(1) Computational Modelling of Reinforcement Learning in Hypomania-Prone Individuals and Visualisation
Investigating how hypomania-proneness and acute mood fluctuations influence reinforcement learning. Using data from a probabilistic selection task, the project explores the interaction between trait vulnerability (HPS scores) and state mood manipulations on reward/punishment sensitivity using data from Liam Mason and Colleagues (UCL).
Analysis of a human behavioural experiment investigating whether different experimental manipulations can help participants overcome an initial bias in estimating reward probabilities. Participants were biased (via video) to believe Option A had higher reward probability than Option B, when in reality both had equal probability (75%).
Analysis of a working memory experiment examining whether unfilled gaps between items provide retroactive benefits (better memory for items before the gap) or proactive benefits (better memory for items after the gap) in a serial recall task.