When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health.
We focus on developing computational tools with real potential for clinical applications.
Ongoing studies
Latest Publications
Guennouni, Ismail; Dupret, Samuel; Huys, Quentin J. M.; Speekenbrink, Maarten
(Re)building Cooperation: Effects of a Cognitive Intervention on Cooperative Behaviour in Games Journal Article
In: Computational Psychiatry, vol. 10, no. 1, pp. 184–201, 2026, ISSN: 2379-6227.
@article{GuennouniSpeekenbrink26,
title = {(Re)building Cooperation: Effects of a Cognitive Intervention on Cooperative Behaviour in Games},
author = {Ismail Guennouni and Samuel Dupret and Quentin J. M. Huys and Maarten Speekenbrink},
url = {https://acplab.org/wp-content/uploads/publications/GuennouniSpeekenbrink26.pdf},
doi = {10.5334/cpsy.178},
issn = {2379-6227},
year = {2026},
date = {2026-09-17},
journal = {Computational Psychiatry},
volume = {10},
number = {1},
pages = {184–201},
publisher = {Ubiquity Press, Ltd.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fielder, Jennifer C.; Viding, Essi; Huys, Quentin J. M.; Steinbeis, Nikolaus
Social feedback informs generalisation of control beliefs Journal Article
In: Cognition, vol. 278, pp. 106704, 2026, ISSN: 0010-0277.
@article{FielderSteinbeis27,
title = {Social feedback informs generalisation of control beliefs},
author = {Jennifer C. Fielder and Essi Viding and Quentin J. M. Huys and Nikolaus Steinbeis},
url = {https://acplab.org/wp-content/uploads/publications/FielderSteinbeis27.pdf},
doi = {10.1016/j.cognition.2026.106704},
issn = {0010-0277},
year = {2026},
date = {2026-09-16},
journal = {Cognition},
volume = {278},
pages = {106704},
publisher = {Elsevier BV},
abstract = {Perceived control in one context can affect behaviour in novel contexts. One potentially important variable determining generalisation is how perceived control in one context shapes beliefs about the self. Typically, learned helplessness studies do not control or manipulate beliefs about the self. Here, we test whether observing others' ability to exert control helps to inform inferences about whether the controllability is primarily due to one's own ability or a feature of the current environment, and thereby determines the degree of generalisation. In an initial study (N = 200) and pre-registered replication study (N = 436) we used comparative social feedback about performance in a novel task (the Wheel Stopping task) to assess how self- or environment-specific inferences shape control beliefs. Linear mixed effects models in both studies revealed that both task controllability and social feedback uniquely predicted participants' local control beliefs (trial-by-trial), after accounting for perceived task difficulty. Additionally, in Study 2, there was a significant decrease of internal locus of control in the group given low relative feedback in the low control condition, suggesting that in low control, social feedback impacts global control beliefs. Study 2 further revealed that task controllability and feedback were not related to changes in reported self- versus task-specific attribution of control. Social feedback generalised to influence behaviour in a second task in terms of the number of actions performed but not the time taken to escape or the proportion of those who learned to escaped. These results suggest that comparative social feedback shapes local control beliefs, global control beliefs in low control scenarios, and generalises to some aspects of behaviour in a second task, but does not change reported self- or task-attribution of control.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Perceived control in one context can affect behaviour in novel contexts. One potentially important variable determining generalisation is how perceived control in one context shapes beliefs about the self. Typically, learned helplessness studies do not control or manipulate beliefs about the self. Here, we test whether observing others' ability to exert control helps to inform inferences about whether the controllability is primarily due to one's own ability or a feature of the current environment, and thereby determines the degree of generalisation. In an initial study (N = 200) and pre-registered replication study (N = 436) we used comparative social feedback about performance in a novel task (the Wheel Stopping task) to assess how self- or environment-specific inferences shape control beliefs. Linear mixed effects models in both studies revealed that both task controllability and social feedback uniquely predicted participants' local control beliefs (trial-by-trial), after accounting for perceived task difficulty. Additionally, in Study 2, there was a significant decrease of internal locus of control in the group given low relative feedback in the low control condition, suggesting that in low control, social feedback impacts global control beliefs. Study 2 further revealed that task controllability and feedback were not related to changes in reported self- versus task-specific attribution of control. Social feedback generalised to influence behaviour in a second task in terms of the number of actions performed but not the time taken to escape or the proportion of those who learned to escaped. These results suggest that comparative social feedback shapes local control beliefs, global control beliefs in low control scenarios, and generalises to some aspects of behaviour in a second task, but does not change reported self- or task-attribution of control.
Bagdades, Elena; Biria, Marjan; Burman, Charlotte; Delpech, Raphaëlle; Huys, Quentin J. M.; Moses-Payne, Madeleine; Norman, Jessica; Pizzagalli, Diego A.; Spencer, Lucienne; Tromans, Naomi; Singh, Ilina; Leigh, Eleanor; Krebs, Georgina; Stringaris, Argyris
Social prediction errors and feedback shape momentary mood and anxiety: Moderation by social anxiety and depression symptoms. Journal Article
In: Behaviour Research and Therapy, vol. 205, pp. 105145, 2026, ISSN: 1873-622X.
@article{BagdadesStringaris26,
title = {Social prediction errors and feedback shape momentary mood and anxiety: Moderation by social anxiety and depression symptoms.},
author = {Elena Bagdades and Marjan Biria and Charlotte Burman and Raphaëlle Delpech and Quentin J. M. Huys and Madeleine Moses-Payne and Jessica Norman and Diego A. Pizzagalli and Lucienne Spencer and Naomi Tromans and Ilina Singh and Eleanor Leigh and Georgina Krebs and Argyris Stringaris},
url = {https://acplab.org/wp-content/uploads/publications/BagdadesStringaris26.pdf},
doi = {10.1016/j.brat.2026.105145},
issn = {1873-622X},
year = {2026},
date = {2026-08-31},
journal = {Behaviour Research and Therapy},
volume = {205},
pages = {105145},
abstract = {Social interactions strongly influence mental wellbeing, yet the mechanisms linking them to momentary mood and anxiety remain unclear. Building on work showing that reward prediction errors (PEs) shape mood, we extend this framework to the social domain. In a preregistered study, 185 participants (ages 14-45), varying in social anxiety and depression symptoms, completed a task involving serial social interactions. We compared computational models to test how social feedback, expectations, and PEs related to momentary affect. Results showed that social feedback and PEs best explained affective fluctuations. Social PEs predicted anxiety more strongly than social feedback, while social feedback predicted mood more strongly than social PEs. Critically, higher social anxiety symptoms heightened affective responses to social feedback, whereas depressive symptoms increased sensitivity to negative PEs for mood and to social feedback for anxiety. These findings provide a computational account of social affective dynamics across development and highlight potential risk markers and treatment targets.},
keywords = {},
pubstate = {aheadofprint},
tppubtype = {article}
}
Social interactions strongly influence mental wellbeing, yet the mechanisms linking them to momentary mood and anxiety remain unclear. Building on work showing that reward prediction errors (PEs) shape mood, we extend this framework to the social domain. In a preregistered study, 185 participants (ages 14-45), varying in social anxiety and depression symptoms, completed a task involving serial social interactions. We compared computational models to test how social feedback, expectations, and PEs related to momentary affect. Results showed that social feedback and PEs best explained affective fluctuations. Social PEs predicted anxiety more strongly than social feedback, while social feedback predicted mood more strongly than social PEs. Critically, higher social anxiety symptoms heightened affective responses to social feedback, whereas depressive symptoms increased sensitivity to negative PEs for mood and to social feedback for anxiety. These findings provide a computational account of social affective dynamics across development and highlight potential risk markers and treatment targets.
Berndt, Lioba C. S.; Crawley, Daisy; Tymchyk, Ruslana; Hall, Anna; Onysk, Jakub; Erdmann, Tore; Wimmer, Elliott; Berwian, Isabel M.; Norbury, Agnes; Huys, Quentin J. M.
Early reduction in aversive Pavlovian bias as a mediator of anhedonia improvement during Behavioural Activation in realistic treatment settings. Journal Article
In: PLoS Computational Biology, vol. 22, iss. 7, pp. e1014439, 2026, ISSN: 1553-7358.
@article{BerndtHuys26,
title = {Early reduction in aversive Pavlovian bias as a mediator of anhedonia improvement during Behavioural Activation in realistic treatment settings.},
author = {Lioba C. S. Berndt and Daisy Crawley and Ruslana Tymchyk and Anna Hall and Jakub Onysk and Tore Erdmann and Elliott Wimmer and Isabel M. Berwian and Agnes Norbury and Quentin J. M. Huys},
url = {https://acplab.org/wp-content/uploads/publications/BerndtHuys26.pdf},
doi = {10.1371/journal.pcbi.1014439},
issn = {1553-7358},
year = {2026},
date = {2026-07-14},
journal = {PLoS Computational Biology},
volume = {22},
issue = {7},
pages = {e1014439},
abstract = {Behavioral Activation therapy is an effective treatment for major depressive disorder. Conceptually, the mechanisms through which it acts are thought to involve alterations to reinforcement learning, and several recent studies have supported this in laboratory settings. However, it remains unclear whether reinforcement learning mechanisms are involved in a realistic treatment setting. In a randomized controlled observational study in the UK NHS talking Therapy service, 152 participants with low mood received reinforcement learning assessments using an affective Go/No-Go task. The assessment timepoints were randomized between subjects, with assessments occurring either before (control; n = 78) or during BA therapy (active; n = 74). Changes in Pavlovian biases were quantified using computational modeling. Anhedonia improved during treatment, but not before (p = 0.047; d = 0.53). The active treatment group showed significant changes in Pavlovian parameters compared to controls (pFDR = 0.012; d = 0.65). Pavlovian biases became more positive in the active group (M = 0.44},
keywords = {},
pubstate = {epublish},
tppubtype = {article}
}
Behavioral Activation therapy is an effective treatment for major depressive disorder. Conceptually, the mechanisms through which it acts are thought to involve alterations to reinforcement learning, and several recent studies have supported this in laboratory settings. However, it remains unclear whether reinforcement learning mechanisms are involved in a realistic treatment setting. In a randomized controlled observational study in the UK NHS talking Therapy service, 152 participants with low mood received reinforcement learning assessments using an affective Go/No-Go task. The assessment timepoints were randomized between subjects, with assessments occurring either before (control; n = 78) or during BA therapy (active; n = 74). Changes in Pavlovian biases were quantified using computational modeling. Anhedonia improved during treatment, but not before (p = 0.047; d = 0.53). The active treatment group showed significant changes in Pavlovian parameters compared to controls (pFDR = 0.012; d = 0.65). Pavlovian biases became more positive in the active group (M = 0.44
Cheng, Annie; Konova, Anna; Powers, Albert; Corlett, Philip; Levy, Ifat; Gu, Xiaosi; Huys, Quentin; Pushkarskya, Helen; Fineberg, Sarah; Hauser, Tobias; Bzdok, Danilo; Harpaz-Rotem, Ilan; Babuscio, Theresa; Nichols, Lisa; Zhao, Yize; Sharma, Manu; Meeker, Daniella; Xu, Hua; Rutledge, Robb B.; Pearlson, Godfrey D.; Pittenger, Christopher; Yip, Sarah W.
In: Biological Psychiatry. Cognitive neuroscience and neuroimaging, 2026, ISSN: 2451-9030.
@article{ChengYip26,
title = {Threading the needle: Practical considerations for merging theory-driven computational psychiatry with data-driven analytics to enhance precision health at scale.},
author = {Annie Cheng and Anna Konova and Albert Powers and Philip Corlett and Ifat Levy and Xiaosi Gu and Quentin Huys and Helen Pushkarskya and Sarah Fineberg and Tobias Hauser and Danilo Bzdok and Ilan Harpaz-Rotem and Theresa Babuscio and Lisa Nichols and Yize Zhao and Manu Sharma and Daniella Meeker and Hua Xu and Robb B. Rutledge and Godfrey D. Pearlson and Christopher Pittenger and Sarah W. Yip},
url = {https://acplab.org/wp-content/uploads/publications/ChengYip26.pdf},
doi = {10.1016/j.bpsc.2026.02.009},
issn = {2451-9030},
year = {2026},
date = {2026-02-26},
journal = {Biological Psychiatry. Cognitive neuroscience and neuroimaging},
abstract = {The rapidly evolving field of computational psychiatry enables quantification of specific cognitive processes, and their underlying mechanisms, in a translational and potentially scalable manner, using a combination of data collection via mechanistically informed behavioral tasks and theory-driven mathematical modeling. In parallel, transdiagnostic, dimensional approaches to psychiatric diagnostics, such as RDoC and HiTOP, seek to facilitate links between clinical research and real-world clinical reality, which rarely respects traditional diagnostic boundaries. These two approaches are seldom combined. In addition, while most psychiatric disorders are defined by their longitudinal course, our ability to predict symptom trajectories and tailor treatments to the individual remains limited, in part due to a dearth of longitudinal data collected using assessments sensitive to individual change over time. To address these gaps, the recently launched 'Individually Measured Phenotypes to Advance Computational Translation at Yale' (IMPACT-Y) study is collecting longitudinal data from a transdiagnostic cohort of 2400 individuals, using a combination of 'traditional' clinical research methods (e.g., health records, standardized assessments) and more novel computational approaches (e.g., behavioral tasks with demonstrated sensitivity to latent constructs and to within-person change, spoken narrative data). Here, we discuss unique challenges and opportunities in study design and analysis considerations of IMPACT-Y. Incorporating both theory- and data-driven analytics, we hope that IMPACT-Y will provide an unprecedented resource for characterizing longitudinal trajectories of core computational psychiatry constructs (e.g., reward learning) within and between individuals, for parsing heterogeneity beyond traditional diagnostic categories, and for linking inter- and intra-individual clinical variability to underlying mechanisms.},
keywords = {},
pubstate = {aheadofprint},
tppubtype = {article}
}
The rapidly evolving field of computational psychiatry enables quantification of specific cognitive processes, and their underlying mechanisms, in a translational and potentially scalable manner, using a combination of data collection via mechanistically informed behavioral tasks and theory-driven mathematical modeling. In parallel, transdiagnostic, dimensional approaches to psychiatric diagnostics, such as RDoC and HiTOP, seek to facilitate links between clinical research and real-world clinical reality, which rarely respects traditional diagnostic boundaries. These two approaches are seldom combined. In addition, while most psychiatric disorders are defined by their longitudinal course, our ability to predict symptom trajectories and tailor treatments to the individual remains limited, in part due to a dearth of longitudinal data collected using assessments sensitive to individual change over time. To address these gaps, the recently launched 'Individually Measured Phenotypes to Advance Computational Translation at Yale' (IMPACT-Y) study is collecting longitudinal data from a transdiagnostic cohort of 2400 individuals, using a combination of 'traditional' clinical research methods (e.g., health records, standardized assessments) and more novel computational approaches (e.g., behavioral tasks with demonstrated sensitivity to latent constructs and to within-person change, spoken narrative data). Here, we discuss unique challenges and opportunities in study design and analysis considerations of IMPACT-Y. Incorporating both theory- and data-driven analytics, we hope that IMPACT-Y will provide an unprecedented resource for characterizing longitudinal trajectories of core computational psychiatry constructs (e.g., reward learning) within and between individuals, for parsing heterogeneity beyond traditional diagnostic categories, and for linking inter- and intra-individual clinical variability to underlying mechanisms.
Latest Preprints
Jafree, Daniyal J; Sun, Mingze; Stewart, Gordon W; Gishen, Faye; Swanton, Charles; Reza,; Motallebzadeh,
Integrated MB-PhD training is a long-term investment in the clinician-scientist workforce Journal Article
In: medRxiv, 2026.
@article{JafreeMotallebzadeh26,
title = {Integrated MB-PhD training is a long-term investment in the clinician-scientist workforce},
author = {Daniyal J Jafree and Mingze Sun and Gordon W Stewart and Faye Gishen and Charles Swanton and Reza and Motallebzadeh},
url = {https://acplab.org/wp-content/uploads/publications/JafreeMotallebzadeh26.pdf},
doi = {10.64898/2026.08.26.26361003},
year = {2026},
date = {2026-09-01},
journal = {medRxiv},
publisher = {openRxiv},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Qiu, Zeguo; Wang, Muzhi; Lu, Haoyang; Abir, Yaniv; Zharmakhan, Raziya; Singh, Nikki; Poppe, Michaela; Degni, Luigi; Huys, Quentin JM
EEG biomarkers of reinforcement learning and motivation: A multi-task battery Journal Article
In: 2026.
@article{QiuHuys26,
title = {EEG biomarkers of reinforcement learning and motivation: A multi-task battery},
author = {Zeguo Qiu and Muzhi Wang and Haoyang Lu and Yaniv Abir and Raziya Zharmakhan and Nikki Singh and Michaela Poppe and Luigi Degni and Quentin JM Huys},
url = {https://acplab.org/wp-content/uploads/publications/QiuHuys26.pdf},
doi = {10.64898/2026.06.28.735051},
year = {2026},
date = {2026-07-04},
publisher = {openRxiv},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Onysk, Jakub; Chen, Jiazhou; Huys, Quentin JM
On the computational nature of emotions: insights from metareasoning and transformers Journal Article
In: psyArxiv, 2026.
@article{OnyskHuys26,
title = {On the computational nature of emotions: insights from metareasoning and transformers},
author = {Jakub Onysk and Jiazhou Chen and Quentin JM Huys},
url = {https://acplab.org/wp-content/uploads/publications/OnyskHuys26.pdf},
doi = {10.31234/osf.io/m478p_v1},
year = {2026},
date = {2026-05-19},
urldate = {2026-05-18},
journal = {psyArxiv},
publisher = {Center for Open Science},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Serfaty, Jade; Huys, Quentin J. M.
Subjective emotion judgements adhere to principles of Bayesian inference and efficient representation Journal Article
In: psyArxiv, 2026.
@article{SerfatyHuys26,
title = {Subjective emotion judgements adhere to principles of Bayesian inference and efficient representation},
author = {Jade Serfaty and Quentin J. M. Huys},
url = {https://acplab.org/wp-content/uploads/publications/SerfatyHuys26.pdf},
doi = {https://doi.org/10.31234/osf.io/ufmqy_v1},
year = {2026},
date = {2026-05-18},
journal = {psyArxiv},
publisher = {Center for Open Science},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kim, Taekwan; Viding, Essi; Huys, Quentin J M
Reliable detection of longitudinal change incomputational models Journal Article
In: psyArxiv, 2026.
@article{KimHuys26,
title = {Reliable detection of longitudinal change incomputational models},
author = {Taekwan Kim and Essi Viding and Quentin J M Huys},
url = {https://acplab.org/wp-content/uploads/publications/KimHuys26.pdf},
doi = {https://osf.io/s9dhk},
year = {2026},
date = {2026-04-26},
journal = {psyArxiv},
keywords = {},
pubstate = {published},
tppubtype = {article}
}