The neural correlates of the influence of learning on perceptual decision making

Diaz, Jessica Ann (2018) The neural correlates of the influence of learning on perceptual decision making. PhD thesis, University of Glasgow.

Abstract

Perceptual decision making involves the classification of sensory information, usually followed by an overt behavioural response. Any decision making, and perceptual decision making in particular, can be understood both theoretically and neurologically as a process of an accumulation of evidence to some threshold, at which point a commitment to a choice is made. This process can be examined in human subjects by analysing EEG data during perceptual decision making and identifying temporal components that are the neural signatures of the accumulation-to-bound decision making (see Philiastides & Sajda, 2006; Philiastides, Ratcliff, & Sajda, 2006a; Philiastides et al., 2006a; Ratcliff, Philiastides, & Sajda, 2009b). It may also be statistically modelled using sequential sampling models (see Ratcliff & Smith, 2004; Ratcliff, Gomez, & McKoon, 2004; Ratcliff & McKoon, 2008; Ratcliff & Van Dongen, 2011). Taken together, these provide us with a experimental and theoretical framework for the study of the neuroscience of human decision making. In this thesis, our aim is to address some open questions with respect to human perceptual decision making using the theoretical framework of sequential sampling models and the experimental paradigm of measuring temporal components in single-trial EEG discriminant analysis.
In Chapter 2, we will describe our studies of the role of learning on perceptual decision making. In particular, here we address competing hypotheses about the nature and location of perceptual learning in the brain. We provide evidence that perceptual learning arises from changes in higher level brain areas that are related to decision-making, rather than from perceptually earlier areas that are related to the encoding of sensory stimuli. In Chapter 3, we provide a specific mechanistic account of how learning affects perceptual decision making. This work follows from the work of others who have applied reinforcement learning theories (see Sutton & Barto, 1998) to the study of perceptual learning. In Chapter 4, we will describe our studies of the interaction of prior expectation and learning on decision making. In this study, we particularly aim to address whether prior expectation affects baseline activation or evidence accumulation in the decision making system, and how this changes with training. Here, we obtain evidence showing how the effects of prior expectation are more related to evidence accumulation rather than baseline activation. In Chapter 5, we provide sequential sampling models, particularly drift diffusion models, of the data that we’ve obtained in the main experiments described in Chapter 2 and Chapter 4. The principal results here show how learning and prior expectation primarily have their effect on perceptual decision making by increasing the rate of evidence accumulation.
Our general conclusion is that using a combination of the theoretical framework of sequential sampling models and the experimental paradigm of measuring temporal components in single-trial EEG discriminant analysis provides an effective and comprehensive means to address open questions with respect to human perceptual decision making.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Perceptual learning, decision making, reinforcement learning, computational modelling, neuroscience, EEG.
• Subjects: B Philosophy. Psychology. Religion > BF Psychology; R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Supervisor's Name: Philiastides, Dr. Marios
• Date of Award: 2018
• Depositing User: Miss Jessica Ann Diaz
• Unique ID: glathesis:2018-30795
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 21 Sep 2018 14:00
• Last Modified: 16 Nov 2018 16:49
• URI: https://theses.gla.ac.uk/id/eprint/30795

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