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Categorical speech representation in human superior temporal gyrus.

Chang EF, Rieger JW, Johnson K, Berger MS, Barbaro NM, Knight RT - Nat. Neurosci. (2010)

Bottom Line: We found that the neural representation of speech sounds is categorically organized in the human posterior superior temporal gyrus.Although speech-sound responses were distributed, spatially discrete cortical loci were found to underlie specific phonetic discrimination.Our results provide direct evidence for acoustic-to-higher order phonetic level encoding of speech sounds in human language receptive cortex.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA. changed@neurosurg.ucsf.edu

ABSTRACT
Speech perception requires the rapid and effortless extraction of meaningful phonetic information from a highly variable acoustic signal. A powerful example of this phenomenon is categorical speech perception, in which a continuum of acoustically varying sounds is transformed into perceptually distinct phoneme categories. We found that the neural representation of speech sounds is categorically organized in the human posterior superior temporal gyrus. Using intracranial high-density cortical surface arrays, we found that listening to synthesized speech stimuli varying in small and acoustically equal steps evoked distinct and invariant cortical population response patterns that were organized by their sensitivities to critical acoustic features. Phonetic category boundaries were similar between neurometric and psychometric functions. Although speech-sound responses were distributed, spatially discrete cortical loci were found to underlie specific phonetic discrimination. Our results provide direct evidence for acoustic-to-higher order phonetic level encoding of speech sounds in human language receptive cortex.

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Correlation of neurometric and psychometric category boundariesPeak encoding at 110–150ms. A. Left, Comparison of neuronal (dark) and psychophysical (light/dashed) -derived identification functions. Neurometric identification functions were determined by using the MDS distance between each stimulus position and the three cluster means. Middle, Correlation between neurometric and psychometric identification functions (Pearson’s correlation, 0.92 for /ba/, 0.98 for /da/, and 0.92 for the /ga/ category; dotted line: threshold of corrected p-value at 0.05. Right, Comparison of neural (red) and psychophysical (black/dashed) discrimination functions. The neurometric discrimination functions were derived from the distance of the stimulus responses in MDS space. At 110 ms both the position of the maxima and the general shape of the neurometric function correlate well with the psychometric function. (r=0.66, p<0.05). Early (0–40ms, B) and late (180–220ms, C) epoch field potentials demonstrate poor correlation between neural and psychophysical results (see insets).
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Figure 3: Correlation of neurometric and psychometric category boundariesPeak encoding at 110–150ms. A. Left, Comparison of neuronal (dark) and psychophysical (light/dashed) -derived identification functions. Neurometric identification functions were determined by using the MDS distance between each stimulus position and the three cluster means. Middle, Correlation between neurometric and psychometric identification functions (Pearson’s correlation, 0.92 for /ba/, 0.98 for /da/, and 0.92 for the /ga/ category; dotted line: threshold of corrected p-value at 0.05. Right, Comparison of neural (red) and psychophysical (black/dashed) discrimination functions. The neurometric discrimination functions were derived from the distance of the stimulus responses in MDS space. At 110 ms both the position of the maxima and the general shape of the neurometric function correlate well with the psychometric function. (r=0.66, p<0.05). Early (0–40ms, B) and late (180–220ms, C) epoch field potentials demonstrate poor correlation between neural and psychophysical results (see insets).

Mentions: To evaluate how well the neural pattern correlated to the psychophysical behavior, neurometric identification functions for each phonetic category were plotted using the normalized distance in MDS space between each stimulus position and the three cluster means. This revealed a similar appearance to the psychometric identification functions, with steep boundaries occurring between phoneme categories (Pearson’s correlation, r>0.9 for each function at 110 ms intervals start; p<0.05; Fig. 3a, Supplementary Figure 4). A neurometric discrimination function was also derived from distances between individual stimulus positions in MDS space. This also achieved good correlation with the psychometric functions for discrimination (Pearson’s correlation, r=0.66 at 110 ms intervals start; p<0.05; Fig. 3b). More importantly, we observed good correspondence between the two neurometric functions: the peaks of the discrimination occur for the same stimuli as the steepest parts of the identification, thus fulfilling the criterion for neural categorical organization. This organized representation was transient, spanning the neuronal response from 110–160 ms.


Categorical speech representation in human superior temporal gyrus.

Chang EF, Rieger JW, Johnson K, Berger MS, Barbaro NM, Knight RT - Nat. Neurosci. (2010)

Correlation of neurometric and psychometric category boundariesPeak encoding at 110–150ms. A. Left, Comparison of neuronal (dark) and psychophysical (light/dashed) -derived identification functions. Neurometric identification functions were determined by using the MDS distance between each stimulus position and the three cluster means. Middle, Correlation between neurometric and psychometric identification functions (Pearson’s correlation, 0.92 for /ba/, 0.98 for /da/, and 0.92 for the /ga/ category; dotted line: threshold of corrected p-value at 0.05. Right, Comparison of neural (red) and psychophysical (black/dashed) discrimination functions. The neurometric discrimination functions were derived from the distance of the stimulus responses in MDS space. At 110 ms both the position of the maxima and the general shape of the neurometric function correlate well with the psychometric function. (r=0.66, p<0.05). Early (0–40ms, B) and late (180–220ms, C) epoch field potentials demonstrate poor correlation between neural and psychophysical results (see insets).
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Figure 3: Correlation of neurometric and psychometric category boundariesPeak encoding at 110–150ms. A. Left, Comparison of neuronal (dark) and psychophysical (light/dashed) -derived identification functions. Neurometric identification functions were determined by using the MDS distance between each stimulus position and the three cluster means. Middle, Correlation between neurometric and psychometric identification functions (Pearson’s correlation, 0.92 for /ba/, 0.98 for /da/, and 0.92 for the /ga/ category; dotted line: threshold of corrected p-value at 0.05. Right, Comparison of neural (red) and psychophysical (black/dashed) discrimination functions. The neurometric discrimination functions were derived from the distance of the stimulus responses in MDS space. At 110 ms both the position of the maxima and the general shape of the neurometric function correlate well with the psychometric function. (r=0.66, p<0.05). Early (0–40ms, B) and late (180–220ms, C) epoch field potentials demonstrate poor correlation between neural and psychophysical results (see insets).
Mentions: To evaluate how well the neural pattern correlated to the psychophysical behavior, neurometric identification functions for each phonetic category were plotted using the normalized distance in MDS space between each stimulus position and the three cluster means. This revealed a similar appearance to the psychometric identification functions, with steep boundaries occurring between phoneme categories (Pearson’s correlation, r>0.9 for each function at 110 ms intervals start; p<0.05; Fig. 3a, Supplementary Figure 4). A neurometric discrimination function was also derived from distances between individual stimulus positions in MDS space. This also achieved good correlation with the psychometric functions for discrimination (Pearson’s correlation, r=0.66 at 110 ms intervals start; p<0.05; Fig. 3b). More importantly, we observed good correspondence between the two neurometric functions: the peaks of the discrimination occur for the same stimuli as the steepest parts of the identification, thus fulfilling the criterion for neural categorical organization. This organized representation was transient, spanning the neuronal response from 110–160 ms.

Bottom Line: We found that the neural representation of speech sounds is categorically organized in the human posterior superior temporal gyrus.Although speech-sound responses were distributed, spatially discrete cortical loci were found to underlie specific phonetic discrimination.Our results provide direct evidence for acoustic-to-higher order phonetic level encoding of speech sounds in human language receptive cortex.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA. changed@neurosurg.ucsf.edu

ABSTRACT
Speech perception requires the rapid and effortless extraction of meaningful phonetic information from a highly variable acoustic signal. A powerful example of this phenomenon is categorical speech perception, in which a continuum of acoustically varying sounds is transformed into perceptually distinct phoneme categories. We found that the neural representation of speech sounds is categorically organized in the human posterior superior temporal gyrus. Using intracranial high-density cortical surface arrays, we found that listening to synthesized speech stimuli varying in small and acoustically equal steps evoked distinct and invariant cortical population response patterns that were organized by their sensitivities to critical acoustic features. Phonetic category boundaries were similar between neurometric and psychometric functions. Although speech-sound responses were distributed, spatially discrete cortical loci were found to underlie specific phonetic discrimination. Our results provide direct evidence for acoustic-to-higher order phonetic level encoding of speech sounds in human language receptive cortex.

Show MeSH
Related in: MedlinePlus