Computational psychopharmacology: a translational and pragmatic approach

Psychopharmacology needs novel quantitative measures and theoretical approaches based on computational modelling that can be used to help translate behavioural findings from experimental animals to humans, including patients with neuropsychiatric disorders.

This brief review exemplifies this approach when applied to recent published studies of the effects of manipulating central dopaminergic and serotoninergic systems in rodents and marmoset monkeys, and possible comparisons with healthy human volunteers receiving systemic agents or patients with depression and schizophrenia.

Behavioural effects of central depletions of dopamine or serotonin in monkeys in probabilistic learning paradigms are characterised further by computational modelling methods and related to rodent and human data.

Several examples are provided of the power of computational modelling to derive new measures and reappraise conventional explanations of regional neurotransmitter depletion and other drug effects, whilst enhancing construct validation in patient groups. Specifically, effects are shown on such parameters as 'stimulus stickiness' and 'side stickiness', which occur over and above effects on standard parameters of reinforcement learning, reminiscent of some early innovations in data analysis in psychopharmacology.

Computational modelling provides a useful methodology for further detailed analysis of behavioural mechanisms that are affected by pharmacological manipulations across species and will aid the translation of experimental findings to understand the therapeutic effects of medications in neuropsychiatric disorders, as well as facilitating future drug discovery.