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- Title
Abstract Neural Representations of Category Membership beyond Information Coding Stimulus or Response.
- Authors
Mok, Robert M.; Love, Bradley C.
- Abstract
For decades, researchers have debated whether mental representations are symbolic or grounded in sensory inputs and motor programs. Certainly, aspects of mental representations are grounded. However, does the brain also contain abstract concept representations that mediate between perception and action in a flexible manner not tied to the details of sensory inputs and motor programs? Such conceptual pointers would be useful when concepts remain constant despite changes in appearance and associated actions. We evaluated whether human participants acquire such representations using fMRI. Participants completed a probabilistic concept learning task in which sensory, motor, and category variables were not perfectly coupled or entirely independent, making it possible to observe evidence for abstract representations or purely grounded representations. To assess how the learned concept structure is represented in the brain, we examined brain regions implicated in flexible cognition (e.g., pFC and parietal cortex) that are most likely to encode an abstract representation removed from sensory–motor details. We also examined sensory–motor regions that might encode grounded sensory–motor-based representations tuned for categorization. Using a cognitive model to estimate participants' category rule and multivariate pattern analysis of fMRI data, we found the left pFC and human middle temporal visual area (MT)/V5 coded for category in the absence of information coding for stimulus or response. Because category was based on the stimulus, finding an abstract representation of category was not inevitable. Our results suggest that certain brain areas support categorization behavior by constructing concept representations in a format akin to a symbol that differs from stimulus–motor codes.
- Subjects
STIMULUS &; response (Psychology); MENTAL representation; PARIETAL lobe; CONCEPT learning; MULTIVARIATE analysis; FUNCTIONAL magnetic resonance imaging
- Publication
Journal of Cognitive Neuroscience, 2022, Vol 34, Issue 10, p1719
- ISSN
0898-929X
- Publication type
Article
- DOI
10.1162/jocn_a_01651