%0 Thesis %9 Doctoral %A Bedder, Rachel Louise %B UCL Queen Square Institute of Neurology %D 2021 %F discovery:10129689 %I UCL (University College London) %P 195 %T Asymmetries Between Gains and Losses in Mood and Decision Making %U https://discovery.ucl.ac.uk/id/eprint/10129689/ %X The thesis begins by exploring a large-scale data set from the smartphone application The Great Brain Experiment. I leverage this sample size to show that gambling for prospective losses (but not gains) increases throughout the day. I introduce the question of how exploring asymmetries between attitudes and responses to gains and losses may provide useful insights in the field of Computational Psychiatry. The next section of the thesis concerns mood and affective states, and their connections to decision-making. I introduce a novel paradigm: the Future Prospects Task, which allows for a comparison between how people feel about choosing between prospective gains and prospective losses, and how they feel about such prospects in the future. Computational modelling reveals that affective responses to losses are greater than responses to gains, demonstrating an affective negativity bias. It also demonstrates that the valence of future prospects has an impact on affective state, and that risky decision-making increases with proximity to positive futures, and conversely decreases in proximity to negative futures. This novel paradigm was adapted for a new smartphone application The Happiness Project and for fMRI. Some of the early pilot results for the smartphone application are presented, and their feasibility for future longitudinal testing discussed. The fMRI paradigm and hypotheses are described in the discussion chapter, as data collection was disrupted due to COVID-19. I also endeavour in the thesis to further extend our understanding of models of affective dynamics, which have become popular in the last decade. I include analyses of robustness, and highlight the statistical issues that should be taken into account with their usage %Z Copyright © The Author 2021. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.