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Personal profile


Daniel studies mood, learning, and decision making. He uses computational methods (e.g., reinforcement learning models of behaviour, multivariate pattern analysis of neural data) to understand how these phenomena interact. He is particularly interested in investigating the ways that interactions between mood, learning, and decision making might go awry in psychiatric conditions like major depression and bipolar disorder.

Education/Academic qualification

Psychology, PhD, University of Melbourne

Award Date: 5 Apr 2017

Research area keywords

  • mood
  • mood disorders
  • Affective science
  • learning
  • decision making
  • Computational Modelling


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