Generalization in Category Learning: The Roles of Representational and Decisional Uncertainty

Effective generalization in a multiple-category situation involves both assessing potential membership in individual categories and resolving conflict between categories while implementing a decision bound. We separated generalization from decision bound implementation using an information integration task in which category exemplars varied over two incommensurable feature dimensions. Human subjects first learned to categorize stimuli within limited training regions, and then, during fMRI scanning, they also categorized transfer stimuli from new regions of perceptual space. Transfer stimuli differed both in distance from the training region prototype and distance from the decision bound, allowing us to independently assess neural systems sensitive to each. Across all stimulus regions, categorization was associated with activity in the extrastriate visual cortex, basal ganglia, and the bilateral intraparietal sulcus. Categorizing stimuli near the decision bound was associated with recruitment of the frontoinsular cortex and medial frontal cortex, regions often associated with conflict and which commonly coactivate within the salience network. Generalization was measured in terms of greater distance from the decision bound and greater distance from the category prototype (average training region stimulus). Distance from the decision bound was associated with activity in the superior parietal lobe, lingual gyri, and anterior hippocampus, whereas distance from the prototype was associated with left intraparietal sulcus activity. The results are interpreted as supporting the existence of different uncertainty resolution mechanisms for uncertainty about category membership (representational uncertainty) and uncertainty about decision bound (decisional uncertainty).