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Publication Detail
Auditory Pattern Detection
Abstract
The work presented in this doctoral thesis uses behavioural methods and neuroimaging to investigate how human listeners detect patterns and statistical regularities in complex sound sequences. Temporal pattern analysis is essential to sensory processing, especially listening, since most auditory signals only have meaning as sequences over time. Previous evidence suggests that the brain is sensitive to the statistics of sensory stimulation. However, the process through which this sensitivity arises is largely unknown. This dissertation is organised as follows: Chapter 1 reviews fundamental principles of auditory scene analysis and existing models of regularity processing to constrain the scientific questions being addressed. Chapter 2 introduces the two neuroimaging techniques used in this work, magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI). Chapters 3-6 are experimental sections. In Chapter 3, a novel stimulus is presented that allows probing listeners’ sensitivity to the emergence and disappearance of complex acoustic patterns. Pattern detection performance is evaluated behaviourally, and systematically compared with the predictions of an ideal observer model. Chapters 4 and 5 describe the brain responses measured during processing of those complex regularities using MEG and fMRI, respectively. Chapter 6 presents an extension of the main behavioural task to the visual domain, which allows pattern detection to be compared in audition and vision. Chapter 7 concludes with a general discussion of the experimental results and provides directions for future research. Overall, the results are consistent with predictive coding accounts of perceptual inference and provide novel neurophysiological evidence for the brain's exquisite sensitivity to stimulus context and its capacity to encode high-order structure in sensory signals.
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