Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviours (STB) to provide timely and personalised interventions. Developing computational models of STB that integrate across behavioural, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalised interventions. In an endeavour to augment current diagnostic modalities for suicidal thoughts and behaviours (STBs), this research utilises computational frameworks, specifically Reinforcement Learning and Active Inference models, to decipher the underpinnings of suicidality. Integrating behavioural, cognitive, and neurobiological levels of analysis, the study seeks to formalise cognitive theories of suicide and elucidate the neural and decision-making mechanisms implicated.
