Functional magnetic resonance imaging data for the neural dynamics underlying the acquisition of distinct auditory categories

• Published in: Data in brief 2023-04, Vol.47, p.108972-108972, Article 108972
• Main Authors: Gan, Zhenzhong, Wang, Suiping, Feng, Gangyi
• Format: Article
• Language: English
• Subjects: Auditory category learning; Category structure; Data; FMRI; MVPA; Neural dynamics; Representation
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2023.108972

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 (n = 30, 19 females) or II (n = 30, 22 females) learning task. Each task consisted of six training blocks where each consisting of 40 trials. Spatiotemporal multivariate representational similarity analysis has been used to examine the emerging patterns of neural representations during learning [1]. This open-access dataset could potentially be reused to investigate a range of neural mechanisms (e.g., functional network organizations underlying learning of different structures of categories and neuromarkers associated with individual behavioral learning success) involved in auditory category learning.