Theory-driven approaches: advancing understanding of disease processes
Cognitive tasks and neuroimaging are combined with theoretical approaches in computational psychiatry to learn more about differences in behaviour, which in extremis are associated with symptoms of mental illness. Computational models are created with the aim of explaining observed behaviour (e.g. responses on tasks) in a mathematical framework. These models are composed of free variables, i.e. parameters which vary on a per individual basis and represent unobservable neurobiological processes, such as learning or decision-making. When these models are applied to imaging and to cognitive tasks, the resulting parameters can illustrate individual differences in performance at a mechanistic level, capturing the heterogeneity that is a cardinal feature of complex conditions.
Reinforcement learning models represent a promising example of this approach. Under this framework, the relationship between two modes of learning about rewards can be assessed e.g., goal-directed vs. habitual. Fitting these models to each individual’s data produces parameters, i.e., summaries of the learning process, which can be compared to reveal if there is a significant difference between two predefined groups or if novel groups exist within the population.
These models have a biological basis, and provide further mechanistic insight into the processes underlying aberrant behaviour. For example, mismatches between what is expected and what is actually observed by an individual generates prediction errors, which are encoded by dopaminergic neurons in subcortical regions [1]. Deficits in goal-directed learning, and therefore a tendency towards habits, have been found across disorders such as binge eating, addiction, social anxiety and obsessive-compulsive disorder (OCD), and have also been related to a specific transdiagnostic factor - compulsive behaviour and intrusive thought - which underlies all of these disorders [3].