Computational Psychiatry is a multidisciplinary field of research at the intersection of psychiatry, neuroscience, machine learning and statistics. The aim of the field is to harness advances in these fields to advance treatments for mental illnesses. For overviews over computational psychiatry, see Huys et al., 2016 Nature Neuroscience and Huys et al. 2020 Neuropsychopharmacology

We are part of the Divison of Psychiatry and the Max Planck UCL Centre for Computational Psychiatry and Ageing Research in the Institute of Neurology at University College London.

Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter. Malamud Jolanda; Guloksuz Sinan; Winkel Ruud van; Delespaul Philippe; De Hert Marc A. F.; Derom Catherine; Thiery Evert; Jacobs Nele; Rutten Bart P. F.; Huys Quentin J. M. (2024). PLoS Computational Biology.

Mood disorders involve complex interactions between internal emotions and external factors. Dynamical systems theory suggests these interactions influence key aspects of mood disorders. The study proposes using ecological momentary assessments (EMA) with a Kalman filter framework to analyze these dynamics comprehensively.

Results show this approach outperforms standard methods, revealing that depression alters mood interactions, responses to external inputs, and mood controllability. The Kalman filter applied to EMA data offers a promising method for understanding mood dynamics in depression, potentially leading to new insights into mechanisms and treatments.

A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting. Talwar Anahita; Huys Quentin JM; Cormack Francesca; Roiser Jonathan P (2024). Cognitive, Affective, and Behavioral Neuroscience.

Attentional set shifting is the ability to change focus of attention. The CANTAB IED test measures this ability, showing wide variation in the general population. A study using a hierarchical model that learns feature values and dimension attention best explained test data. The model found that compulsive symptoms correlate with slower learning and higher bias towards the first relevant stimulus dimension. This approach offers a new way to analyze CANTAB IED data and suggests a mechanism for variation in set shifting performance and its link to compulsive symptoms.

Real-world fluctuations in motivation drive effort-based choices. Hewitt Samuel RC; Norbury Agnes; Huys Quentin J. M. and Hauser Tobias U (2024). PsyArxiv

Subjective experiences, like feeling motivated, fluctuate over time. However, we usually ignore these fluctuations when studying how feelings predict behaviour. Here, we examine whether naturalistic ups and downs in states influence the subjective value of choices.

Task-based willingness to exert effort for reward was specifically boosted when people felt more motivated. This naturalistic state-behaviour coupling was significantly strengthened in individuals with higher trait apathy. Computational modelling revealed that the fluctuations in state changed and preceded sensitivity to reward, thereby driving choices.

Our results show that typical, day-to-day fluctuations in feelings and cognition are tightly linked, and critical to understanding fundamental human behaviours in the real-world.

Hidden state inference requires abstract contextual representations in ventral hippocampus. Mishchanchuk Karyna; Gregoriou Gabrielle; Qü Albert; Kastler Alizée; Huys Quentin J.M.; Wilbrecht Linda and MacAskill Andrew F. (2024). bioRxiv.

The ability to form and utilize subjective, latent contextual representations to influence decision making is a crucial determinant of everyday life. Here we show that the CA1 area of the ventral hippocampus is necessary for mice to perform hidden state inference during a 2-armed bandit task.

These findings offer insight into how latent contextual information is used to optimize decision-making processes, and emphasize a key role of the hippocampus in hidden state inference.