Why has computational psychiatry yet to influence routine clinical practice? One reason may
be that it has neglected context and temporal dynamics in the models of certain mental …

To survive, organisms must effectively respond to the challenge of maintaining their
physiological integrity in the face of an ever-changing environment. Preserving this …

For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …

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

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective
sciences have proliferated rather than converged due to differing assumptions about what …

Psychiatry faces fundamental challenges with regard to mechanistically guided differential
diagnosis, as well as prediction of clinical trajectories and treatment response of individual …

Improvements in understanding the neurobiological basis of mental illness have
unfortunately not translated into major advances in treatment. At this point, it is clear that …

Bringing precision to the understanding and treatment of mental disorders requires
instruments for studying clinically relevant individual differences. One promising approach is …

Research in computational psychiatry is dominated by models of behavior. Subjective
experience during behavioral tasks is not well understood, even though it should be relevant …

Symptom expression in psychiatric conditions is often linked to altered threat perception,
however how computational mechanisms that support aversive learning relate to specific …