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Accumulation System: Distributed Neural Substrates of Perceptual Decision Making Revealed by fMRI Deconvolution.

Authors :
Yusuke Morito
Tsutomu Murata
Source :
Journal of Neuroscience. 6/15/2022, Vol. 42 Issue 24, p4891-4912. 22p.
Publication Year :
2022

Abstract

Neural substrates of evidence accumulation have been a central issue in decision-making studies because of the prominent success of the accumulation model in explaining a wide range of perceptual decision making. Since accumulation-shaped activities have been found in multiple brain regions, which are called accumulators, questions regarding functional relations among these accumulators are emerging. This study employed the deconvolution method of functional magnetic resonance imaging (fMRI) signals from human male and female participants during object-category decision tasks, taking advantage of the whole-brain coverage of fMRI with improved availability of temporal information of the deconvolved activity. We detected the accumulation activity in many noncategory-selective regions (NCSRs) over the frontal, parietal, and temporal lobes as well as category-selective regions (CSRs) of the categorization task. Importantly, the frontal regions mostly showed activity peaks matching the decision timing (classified as "type-A accumulator"), while activity peaks of the parietal and temporal regions were behind the decision (classified as "type-B accumulator"). The CSRs showed activity peaks whose timing depended on both region and stimulus preference, plausibly reflecting the competition among the alternative choices (classified as "type-C accumulator"). The results suggest that these functionally heterogeneous accumulators form a system for evidence accumulation in which the type-A accumulator regions make decisions in a general manner while the type-B and type-C accumulator regions are employed depending on the modality and content of decision tasks. The concept of the accumulation system may provide a key to understanding the universality of the accumulation model over various kinds of decision tasks. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02706474
Volume :
42
Issue :
24
Database :
Academic Search Index
Journal :
Journal of Neuroscience
Publication Type :
Academic Journal
Accession number :
157513493
Full Text :
https://doi.org/10.1523/JNEUROSCI.1062-21.2022