Benji’s Reviews > Computational Psychiatry: A Primer > Status Update

Computational assays, such as those based on Bayesian approaches, could help diagnostic tests. Computational psychiatry could also help psychotherapy: psychotherapy being a learning process, it may benefit from the rich computational understanding of learning processes.

Data-driven and theory-driven approaches are not incompatible: theory-driven models can provide descriptions that efficiently summarize complex data, and these summaries can provide inputs for machine-learning algorithms. The combination of both methods has been found to outperform data-driven approaches alone.

Females learn better from rewards than males do - a finding that is consistent with higher striatal presynaptic dopamine synthesis capacity and possible higher striatal dopaminergic innervations, as assessed by dopamine transporter binding, in females relative to males.

Progress in realizing the clinical benefits of computational studies will require a broader range of study designs to be implemented in the future. Studies to find associations between computationally defined processes and treatment response. Studies to pinpoint causality to allow for cognitive and pharmacological interventions.

Phasic activity of the central norepinephrine system may contain an estimate of volatility or unexpected uncertainty, consistent with current theories on the broader role of norepinephrine, which is argued to increase the gain of sensory representations and thus increase their impact on behavior.

Learning about a volatile process is more efficiently achieved with a higher learning rate. Humans estimate the volatility of the process they are learning about and tune their learning rate to increase learning efficiency.

While finding the optimal approach to generalization and credit assignment is a core question tackled by the machine-learning literature, to date fear-conditioning studies in humans that examine generalization have tended to rely on summary statistics rather computational approaches.

Reinforcement learning has been described as especially promising in this regard and has indeed shown potential for classification of depression from purely behavioral data without the need for (subjective) questionnaires.

An important aim for computational psychiatry is the development of computational assays that can be used to separate patients into subgroups, generate treatment recommendations, and make predictions for the outcome of those treatments.

This suggests that model parameters obtained by fitting a behavioral task, such as probabilistic learning task, could be used to classify MDD to a high accuracy. The classification of diseases is an important goal of computational psychiatry.