This brief review examines the potential to use decision science to objectively characterize
depression. We provide a brief overview of the existing literature examining different …

In many everyday decisions, individuals choose between trialling something novel or
something they know well. Deciding when to try a new option or stick with an option that is …

Organisms learn from prediction errors (PEs) to predict the future. Laboratory studies using
small financial outcomes find that humans use PEs to update expectations and link …

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions
worldwide. Computational neuropsychiatry offers potential breakthroughs through the …

Deficits in reward learning are core symptoms across many mental disorders. Recent work
suggests that such learning impairments arise by a diminished ability to use reward history …

Background From a behavioural perspective anhedonia is defined as diminished interest in
the engagement of pleasurable activities. Despite its presence across a range of psychiatric …

Recent evidence indicates that reward value encoding in humans is highly context
dependent, leading to suboptimal decisions in some cases, but whether this computational …

The field of computational psychiatry advocates for the use of behavioral task-derived
computational measures to improve our understanding, diagnosis and treatment of …

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors
influence these illnesses, ultimately, it is the science of the brain that will lead to novel …

Aberrant reward processing and poor self-regulation have a crucial role in the development
of several adverse outcomes in youth, including mental health disorders and risky …