ArticleProceedings of the National Academy of Sciences of the United States of America2018
Focal versus distributed temporal cortex activity for speech sound category assignment.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Dynamic acoustic-to-categorical representations of phonemes and prosody along ventral and dorsal speech streams.Nature communications · 2026Article
- Lateralization and Time-Course of Cortical Phonological Representations during Syllable Production.eNeuro · 2023Article
- A neuronal prospect theory model in the brain reward circuitry.Nature communications · 2022Article
- Self-prioritization is supported by interactions between large-scale brain networks.The European journal of neuroscience · 2022Article
- Functional Plasticity Coupled With Structural Predispositions in Auditory Cortex Shape Successful Music Category Learning.Frontiers in neuroscience · 2022Article
- Lexical Influences on Categorical Speech Perception Are Driven by a Temporoparietal Circuit.Journal of cognitive neuroscience · 2021Article
- Selective modulation of interhemispheric connectivity by transcranial alternating current stimulation influences binaural integration.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets.BMC neuroscience · 2021Article
- Neurocognitive dynamics of near-threshold voice signal detection and affective voice evaluation.Science advances · 2020Article
- Shared and modality-specific brain regions that mediate auditory and visual word comprehension.eLife · 2020Article
- Combining predictive coding and neural oscillations enables online syllable recognition in natural speech.Nature communications · 2020Article
- Categorical Encoding of Vowels in Primary Auditory Cortex.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.Journal of neural engineering · 2020Article
- Plasticity in auditory categorization is supported by differential engagement of the auditory-linguistic network.NeuroImage · 2019Article
- Low-Frequency Oscillations Code Speech during Verbal Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech.Current biology : CB · 2019Article
- Criminal Responsibility and Neuroscience: No Revolution Yet.Frontiers in psychology · 2019Review
- Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features.PLoS biology · 2018Article
- Aligning statistical models with inference goals in the neuroscience of language: A dual-dependency taxonomy.Imaging neuroscience (Cambridge, Mass.)Article
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8 authors.
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Abstract
Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision.
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