Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747534
Title: Models of decision making and behavioural control in computational psychiatry
Author: Mancinelli, Federico
ISNI:       0000 0004 7231 2679
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Computational psychiatry, which is a recent area of research, involves the use of statistical and computational methods to investigate human psychopathology and the brain. Here, I present models of human behaviour in tasks where subjects expressed preferences between options characterised by orthogonal amounts and odds of monetary rewards and different degrees and demands on behavioural control. We first model an experiment which examined subjects’ propensity to gamble for different odds and amounts of reward in the face of Acute Tryptophan Depletion (ATD). Our computational approach supported existing statistical evidence that specific serotonin receptor types might mediate the effects of ATD on the sensitivity of subjects to rewards. We also showed that subjects’ choices were influenced by the weighted sum of the probability and the amount, rather than by their interaction, as required by conventional prospect theory. In the remainder of the thesis, we present the design, realisation, and analysis of a novel task for humans in which choice was tied to perceived behavioural control (as measured by Hannah Levenson’s multi-dimensional Locus of Control scales). The genesis of this task was an attempt to capture aspects of controllability relevant to psychopathology in depression. In the task, the winning amounts were made explicit to subjects, but the odds, and the extent to which these depended on their decisions and efforts, were learned. We used computational modelling to interpret various measures of choice, choice evolution, and indecision in the task.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.747534  DOI: Not available
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