A distributed dynamic brain network mediates linguistic tone representation and categorization

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

doi:10.1016/j.neuroimage.2020.117410

Cited by 10 publications

(11 citation statements)

References 76 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found a dorsal auditory-motor pathway consisting of the left superior temporal gyrus (STG), supramarginal gyrus (SMG), anterior inferior parietal lobule (IPLa), and precentral gyrus (PreCG) shows increasing representations of perceptual category distance. This auditory-category representational network is consistent with a broad speech processing network, spanning the frontal, temporal, and parietal lobes (Feng et al, 2021;Giraud and Poeppel, 2012;Hickok and Poeppel, 2007;Rauschecker and Scott, 2009). Among these brain regions, the STG has been demonstrated to encode multidimensional acoustic signals (including spectral and temporal modulation) that differentiate native speech categories (Arsenault and Buchsbaum, 2015;Bonte et al, 2014;Feng et al, 2016;Feng et al, 2018;Formisano et al, 2008;Mesgarani et al, 2014), which is presumably acquired slowly with the development of one's native language.…”

Section: Discussionsupporting

confidence: 70%

“…Previous studies have found that the IFG is involved in the acquisition of auditory or non-native speech categories (Lee et al, 2012;Myers, 2014;Myers and Swan, 2012) with response patterns in this region showing greater sensitivity to between-category changes and reduced sensitivity to within-category changes after training. To achieve successful learning, the IFG is hypothesized to work dynamically with the temporal cortices (e.g., STG) to form categorical representations and make overt categorization decisions (Feng et al, 2021;Myers, 2014;Myers et al, 2009;Myers and Swan, 2012). Here we demonstrate that different sub-regions of the IFG showed distinct profiles of plasticity.…”

Section: Discussionmentioning

confidence: 99%

“…www.fil.ion.ucl.ac.uk/spm/) closely following the pipeline described in previous studies (Feng et al, 2021;Feng et al, 2018;Feng et al, 2019). Here, we provide brief descriptions of these procedures.…”

Section: Spm12mentioning

confidence: 99%

See 2 more Smart Citations

Separate neural dynamics underlying the acquisition of different auditory category structures

Feng

Gan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Current models of auditory category learning argue for a rigid specialization of hierarchically organized regions that are fine-tuned to extracting and mapping acoustic dimensions to categories. We test a competing hypothesis: the neural dynamics of emerging auditory representations are driven by category structures and learning strategies. We designed a category learning experiment where two groups of learners learned novel auditory categories with identical dimensions but differing category structures: rule-based (RB) and information-integration (II) based categories. Despite similar learning accuracies, strategies and cortico-striatal systems processing feedback differed across structures. Emergent neural representations of category information within an auditory frontotemporal pathway exclusively for the II learning task. In contrast, the RB task yielded neural representations within distributed regions involved in cognitive control that emerged at different time-points of learning. Our results demonstrate that learners’ neural systems are flexible and show distinct spatiotemporal patterns that are not dimension-specific but reflect underlying category structures.SignificanceWhether it is an alarm signifying danger or the characteristics of background noise, humans are capable of rapid auditory learning. Extant models posit that novel auditory representations emerge in the superior temporal gyrus, a region specialized for extracting behaviorally relevant auditory dimensions and transformed onto decisions via the dorsal auditory stream. Using a computational cognitive neuroscience approach, we offer an alternative viewpoint: emergent auditory representations are highly flexible, showing distinct spatial and temporal trajectories that reflect different category structures.

show abstract

Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Separate neural dynamics underlying the acquisition of different auditory category structures

Feng

Gan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Nativeness in Neural Representational Structure Predicts Sound-to-Category Learning Success It has been previously demonstrated that task-general and acoustic-invariant neural representations of Mandarin tone categories for native listeners are evidenced in the superior temporal areas and inferior parietal lobule using multivariate pattern classification (Feng, Gan et al, 2018;Feng et al, 2021). While this classification approach reveals category-level representations, this analytic method cannot capture the fine representational structures underlying the neural activation patterns.…”

Section: Discussionmentioning

confidence: 99%

“…The same searchlight RSA was also conducted for native speakers for comparison. This searchlight approach has been described extensively in previous studies (Feng, Gan et al, 2018;Feng et al, 2019Feng et al, , 2021. We briefly described the approach here.…”

Section: Model-based Rsa and Searchlight Approachmentioning

confidence: 99%

Emerging Native-Similar Neural Representations Underlie Non-Native Speech Category Learning Success

Feng

Hsu

et al. 2021

Neurobiology of Language

Self Cite

View full text Add to dashboard Cite

Learning non-native phonetic categories in adulthood is an exceptionally challenging task, characterized by large inter-individual differences in learning speed and outcomes. The neurobiological mechanisms underlying the inter-individual differences in the learning efficacy are not fully understood. Here we examine the extent to which training-induced neural representations of non-native Mandarin tone categories in English listeners (n = 53) are increasingly similar to those of the native listeners (n = 33) who acquired these categories early in infancy. We particularly assess whether the neural similarities in representational structure between non-native learners and native listeners are robust neuromarkers of inter-individual differences in learning success. Using inter-subject neural representational similarity (IS-NRS) analysis and predictive modeling on two functional magnetic resonance imaging (fMRI) datasets, we examined the neural representational mechanisms underlying speech category learning success. Learners’ neural representations that were significantly similar to the native listeners emerged in brain regions mediating speech perception following training; the extent of the emerging neural similarities with native listeners significantly predicted the learning speed and outcome in learners. The predictive power of IS-NRS outperformed models with other neural representational measures. Furthermore, neural representations underlying successful learning are multidimensional but cost-efficient in nature. The degree of the emergent native-similar neural representations was closely related to the robustness of neural sensitivity to feedback in the frontostriatal network. These findings provide important insights into the experience-dependent representational neuroplasticity underlying successful speech learning in adulthood and could be leveraged in designing individualized feedback-based training paradigms that maximize learning efficacy.

show abstract

A practical guide to applying machine learning to infant EEG data

Reh²,

Mostafavi³

2022

Developmental Cognitive Neuroscience

View full text Add to dashboard Cite

A distributed dynamic brain network mediates linguistic tone representation and categorization

Cited by 10 publications

References 76 publications

Separate neural dynamics underlying the acquisition of different auditory category structures

Separate neural dynamics underlying the acquisition of different auditory category structures

Emerging Native-Similar Neural Representations Underlie Non-Native Speech Category Learning Success

A practical guide to applying machine learning to infant EEG data

Contact Info

Product

Resources

About