Use this URL to cite or link to this record in EThOS:
Title: Neurocomputational accounts of choice variability and affect during decision-making
Author: Chew, Jie Han Benjamin
ISNI:       0000 0004 8508 437X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2020
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Humans exhibit surprising variability in behaviour, often making different choices under identical conditions. While the outcomes of these choices typically lead to explicit rewards that have been shown to influence subsequent affective states, less well understood is how the brain represents rewards that are intrinsically meaningful to an individual. The first part of this thesis examines the contributions of endogenous fluctuations in brain activity to behaviour. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes but it remains unknown whether such fluctuations also influence high-level cognitive processes including decision making. Using a novel application of real-time functional magnetic resonance imaging, I find that low pre-stimulus brain activity lead to increased occurrences of risky choice. Using computational modeling, I show that greater risk taking is explained by enhanced phasic responses to offers in a decision network. These findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex behaviour. I then examine how the neuroanatomy of the brain in the form of tissue microstructure relates to risk preferences by leveraging on in vivo histology using magnetic resonance imaging. The second part of this thesis investigates how experienced events, such as rewards received following choice, are aggregated into affective states. Despite their relevance to ideas like goal-setting and well-being, little is known about the impact of intrinsic rewards on affective states and their representation in the brain. A reinforcement learning task incorporating a skilled performance component that did not influence payment was developed to examine this. Computational modeling revealed that momentary happiness depended on past extrinsic rewards and also intrinsic rewards related to the experience of successful skilled performance. Individuals for whom intrinsic rewards more strongly influence momentary happiness exhibit stronger ventromedial prefrontal cortex responses for successful skilled performance. These findings show that the ventromedial prefrontal cortex represents the subjective value of intrinsic rewards, and that computational models of mood dynamics provide a tool that can be used to measure implicit values of abstract goods and experiences.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available