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Per-Unit Sequence Models01:26

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Further tests of sequence-sensitive models in a modified garner task using separable dimensions.

Deborah J Lin1, Daniel R Little1

  • 1Melbourne School of Psychological Sciences, University of Melbourne.

Journal of Experimental Psychology. General
|November 17, 2022
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Summary
This summary is machine-generated.

Sequential effects in perceptual categorization are present for both separable and integral dimensions. A sequence-sensitive exemplar model better explains these findings than a feature model.

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Area of Science:

  • Cognitive Psychology
  • Perception

Background:

  • Perceptual categorization distinguishes between separable and integral dimensions.
  • Sequential effects in cognitive tasks are well-documented.

Purpose of the Study:

  • Investigate sequential effects in perceptual categorization using separable dimensions.
  • Compare sequence-sensitive exemplar and feature models.

Main Methods:

  • Four experiments using separable dimensions in a modified Garner paradigm.
  • Fitting sequence-sensitive exemplar and feature models to data.

Main Results:

  • Sequential effects were found for separable dimensions, similar to integral dimensions.
  • The filtering task effect was absent for separable dimensions.
  • An exemplar model provided a better fit than a feature model.

Conclusions:

  • Sequential effects are prevalent in perceptual categorization regardless of dimension type.
  • Exemplar models offer a more comprehensive explanation for these effects.