title: Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns.
abstract: We use behavioral methods, magnetoencephalography, and functional MRI to investigate how human listeners discover temporal patterns and statistical regularities in complex sound sequences. Sensitivity to patterns is fundamental to sensory processing, in particular in the auditory system, because most auditory signals only have meaning as successions over time. Previous evidence suggests that the brain is tuned to the statistics of sensory stimulation. However, the process through which this arises has been elusive. We demonstrate that listeners are remarkably sensitive to the emergence of complex patterns within rapidly evolving sound sequences, performing on par with an ideal observer model. Brain responses reveal online processes of evidence accumulation--dynamic changes in tonic activity precisely correlate with the expected precision or predictability of ongoing auditory input--both in terms of deterministic (first-order) structure and the entropy of random sequences. Source analysis demonstrates an interaction between primary auditory cortex, hippocampus, and inferior frontal gyrus in the process of discovering the regularity within the ongoing sound sequence. The results are consistent with precision based predictive coding accounts of perceptual inference and provide compelling neurophysiological evidence of the brain's capacity to encode high-order temporal structure in sensory signals.
authors.affiliation: Ear Institute, University College London, London WC1X 8EE, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom; n.barascud@ucl.ac.uk m.chait@ucl.ac.uk.
authors.forename: Nicolas
authors.identifier: http://orcid.org/0000-0003-1495-561X
authors.initials: N
authors.lastname: Barascud
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary, University of London, London E1 4NS, United Kingdom;
authors.forename: Marcus T
authors.initials: MT
authors.lastname: Pearce
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Medical School, Newcastle University, Newcastle-upon-Tyne NE2 4HH, United Kingdom.
authors.forename: Timothy D
authors.initials: TD
authors.lastname: Griffiths
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom;
authors.forename: Karl J
authors.identifier: http://orcid.org/0000-0001-7984-8909
authors.initials: KJ
authors.lastname: Friston
authors.affiliation: Ear Institute, University College London, London WC1X 8EE, United Kingdom; n.barascud@ucl.ac.uk m.chait@ucl.ac.uk.
authors.forename: Maria
authors.initials: M
authors.lastname: Chait
mesh_terms: D000162:Acoustics; D001921:Brain / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography; D011601:Psychophysics
keywords: MEG; MMN; fMRI; pattern detection; statistical learning