Humans do not integrate new information objectively: outcomes carrying a positive affective
value and evidence confirming one's own prior belief are overweighed. Until recently …

In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the
information needed to precisely estimate the probability and value of potential outcomes as …

Importance Computational psychiatry studies have investigated how reinforcement learning
may be different in individuals with mood and anxiety disorders compared with control …

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence,
it is important to understand the biases present in their outputs in order to avoid perpetuating …

Computational psychiatry is a rapidly growing field attempting to translate advances in
computational neuroscience and machine learning into improved outcomes for patients …

Determining how environments shape how people learn is central to understanding
individual differences in goal-directed behaviour. Studies of the effects of early-life adversity …

Our knowledge about the state of the world is often incomplete, making it difficult to select
the best course of action. One strategy that can be used to improve our ability to make …

Previous research has stressed the importance of uncertainty for controlling the speed of
learning, and how such control depends on the learner inferring the noise properties of the …

People radically differ in how they cope with uncertainty. Clinical researchers describe a
dispositional characteristic known as “intolerance of uncertainty”, a tendency to find …

Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting
probabilistic decision-making to second-order uncertainty might reflect a core deficit that cuts …