Economides, MW; (2015) Multiple value systems for adaptive decision-making. Doctoral thesis , UCL (University College London).

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Abstract

Values, rewards, uncertainty and risk play a central role in economic and psychological theories of decision-making. Over the past decade, numerous experiments have used neuroimaging techniques to uncover the neural realization of such decision variables while individuals engage in a range of tasks. These have led to a consensus that economic choice involves interplay between multiple systems that enjoy both cooperative and competitive relations. In this thesis, I utilize functional magnetic resonance imaging (fMRI) and computational formalizations of choice to explore how these different brain systems interact to support adaptive decision-making. In Chapters 4 and 5, I present data from a task in which the inclusion of a dynamic environment required subjects to sometimes approach an option they would normally avoid, or avoid an option they would normally approach. This allowed me to uncover brain systems that track time-varying components of the environment, or immediate reward information, as well as the mechanisms by which these components are integrated. I found that adaptive control in this context involves downstream integration, via functional coupling, of distinct decision components that are computed in separate, often widespread, networks. Yet, choice variables represented in the striatum may in some cases be resistant to modulation, contributing to maladaptive behaviour. In Chapter 6, I investigate whether task training alters the way in which these different value systems manifest in choice; or more broadly, whether value computations in the brain adapt as humans become more proficient at internalizing models of the world. To address this, I trained subjects on a value-guided decision-making task for 3 consecutive days. The data are suggestive of a shift in the implementation of value-guided planning with training, from a more cumbersome, resource-dependant mechanism, to a more efficient and robust process that remains resistant to attentional load.

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