Individual differences in computational psychiatry: A review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice. •Emerging evidence shows many computational measures to have poor psychometric properties.•This poses a risk of undermining the use of these measures for studying mental disorders.•This is largely a consequence of over-relying on cross-sectional single-task study designs.•To move forward, the field needs to embrace longitudinal designs with batteries of tasks.•The development of assays should be guided by a long-term vision of their applications.