Neural dynamics underlying the acquisition of distinct auditory category structures
Despite the multidimensional and temporally fleeting nature of auditory signals we quickly learn to assign novel sounds to behaviorally relevant categories. The neural systems underlying the learning and representation of novel auditory categories are far from understood. Current models argue for a...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-12-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921008387 |
| _version_ | 1818984946471010304 |
|---|---|
| author | Gangyi Feng Zhenzhong Gan Han Gyol Yi Shawn W. Ell Casey L. Roark Suiping Wang Patrick C.M. Wong Bharath Chandrasekaran |
| author_facet | Gangyi Feng Zhenzhong Gan Han Gyol Yi Shawn W. Ell Casey L. Roark Suiping Wang Patrick C.M. Wong Bharath Chandrasekaran |
| author_sort | Gangyi Feng |
| collection | DOAJ |
| description | Despite the multidimensional and temporally fleeting nature of auditory signals we quickly learn to assign novel sounds to behaviorally relevant categories. The neural systems underlying the learning and representation of novel auditory categories are far from understood. Current models argue for a rigid specialization of hierarchically organized core regions that are fine-tuned to extracting and mapping relevant auditory dimensions to meaningful categories. Scaffolded within a dual-learning systems approach, we test a competing hypothesis: the spatial and temporal dynamics of emerging auditory-category representations are not driven by the underlying dimensions but are constrained by category structure and learning strategies. To test these competing models, we used functional Magnetic Resonance Imaging (fMRI) to assess representational dynamics during the feedback-based acquisition of novel non-speech auditory categories with identical dimensions but differing category structures: rule-based (RB) categories, hypothesized to involve an explicit sound-to-rule mapping network, and information integration (II) based categories, involving pre-decisional integration of dimensions via a procedural-based sound-to-reward mapping network. Adults were assigned to either the RB (n = 30, 19 females) or II (n = 30, 22 females) learning tasks. Despite similar behavioral learning accuracies, learning strategies derived from computational modeling and involvements of corticostriatal systems during feedback processing differed across tasks. Spatiotemporal multivariate representational similarity analysis revealed an emerging representation within an auditory sensory-motor pathway exclusively for the II learning task, prominently involving the superior temporal gyrus (STG), inferior frontal gyrus (IFG), and posterior precentral gyrus. In contrast, the RB learning task yielded distributed neural representations within regions involved in cognitive-control and attentional processes that emerged at different time points of learning. Our results unequivocally demonstrate that auditory learners’ neural systems are highly flexible and show distinct spatial and temporal patterns that are not dimension-specific but reflect underlying category structures and learning strategies. |
| first_indexed | 2024-12-20T18:27:05Z |
| format | Article |
| id | doaj.art-ae1584db7f0f455095e70c40f35563b7 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-20T18:27:05Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-ae1584db7f0f455095e70c40f35563b72022-12-21T19:30:07ZengElsevierNeuroImage1095-95722021-12-01244118565Neural dynamics underlying the acquisition of distinct auditory category structuresGangyi Feng0Zhenzhong Gan1Han Gyol Yi2Shawn W. Ell3Casey L. Roark4Suiping Wang5Patrick C.M. Wong6Bharath Chandrasekaran7Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; Corresponding author at: Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China.Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, ChinaDepartment of Neurological Surgery, University of California, San Francisco, CA 94158, United StatesDepartment of Psychology, Graduate School of Biomedical Sciences and Engineering, University of Maine, 5742 Little Hall, Room 301, Orono, ME 04469-5742, United StatesDepartment of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, United States; Center for the Neural Basis of Cognition, Pittsburgh, PA 15232, United StatesKey Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, ChinaDepartment of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, ChinaDepartment of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, United States; Center for the Neural Basis of Cognition, Pittsburgh, PA 15232, United States; Corresponding author at: Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, United States.Despite the multidimensional and temporally fleeting nature of auditory signals we quickly learn to assign novel sounds to behaviorally relevant categories. The neural systems underlying the learning and representation of novel auditory categories are far from understood. Current models argue for a rigid specialization of hierarchically organized core regions that are fine-tuned to extracting and mapping relevant auditory dimensions to meaningful categories. Scaffolded within a dual-learning systems approach, we test a competing hypothesis: the spatial and temporal dynamics of emerging auditory-category representations are not driven by the underlying dimensions but are constrained by category structure and learning strategies. To test these competing models, we used functional Magnetic Resonance Imaging (fMRI) to assess representational dynamics during the feedback-based acquisition of novel non-speech auditory categories with identical dimensions but differing category structures: rule-based (RB) categories, hypothesized to involve an explicit sound-to-rule mapping network, and information integration (II) based categories, involving pre-decisional integration of dimensions via a procedural-based sound-to-reward mapping network. Adults were assigned to either the RB (n = 30, 19 females) or II (n = 30, 22 females) learning tasks. Despite similar behavioral learning accuracies, learning strategies derived from computational modeling and involvements of corticostriatal systems during feedback processing differed across tasks. Spatiotemporal multivariate representational similarity analysis revealed an emerging representation within an auditory sensory-motor pathway exclusively for the II learning task, prominently involving the superior temporal gyrus (STG), inferior frontal gyrus (IFG), and posterior precentral gyrus. In contrast, the RB learning task yielded distributed neural representations within regions involved in cognitive-control and attentional processes that emerged at different time points of learning. Our results unequivocally demonstrate that auditory learners’ neural systems are highly flexible and show distinct spatial and temporal patterns that are not dimension-specific but reflect underlying category structures and learning strategies.http://www.sciencedirect.com/science/article/pii/S1053811921008387Auditory category learningCategory structureNeural representationSpatiotemporal dynamicsComputational modelingMVPA |
| spellingShingle | Gangyi Feng Zhenzhong Gan Han Gyol Yi Shawn W. Ell Casey L. Roark Suiping Wang Patrick C.M. Wong Bharath Chandrasekaran Neural dynamics underlying the acquisition of distinct auditory category structures NeuroImage Auditory category learning Category structure Neural representation Spatiotemporal dynamics Computational modeling MVPA |
| title | Neural dynamics underlying the acquisition of distinct auditory category structures |
| title_full | Neural dynamics underlying the acquisition of distinct auditory category structures |
| title_fullStr | Neural dynamics underlying the acquisition of distinct auditory category structures |
| title_full_unstemmed | Neural dynamics underlying the acquisition of distinct auditory category structures |
| title_short | Neural dynamics underlying the acquisition of distinct auditory category structures |
| title_sort | neural dynamics underlying the acquisition of distinct auditory category structures |
| topic | Auditory category learning Category structure Neural representation Spatiotemporal dynamics Computational modeling MVPA |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921008387 |
| work_keys_str_mv | AT gangyifeng neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT zhenzhonggan neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT hangyolyi neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT shawnwell neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT caseylroark neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT suipingwang neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT patrickcmwong neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures AT bharathchandrasekaran neuraldynamicsunderlyingtheacquisitionofdistinctauditorycategorystructures |