The speech signal consists of a continuous stream of consonants and vowels, which must be de- and encoded in human auditory cortex to ensure the robust recognition and categorization of speech sounds. We used small-voxel functional magnetic resonance imaging to study information encoded in local brain activation patterns elicited by consonant-vowel syllables, and by a control set of noise bursts. First, activation of anterior-lateral superior temporal cortex was seen when controlling for unspecific acoustic processing (syllables versus band-passed noises, in a "classic" subtraction-based design). Second, a classifier algorithm, which was trained and tested iteratively on data from all subjects to discriminate local brain activation patterns, yielded separations of cortical patches discriminative of vowel category versus patches discriminative of stop-consonant category across the entire superior temporal cortex, yet with regional differences in average classification accuracy. Overlap (voxels correctly classifying both speech sound categories) was surprisingly sparse. Third, lending further plausibility to the results, classification of speech-noise differences was generally superior to speech-speech classifications, with the notable exception of a left anterior region, where speech-speech classification accuracies were significantly better. These data demonstrate that acoustic-phonetic features are encoded in complex yet sparsely overlapping local patterns of neural activity distributed hierarchically across different regions of the auditory cortex. The redundancy apparent in these multiple patterns may partly explain the robustness of phonemic representations.
- Auditory cortex
- Multivariate pattern classification
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