A neuroimaging dataset during sequential color qualia similarity judgments with and without reports.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Takahiro Hirao, Mitsuhiro Miyamae, Daisuke Matsuyoshi, Ryuto Inoue, Yuhei Takado, Takayuki Obata, Makoto Higuchi, Naotsugu Tsuchiya, Makiko Yamada
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引用次数: 0

Abstract

Recent neuroscientific research has advanced our understanding of consciousness, yet the connection between specific qualitative aspects of consciousness, known as "qualia," and particular brain regions or networks remains elusive. Traditional methods that rely on verbal descriptions from participants pose challenges in neuroimaging studies. To address this, our group has introduced a novel "qualia structure" paradigm that leverages exhaustive, structural, and relational comparisons among qualia instead of verbal reports. In this study, we present the fMRI dataset that captures relational similarity judgments among two out of nine color qualia per trial from 35 participants. This dataset also includes a "no-report" condition in half of the trials to assess the impact of overt reporting. Additionally, each participant's color discriminability was evaluated with a hue test conducted outside the scanner. Our data offer valuable insights into the brain functions associated with color qualia and contribute to a deeper understanding of the neural foundations of consciousness.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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