September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Modeling the Dynamics of Visual Object Categorization
Author Affiliations
  • Jianhong Shen
    Department of Psychology, Vanderbilt University
  • Thomas Palmeri
    Department of Psychology, Vanderbilt University
Journal of Vision September 2015, Vol.15, 1160. doi:10.1167/15.12.1160
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      Jianhong Shen, Thomas Palmeri; Modeling the Dynamics of Visual Object Categorization. Journal of Vision 2015;15(12):1160. doi: 10.1167/15.12.1160.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Novices are faster and more accurate to verify category membership at an intermediate level of abstraction, the so-called the basic or entry level (e.g., “bird”), than a superordinate (e.g., “animal”) or subordinate level (e.g., “Blue Jay”). One explanation for the relative speed of basic-level categorization is that categorization at this intermediate level is a prerequisite for more superordinate and subordinate categorizations – you need to know that it is a bird before you can tell whether it is an animal or a Blue Jay. An alternative explanation is that basic-level categorizations are fast because the basic level is more differentiated and informative, not that it happens first. We evaluated these two hypotheses by fitting the well-known drift-diffusion model of perceptual decision making to accuracy and response time data from a large sample of online participants, including both novices and individuals with varying levels of birding expertise. We specifically identified these two hypotheses with differences in process parameters within the diffusion model: variability in non-decisional, perceptual processing time across category levels would indicate the former hypothesis, whereas variability in drift rate across category levels would indicate the latter hypothesis. We specifically applied the diffusion model using a Bayesian hierarchical framework, which provides a powerful account of individual differences in the model parameters across conditions. Behaviorally, we replicated the basic-level advantage for novices in our online experiments. Theoretically, we found that variability in categorization speed across levels of categorization were well captured by variability in the drift rate across levels without any changes in the non-decisional, perceptual processing time across levels. Our results help to unravel the psychological processes that give rise to the behavioral pattern in speeded categorization and inform the understanding of individual differences in visual object categorization.

Meeting abstract presented at VSS 2015

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