Patterns of Activity in the Categorical Representations of Objects

Abstract

Abstract Object perception has been a subject of extensive fMRI studies in recent years. Yet the nature of the cortical representation of objects in the human brain remains controversial. Analyses of fMRI data have traditionally focused on the activation of individual voxels associated with presentation of various stimuli. The current analysis approaches functional imaging data as collective information about the stimulus. Linking activity in the brain to a stimulus is treated as a pattern-classification problem. Linear discriminant analysis was used to reanalyze a set of data originally published by Ishai et al. (2000), available from the fMRIDC (accession no. 2-20001113D). Results of the new analysis reveal that patterns of activity that distinguish one category of objects from other categories are largely independent of one another, both in terms of the activity and spatial overlap. The information used to detect objects from phase-scrambled control stimuli is not essential in distinguishing one object category from another. Furthermore, performing an object-matching task during the scan significantly improved the ability to predict objects from controls, but had minimal effect on object classification, suggesting that the task-based attentional benefit was nonspecific to object categories.