplars in training compares to the frequency of their constituent parts.