Hu, Mingyue; (2024) Uncovering the Brain Mechanisms that Underlie the Detection of Patterns in Sound Sequences. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The auditory system is wired to be sensitive to patterns in sound, this ability is crucial for the brain to comprehend and interact with its environment. Despite the pivotal role of these processes, the underlying neural mechanisms remain poorly understood. This lack of clarity hinders the development of comprehensive models of auditory processing, machine intelligence, and the creation of targeted interventions for those clinical diseases involving auditory impairment. Expanding on those unsolved issues, this PhD thesis explores several questions surrounding the cognitive processes and neural mechanisms underpinning the auditory system's sensitivity to patterns in human listeners. Chapter 2 investigates the constraints of auditory memory in pattern recognition. In this behavioural study, participants engaged in identifying emerging patterns within rapidly unfolding sound sequences. By varying the durations and informational complexities of patterns, the study assessed whether memory integration is primarily duration-dependent or also configured to monitor discrete items. Chapter 3 explored the neural mechanisms that support pattern detection. Participants passively listened to predictable and unpredictable sound sequences while their brain responses were recorded with magnetoencephalography (MEG). By analysing the time domain signal and localising the neural sources, the study is investigating how the brain is representing the predictable sensory signals. Chapter 4 addresses the challenges in detecting patterns when sound sequence is slowed down, examining how auditory short- term memory, sustained attention, frequency discrimination, and task engagement influence detection performance. Buildingon the findings of Chapter 4, Chapter 5 employed Electroencephalography (EEG) to further explore the correlation between short-term memory abilities and implicit pattern detection. Participants listened passively to sound patterns of 5500ms, and both EEG signals and behavioural performance data were analysed. Overall, the results from this thesis align with predictive coding theory and provide novel insights into the neural underpinnings of perceptual and cognitive processes underlying auditory pattern detection.

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