My research explores how humans perceive, learn from, and make decisions under different types of uncertainty. I approach this through behavioural experiments, computational modelling, and EEG, with a particular focus on neurodevelopmental conditions and mental health.
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Learning under uncertainty
My doctoral research examines how individuals on the autism spectrum perceive and respond to different forms of uncertainty, such as outcome noise (expected uncertainty) and environmental volatility (unexpected uncertainty). Using behavioural experiments, reinforcement-learning and Bayesian models, and EEG, I investigate the underlying neural mechanisms and compare autistic and neurotypical adults, as well as individuals with sub-clinical autistic and anxiety-related traits. This work aims to advance our understanding of adaptive decision-making in complex, uncertain environments.
Information sampling & optimal stopping
I also investigate when it is optimal to stop gathering information and commit to a decision. In best-choice and information-sampling tasks, I use partially observable Markov decision processes (POMDPs) and EEG to uncover the cognitive and neural processes involved. Pre-registered studies I am currently involved with can be found can be found here and here.
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