December 2001
Volume 1, Issue 3
Free
Vision Sciences Society Annual Meeting Abstract  |   December 2001
Models of object categorization reflect multiple categorization strategies
Author Affiliations
  • Robert J. Peters
    Computation and Neural Systems, Caltech, Pasadena, CA, USA
  • Fabrizio Gabbiani
    Baylor College of Medicine, Houston, TX, USA
  • Christof Koch
    Computation and Neural Systems, Caltech, Pasadena, CA, USA
Journal of Vision December 2001, Vol.1, 294. doi:10.1167/1.3.294
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      Robert J. Peters, Fabrizio Gabbiani, Christof Koch; Models of object categorization reflect multiple categorization strategies. Journal of Vision 2001;1(3):294. doi: 10.1167/1.3.294.

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Abstract

We investigated models of visual object categorization in humans, using simple line-drawn objects parameterized along four dimensions unknown to the subjects. The categorization task required subjects to learn to classify training exemplars in a 2-AFC paradigm into two classes linearly separable in the objects' parameter space. Following training, subjects categorized additional test exemplars, and the classification probabilities of these test exemplars were used to fit several categorization models, including exemplar, boundary, prototype, and cue-validity models. Previous results (Peters et al., J Cog Neurosci Suppl 2000) showed that exemplar and boundary models accounted for human categorization performance significantly better than did prototype and cue-validity models, but were not distinguishable from each other. Therefore, we considered new factors that may influence the relative performances of the exemplar and boundary models. First, we tested configurations of training exemplars that differed only in the orientation in parameter space of the boundary separating the two categories. This led to no systematic changes in the relative performances of the exemplar and boundary models. Second, we tested configurations of training exemplars that differed in which features were most important in separating the categories. It appears that when the definition of categories depends critically on pairs of features (“meta-features”) that are in close physical proximity and map well onto the x-y image plane, the boundary model performs as well as the exemplar model. Conversely, when the categories depend on pairs of features that are physically separated and do not map directly to the image plane, the boundary model performs significantly worse than the exemplar model, while the cue-validity model performs as well as the exemplar model. This suggests that observers use different strategies depending on whether they are able to treat the relevant features as a unit.

Peters, R.J., Gabbiani, F., Koch, C.(2001). Models of object categorization reflect multiple categorization strategies [Abstract]. Journal of Vision, 1( 3): 294, 294a, http://journalofvision.org/1/3/294/, doi:10.1167/1.3.294. [CrossRef]
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