Investigating the role of Bayesian inference in duration perception

Baykova, Reny (2021) Investigating the role of Bayesian inference in duration perception. Doctoral thesis (PhD), University of Sussex.

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Abstract

The brain generates predictions about the world based on our prior experiences. Such phenomena have been formally quantified through the framework of Bayesian perceptual inference. The popularity of the Bayesian framework as a theory of perception has increased greatly over the years, but there are still many questions that need to be addressed before we can ascertain whether perception can be classified as truly Bayesian. In this thesis, I investigate whether time perception follows the principles of Bayesian models of perception. The main questions I focused on are how the variability of prior expectations and individual differences in the ability to perceive durations accurately influence temporal estimation. Bayesian models suggest that the magnitude of biases towards the prior would increase if the variance of the prior decreases, but to date, this prediction has not been adequately investigated. Similarly, the theory also suggests that sensory precision, observer’s ability to detect small changes in stimulus magnitude, should also affect perceptual biases, with greater sensory precision resulting in a weaker bias towards the prior. In addition, I was also interested in investigating what brain processes give rise to the perceptual biases that observers experience in magnitude estimation tasks. To do this, across different experiments, I used EEG to investigate if the brain tracks observers’ subjective experience of duration, and eye-tracking to investigate the previously proposed role of dopamine in biasing duration estimation. Finally, I also investigated to what extent prior expectations and time perception, in general, are influenced by conscious awareness. Overall, the experiments presented in this thesis aim to further our understanding of how the brain constructs our perception of time and whether Bayesian frameworks constitute a useful tool for understanding perception in general.

Item Type: Thesis (Doctoral)
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: B Philosophy. Psychology. Religion > BF Psychology > BF0311 Consciousness. Cognition
Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology
Depositing User: Library Cataloguing
Date Deposited: 02 Jul 2021 13:00
Last Modified: 02 Jul 2021 13:00
URI: http://sro.sussex.ac.uk/id/eprint/100003

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