The computational psychiatry of reward: broken brains or misguided minds?

Abstract

Research into the biological basis of emotional and motivational disorders is in danger of riding roughshod over a patient-centered psychiatry and falling into the dualist errors of the past, i.e., by treating mind and brain as conceptually distinct. We argue that a psychiatry informed by computational neuroscience, computational psychiatry, can obviate this danger. Through a focus on the reasoning processes by which humans attempt to maximize reward (and minimize punishment), and how such reasoning is expressed neurally, computational psychiatry can render obsolete the polarity between biological and psychosocial conceptions of illness. Here, the term ‘psychological’ comes to refer to information processing performed by biological agents, seen in light of underlying goals. We reflect on the implications of this perspective for a definition of mental disorder, including what is entailed in asserting that a particular disorder is ‘biological’ or ‘psychological’ in origin. We propose that a computational approach assists in understanding the topography of mental disorder, while cautioning that the point at which eccentric reasoning constitutes disorder often remains a matter of cultural judgment.

Computation in Psychotherapy, or How Computational Psychiatry Can Aid Learning-Based Psychological Therapies

“…Patients and therapists can have maladaptive beliefs about each other and the therapy itself, potentially undermining the patient–therapist relationship (Bevington, Fuggle, & Fonagy, 2015 ). Here a computational framework can help by offering an intuitive, validating, scientifically based model of deviant behavior to both patients and therapists (Moutoussis, Eldar, & Dolan, 2016 ) while focusing on needs, not pathologies (Moutoussis, Story, & Dolan, 2015 ). Second, therapies can have side effects and fail through causing harm.…”

Reward Processing in Motivational and Affective Disorders

“…We argue that the application of this approach to psychiatric disorders, the bedrock of the emerging field of computational psychiatry Wang and Krystal, 2014), can help to bridge a gap between psychological and biological conceptions of mental ill health (for further discussion see Moutoussis et al, 2015).…”

A working memory intervention weakens the reconsolidation of a threat memory and its biases processing towards threat

“…attentional bias) and the valuation systems (i.e. overestimation of threat and their consequences), which commonly underlie sub-optimal decision making in anxiety disorders (37,38).…”