Evidence map›Paper›PMID 34543762›Full record

ArticleNeuroImage2021

Neural dynamics underlying the acquisition of distinct auditory category structures.

Gangyi Feng, Zhenzhong Gan, Han Gyol Yi, Shawn W Ell, Casey L Roark, Suiping Wang, Patrick C M Wong, Bharath Chandrasekaran

Abstract read
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. The nature of non-native speech sound representations.The Journal of the Acoustical Society of America · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Gangyi FengDepartment 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. Electronic address: g.feng@cuhk.edu.hk.
Zhenzhong GanDepartment 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, China.
Han Gyol YiDepartment of Neurological Surgery, University of California, San Francisco, CA 94158, United States.
Shawn W EllDepartment of Psychology, Graduate School of Biomedical Sciences and Engineering, University of Maine, 5742 Little Hall, Room 301, Orono, ME 04469-5742, United States.
Casey L RoarkDepartment 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.
Suiping WangKey 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, China.
Patrick C M WongDepartment 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.
Bharath ChandrasekaranDepartment 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. Electronic address: b.chandra@pitt.edu.

Funding

Supplement to cortical contributions to frequency-following response generation and modulationR01DC013315 · NIDCD · UNIVERSITY OF TEXAS AT AUSTIN · PI ABEL, TAYLOR JOHN, CHANDRASEKARAN, BHARATH · 2014 to 2025
$5.5M
Neural Systems in Auditory and Speech CategorizationR01DC015504 · NIDCD · UNIVERSITY OF TEXAS AT AUSTIN · PI CHANDRASEKARAN, BHARATH · 2017 to 2021
$2.3M
Shared and specific mechanisms of auditory and visual category learningF32DC018979 · NIDCD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ROARK, CASEY L · 2020 to 2021
$131k
NIDCD NIH HHS F32 DC018979NIDCD NIH HHS R01 DC013315
6 · The paper itself

Abstract

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.

Indexed as

Acoustic StimulationAdolescentAdultAuditory CortexAuditory PathwaysAuditory PerceptionBrain MappingFemaleHumansLearningMagnetic Resonance ImagingMalePrefrontal CortexSoundTemporal LobeYoung AdultAuditory category learningCategory structureComputational modelingMVPANeural representationSpatiotemporal dynamics

Identifiers

PMID34543762
PMCPMC8785192

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

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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.