Neural prediction of higher-order auditory sequence statistics.
During auditory perception, we are required to abstract information from complex temporal sequences such as those in music and speech. Here, we investigated how higher-order statistics modulate the neural responses to sound sequences, hypothesizing that these modulations are associated with higher levels of the peri-Sylvian auditory hierarchy. We devised second-order Markov sequences of pure tones with uniform first-order transition probabilities. Participants learned to discriminate these sequences from random ones. Magnetoencephalography was used to identify evoked fields in which second-order transition probabilities were encoded. We show that improbable tones evoked heightened neural responses after 200 ms post-tone onset during exposure at the learning stage or around 150 ms during the subsequent test stage, originating near the right temporoparietal junction. These signal changes reflected higher-order statistical learning, which can contribute to the perception of natural sounds with hierarchical structures. We propose that our results reflect hierarchical predictive representations, which can contribute to the experiences of speech and music. Published by Elsevier Inc.
|Title:||Neural prediction of higher-order auditory sequence statistics|
|Keywords:||Magnetoencephalography (MEG), Predictive coding, Temporoparietal junction (TPJ), Statistical learning, NATURAL SOUNDS, TEMPORAL INTEGRATION, CORTICAL RESPONSES, SPEECH-PERCEPTION, CORTEX, MUSIC, BRAIN, WINDOWS, TIME, ASYMMETRY|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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