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

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.