Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.746526
Title: Computation and representation in decision making and emotion
Author: De Berker, A. O.
ISNI:       0000 0004 7224 2981
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Abstract:
This thesis deals with three components of an organism’s interactions with its environment: learning, decision making, and emotions. In a series of 5 studies, I detail relationships between these processes, and investigate the representation and computations whereby they are achieved. In the first experiment I show how subjective wellbeing is influenced by one’s own rewards and expectations, but also those of other people. Furthermore, I find that parameter estimates of empathy predict decision-making in a distinct test of economic generosity. In my second study, I ask how stressful experiences modulate subsequent learning, detailing a specific impairment in action-learning under stress which also manifests itself in altered pupillary responses. In the third, I use a hierarchical model of learning to show that subjective uncertainty in aversive contexts predicts several dimensions of acute stress responses. Furthermore, I find that individuals who show greater uncertainty-tuning in their stress responses are better at predicting the presence of threat. In the final pair of studies I ask how decision variables for value-based choice are represented in the brain. I describe the combination of quality and quantity into value estimates in humans, revealing a central role for the Anterior Cingulate Cortex in value integration using functional magnetic resonance imaging. I next characterize the neural code for value in non-human primate frontal cortex, using single-neuron data from collaborators. These two studies provide convergent evidence that the value code may be more diverse and non-linear than previously reported, potentially conferring the ability to incorporate uncertainty signals directly in the activity of value coding neurons.
Supervisor: Bestmann, S. ; Dolan, R. ; Rutledge, R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.746526  DOI: Not available
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