ArticleData in brief2023
Functional magnetic resonance imaging data for the neural dynamics underlying the acquisition of distinct auditory categories.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
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3 authors.
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Abstract
How people learn and represent auditory categories in the brain is a fundamental question in auditory neuroscience. Answering this question could provide insights into our understanding of the neurobiology of speech learning and perception. However, the neural mechanisms underlying auditory category learning are far from understood. We have revealed that the neural representations of auditory categories emerge during category training, and the type of category structures drives the emerging dynamics of the representations [1]. The dataset introduced here was derived from [1], where we collected to examine the neural dynamics underlying the acquisition of two distinct category structures: rule-based (RB) and information-integration (II) categories. Participants were trained to categorize these auditory categories with trial-by-trial corrective feedback. The functional magnetic resonance imaging (fMRI) technique was used to assess the neural dynamics related to the category learning process. Sixty adult Mandarin native speakers were recruited for the fMRI experiment. They were assigned to either the RB (
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