Associations between Categorization Rules and Categorical Visual Search

Abstract

One of the tasks of modern categorization theories is the search for cognitive functions associated with categorical learning. Foreign research has demonstrated an association between visual search and continuum category learning (categorical representation): success of the visual search based on single-feature categorical rules does not change with increasing number of distractors, but collapses when the search is supported by information integration categorical rules. The purpose of the current study was to identify links between learning discrete categories and the success of visual search in order to test the previously obtained effect on another type of categories — verbal rules (explicit type) and prototypes (implicit type). It was assumed that, since the representation based on prototypes would have a lower level of awareness, its support for the visual search based (success) would be lower than while forming verbal rules. Participants (N = 121) completed a task where they learnt a new artificial category belonging to one of two types of rules and immediately after that performed a categorical visual search task where they were asked to search for a target relevant to the category they learnt. We found that after learning the verbal rule as well as forming prototypes, visual search success did not collapse with increasing number of distractors. We also found that the higher the success rate in learning a new category, the more effective the visual search was, regardless of the type of rule. Thus, we have shown for the first time that visual search can be supported by different types of categories, both explicit and implicit. We explain the present results and their difference from the results of the previous study by the fact that categories based on discrete features (as opposed to continuums) allows to create more robust representations that are easier to use in non-categorical tasks such as visual search.