Evidence map›Paper›PMID 33011415›Full record

ArticleNeuroImage2021

A distributed dynamic brain network mediates linguistic tone representation and categorization.

Gangyi Feng, Zhenzhong Gan, Fernando Llanos, Danting Meng, Suiping Wang, Patrick C M Wong, Bharath Chandrasekaran

Open access · goldAbstract 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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
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

6 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Neural Fingerprints Underlying Individual Language Learning Profiles.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
    Article
  5. Article
  6. Article
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

7 authors at 3 institutions in 3 countries.

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 GanCenter for the Study of Applied Psychology and School of Psychology, South China Normal University, Guangzhou 510631, China.
Fernando LlanosDepartment of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, United States.
Danting MengCenter for the Study of Applied Psychology and School of Psychology, South China Normal University, Guangzhou 510631, China.
Suiping WangCenter for the Study of Applied Psychology and School of Psychology, South China Normal University, Guangzhou 510631, China; Guangdong Provincial 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. Electronic address: b.chandra@pitt.edu.
South China Normal University · CNChinese University of Hong Kong · HKUniversity of Pittsburgh · US

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
NIDCD NIH HHS R01 DC013315NIDCD NIH HHS R01 DC015504
6 · The paper itself

Abstract

Successful categorization requires listeners to represent the incoming sensory information, resolve the "blooming, buzzing confusion" inherent to noisy sensory signals, and leverage the accumulated evidence towards making a decision. Despite decades of intense debate, the neural systems underlying speech categorization remain unresolved. Here we assessed the neural representation and categorization of lexical tones by native Mandarin speakers (N = 31) across a range of acoustic and contextual variabilities (talkers, perceptual saliences, and stimulus-contexts) using functional magnetic imaging (fMRI) and an evidence accumulation model of decision-making. Univariate activation and multivariate pattern analyses reveal that the acoustic-variability-tolerant representations of tone category are observed within the middle portion of the left superior temporal gyrus (STG). Activation patterns in the frontal and parietal regions also contained category-relevant information that was differentially sensitive to various forms of variability. The robustness of neural representations of tone category in a distributed fronto-temporoparietal network is associated with trial-by-trial decision-making parameters. These findings support a hybrid model involving a representational core within the STG that operates dynamically within an extensive frontoparietal network to support the representation and categorization of linguistic pitch patterns.

Indexed as

AdolescentBrainFemaleFrontal LobeFunctional NeuroimagingHumansLanguageMaleParietal LobePitch PerceptionSpeech PerceptionTemporal LobeYoung AdultCategorization decisionLexical tonesNeural decodingNeural representationPerceptual constancySpeech categorization

Identifiers

PMID33011415
PMCPMC7749825
OpenAlexW3091439017

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.