Evidence map›Paper›PMID 36582408›Full record

ArticleComputational intelligence and neuroscience2022

Investigation of Involvement between Specific Brain Regions and Evaluation Criteria Elements in a Two-Selections Task.

Keita Mitani, Yukinobu Hoshino

Abstract read
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Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Keita MitaniGraduate School of System Engineering, Kochi University of Technology, Kochi, Japan.ORCID https://orcid.org/0000-0003-1529-3101
Yukinobu HoshinoSchool of Systems Engineering, Kochi University of Technology, Kochi, Japan.ORCID https://orcid.org/0000-0001-5285-8007

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is essential to understand the neural mechanisms underlying human decision-making. Several studies using traditional analysis have attempted to explain the neural mechanisms associated with decision-making based on abstract rewards. However, brain-decoding research that utilizes the multivoxel pattern analysis (MVPA) method, especially research focusing on decision-making, remains limited. In brain analysis, decoding strategies for multivoxels are required for various decision-making evaluation criteria. This is because in daily life, the human decision-making process makes use of many evaluation criteria. In the present study, we investigated the representation of evaluation criterion categories in a decision-making process using functional magnetic resonance imaging and MVPA. Participants performed a decision-making task that involved choosing a smartphone by referring to four types of evaluation criteria. The regions of interest (ROIs) were the ventromedial prefrontal cortex (vmPFC), nucleus accumbens (NAcc), and insula. Each combination of the four evaluation criteria was analyzed based on a binary classification using MVPA. From the binary classification accuracy obtained from MVPA, the regions that reflected differences in the evaluation criteria among the ROIs were evaluated. The results of the binary classification in the vmPFC and NAcc indicated that these regions can express evaluation criteria in decision-making processes.

Indexed as

BrainDecision MakingBrain MappingHumansMagnetic Resonance ImagingPrefrontal CortexReward

Identifiers

PMID36582408
PMCPMC9794435

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.