Neural prediction of higher-order auditory sequence statistics.

Abstract:

: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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Furl N,Kumar S,Alter K,Durrant S,Shawe-Taylor J,Griffiths TD

doi

10.1016/j.neuroimage.2010.10.038

subject

Has Abstract

pub_date

2011-02-01 00:00:00

pages

2267-77

issue

3

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(10)01330-3

journal_volume

54

pub_type

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