Feature Selection for Inductive Generalization
Article first published online: 3 NOV 2010
Copyright © 2010 Cognitive Science Society, Inc.
Volume 34, Issue 8, pages 1574–1593, November 2010
How to Cite
Yu, N.-Y., Yamauchi, T., Yang, H.-F., Chen, Y.-L. and Gutierrez-Osuna, R. (2010), Feature Selection for Inductive Generalization. Cognitive Science, 34: 1574–1593. doi: 10.1111/j.1551-6709.2010.01122.x
- Issue published online: 3 NOV 2010
- Article first published online: 3 NOV 2010
- Received 2 February 2009; received in revised form 2 April 2010; accepted 7 April 2010
- Similarity perception;
- Machine learning;
- Feature selection;
- Inductive generalization
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.