Cognitive Science 34 (8):1574-1593 (2010)

Authors
H. E. Yang
National Yang Ming University
Abstract
Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data
Keywords Similarity perception  Feature selection  Inductive generalization  Machine learning
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DOI 10.1111/j.1551-6709.2010.01122.x
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References found in this work BETA

Vison.David Marr - 1982 - W. H. Freeman.
Features of Similarity.Amos Tversky - 1977 - Psychological Review 84 (4):327-352.
Attention, Similarity, and the Identification–Categorization Relationship.Robert M. Nosofsky - 1986 - Journal of Experimental Psychology: General 115 (1):39-57.
Vision as Bayesian Inference: Analysis by Synthesis?Alan Yuille & Daniel Kersten - 2006 - Trends in Cognitive Sciences 10 (7):301-308.

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