The Effects of Categorization on Perceptual Judgment are Robust across Different Assessment Tasks

Abstract

Learned visual categorical perception (CP) effects were assessed using three different measures (similarity rating, same-different judgment, and an XAB task) and two sets of stimuli differing in discriminability and varying on one category-relevant and one category-irrelevant dimension. Participant scores were converted to a common scale to allow assessment method to serve as an independent variable. Two different analyses using the Bayes Factor approach produced patterns of results consistent with learned CP effects: compared to a control group, participants trained on the category distinction could better discriminate between-category pairs of stimuli and were more sensitive to the category-relevant dimension. In addition, performance was better in general for the more highly discriminable stimuli, but stimulus discriminability did not influence the pattern of observed CP effects. Furthermore, these results were consistent regardless of how performance was assessed. This suggests that, for these methods at least, learned CP effects are robust across substantially different performance measures. Four different kinds of learned CP effects are reported in the literature singly or in combination: greater sensitivity between categories, reduced sensitivity within categories, increased sensitivity to category-relevant dimensions, and decreased sensitivity to category-irrelevant dimensions. The results of the current study suggest that the cause of these different patterns of CP effects is not due to either stimulus discriminability or assessment task. Other possible causes of the differences in reported CP findings are discussed.

Keywords

• categorization
• categorical perception
• compression
• expansion
• learning
• similarity
• online experiments
• Bayes factor