Andreou, L-V; (2016) Sensitivity to temporal structure supports auditory scene analysis - a psychophysics and magnetoencephalography investigation. Doctoral thesis , UCL (University College London).

Abstract

The auditory system has developed very sophisticated mechanisms to seek regularities and to extract temporal patterns in sound. This is widely acknowledged yet the mechanisms governing these processes are not fully understood. The purpose of the thesis is to systematically explore how these mechanisms work and what computations they are operating under as well as which regularities we are sensitive to and how this sensitivity plays a role in everyday listening. The thesis is divided into five chapters. In Chapter 1, I review the relevant cognitive and neuroimaging literature. In Chapter 2, I employ magnetoencephalography (MEG) to study the extent to which listeners adapt to temporal structure of acoustic sequences. I demonstrate that the auditory cortex is sensitive to the temporal structure of isochronous sequences, even when this information is not behaviourally relevant. Furthermore, I present evidence that the temporal structure of regular sequences is not learnt as precisely, however, active attending to regular sequences results in improved adaptation. In Chapter 3, I look at a very simple multi-object environment and use an objective measure of performance on a difficult pattern detection task. I show that temporal regularity facilitates stream segregation, but that the effect of temporal regularity as a cue for segregation is limited to relatively fast rates and to situations where frequency separation is insufficient for segregation. In Chapter 4, I show, in the context of a change detection task, that even in complex auditory scenes, sensitivity to temporal regularity is critical to our ability to analyse and detect changes in a dynamic soundscape. In the concluding chapter, I discuss the predictive modelling view of the auditory system and the functional role of temporal structure in auditory scene analysis.

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