Can we recreate the complexity of psychiatric disorders, such as anxiety or depression, in animals other than humans? Although this would be immensely valuable for both theoretical and applied research, it is a tough bet. How about behavioural tests in which animals act in ways that we could reasonably interpret as anxiety- or depression-like? This has been the approach of translational psychiatry for the past couple of decades, but its clinical utility has been disappointing, given the limited success rates of clinical trials for novel psychiatric medicines. The reality is that psychiatric medicines, and central nervous system (CNS) drugs more broadly, are significantly more likely to fail at late-stage clinical trials compared with non-CNS drugs, and too often due to lack of efficacy (Hyman, 2012). This points to failures of the preclinical (i.e., nonhuman) models of psychiatric disorders used during early stages of drug development in predicting results in the clinic. The verdict may therefore be that we cannot even mimic anxiety- or depression-like symptoms in animals. But what if we didn’t have to?

When assessing the validity of translational models, construct validity is a key criterion. This means that translational models should map onto the same construct as in the human disease/function. The target constructs in psychiatry have traditionally been observable symptoms in human patients, and so preclinical models were developed to emulate these symptoms. However, as Neville et al. discuss, psychiatric conditions are more and more understood as computational processes, and there is a growing push to attempt to treat these conditions by targeting their underlying neurocognitive mechanisms, rather than their clinical presentation/observable symptoms. This shift in focus could be mirrored in translational models; the renewed target constructs should be the core computations that give rise to symptoms instead of symptom-analogues (Redish et al., 2022).

This change in perspective has important implications for the way that we evaluate model validity. Computational psychiatry assumes that symptoms are a product of disease-relevant computational mechanisms, but the same mechanisms elicited in nonhuman species may not necessarily result in behaviours that resemble human symptoms. Conversely, animal behaviour that is outwardly similar to human symptoms may be a result of entirely different computational mechanisms. Therefore, thinking more computationally, and by extension mechanistically, means reassessing existing translational models to ensure that they are computationally aligned with human psychiatric processes or developing new ones with this explicit intention. This would potentially target our focus only on disease-relevant mechanisms (i.e., signal) over irrelevant ones (noise)—an important step towards improving the predictive validity of preclinical assays and the effectiveness of psychiatric drug development pipelines.