Computational Psychiatry: towards a mathematically informed understanding of mental illness

Abstract

Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.

Keywords: COGNITION; DEPRESSION; PSYCHIATRY; PSYCHOPHARMACOLOGY; SCHIZOPHRENIA.

Figure 1

A hierarchical generative model, illustrated using the ‘beads’ or ‘urn’ task. On the left, two jars are hidden behind a screen, one containing mostly green and some red balls, the other the converse. A sequence of balls is being drawn from one of these jars, in view of an observer, who is asked to guess from which jar they are coming. We have illustrated a simple hierarchical generative model of this process on the right: the observer is using such a model to make his/her guess. Variables in shaded circles are observed, and variables in unshaded circles are ‘hidden’ (ie, part of the model only). At the bottom of the model is x₁, the colour of the currently observed bead. Uncertainty about this quantity (eg, if the light is low or if the participant is colour-blind) is denoted as ω₁. At the next level of the model is x₂, the belief about the identity of the current jar, and its associated uncertainty ω₂, known as state uncertainty or ambiguity. Another form of uncertainty, risk or outcome uncertainty (ω₀) governs the relationship between the identity of the jar and the next outcome: Even if we are sure of the jar's identity, we cannot be certain of the colour of the next bead. At the top of the model is the belief about the probability that the jars could be swapped at any time, known as volatility. We have not shown them here but this could have its own associated uncertainty, and there could be further levels above this. Last, the participant must use his/her belief about the identity of the jar to make a guess: The mapping between this belief and the response y is affected by a degree of stochasticity or decision ‘noise’, τ. In schizophrenia, there may be too much uncertainty (ie, lower precision) in higher hierarchical areas that encode states or make decisions, and an underestimation of uncertainty in lower (sensory) areas.

Figure 2

Effects of a hierarchical precision imbalance in schizophrenia. A loss of precision encoding in higher hierarchical areas would bias inference away from prior beliefs and towards sensory evidence (the likelihood), illustrated schematically in the middle panel. This single change could manifest in many ways (moving anticlockwise from left to right). (i) A loss of the ability to smoothly pursue a target moving predictably (in this plot, the patient with schizophrenia constantly falls behind the target in his eye tracking, and has to saccade to catch up again); when the target is briefly stabilised on his retina (to reveal the purely predictive element of pursuit), shown as the red unbroken line, his/her eye velocity drops very significantly (figure adapted from Hong et al76). (ii) These graphs illustrate averaged electrophysiological responses in a mismatch negativity paradigm, in which a series of identical tones is followed by a deviant (oddball) tone; in the control subject, the oddball causes a pronounced negative deflection at around 120 ms (blue circle), but in a patient with schizophrenia, there is no such deflection (red circle); that is, the brain responses to predictable and unpredictable stimuli are very similar (figure adapted from Turetsky et al71). (iii) The physiological change underlying the precision imbalance is a relative decrease in synaptic gain in high hierarchical areas, and a relative increase in lower hierarchical areas. This change would also manifest as an alteration in connectivity, shown here as significant whole brain differences in connectivity with a thalamic seed between controls and patients with schizophrenia; red/yellow areas are more strongly coupled in those with schizophrenia, and include primary sensory areas (auditory, visual, motor and somatosensory); blue areas are more weakly coupled, and include higher hierarchical areas (medial and lateral prefrontal cortex, cingulate cortex and hippocampus) and the striatum (figure adapted from Anticevic et al72). (iv) An imbalance in hierarchical precision may lead to a failure to attenuate the sensory consequences of one's own actions, here illustrated by the force-matching paradigm used to measure this effect. In this paradigm, the participant must match a target force by either pressing on a bar with their finger (below) or using a mechanical transducer (top): Control subjects tend to exert more force than necessary in the former condition, but patient with schizophrenia do not (figure adapted from Pareés et al119). (v) A loss of the precision of prior beliefs can cause a resistance to visual illusions that rely on those prior beliefs for their perceptual effects. Control subjects perceive the face on the right as a convex face lit from below, due to a powerful prior belief that faces are convex, whereas patients with schizophrenia tend to perceive it veridically as a concave (hollow) face lit from above.