@article{BrowningGeddes25,

title = {Pramipexole augmentation for the acute phase of treatment-resistant, unipolar depression: a placebo-controlled, double-blind, randomised trial in the UK.},

author = {Michael Browning and Philip J. Cowen and Ushma Galal and Ashley Baldwin and Anthony J. Cleare and Jonathan Evans and Quentin J. M. Huys and David Kessler and Micheal Kurkar and Neil Nixon and Abhinav Rastogi and Stuart Watson and Ly-Mee Yu and Sam Mort and Judit Simon and Agata Laszewska and Alexander C. Lewis and Sophie M. Roberts and Victoria Fiske and Lisa M. Frending and Catherine Money and Beata R. Godlewska and Howard T. Ryland and Don Chamith Halahakoon and Laurence Astill Wright and Barbara Salas and Apoorva Peddada and Mourad Wahba and Katharine S. Taylor and Jess Kerr-Gaffney and Duncan Swiffen and Caroline Zangani and Katharine A. Smith and Catherine J. Harmer and John R. Geddes and P. A. X. -D. group},

url = {https://acplab.org/wp-content/uploads/publications/BrowningGeddes25.pdf},

doi = {10.1016/S2215-0366(25)00194-4},

issn = {2215-0374},

year  = {2025},

date = {2025-08-01},

journal = {The Lancet Psychiatry},

volume = {12},

issue = {8},

pages = {579–589},

publisher = {Elsevier BV},

abstract = {About 30% of patients with depression treated with antidepressant medication do not respond sufficiently to the first agents used. Pramipexole might usefully augment antidepressant medication in such cases of treatment-resistant depression, but data on its effects and tolerability are scarce. We aimed to assess the efficacy and tolerability of pramipexole augmentation of ongoing antidepressant treatment, over 48 weeks, in patients with treatment-resistant depression. We did a multicentre, double-blind, placebo-controlled randomised trial in which adults with resistant major depressive disorder were randomly assigned (1:1; using an online randomisation system) to 48 weeks of pramipexole (titrated to 2.5 mg) or placebo added to their ongoing antidepressant medication. The study was conducted in nine National Health Service Trusts in England. Participants, investigators, and researchers involved in recruitment and assessment were masked to group allocation, and the central pharmacy team dispensing the medication was not masked. The primary outcome was change from baseline to week 12 in the total score of the 16-item Quick Inventory of Depressive Symptomology self-report version (QIDS-SR (16)). The primary analysis was performed on the intention-to-treat population that included all eligible, randomly assigned participants. People with lived experience were involved in the design, oversight, and interpretation of the study. The trial was registered with ISCTRN (ISRCTN84666271) and EudraCT (2019-001023-13) and is complete. Between Feb 16 and May 29, 2024, 217 participants attended a screening visit, of whom 66 were excluded due to ineligibility. 151 participants were randomly assigned (75 to the pramipexole group and 75 to the placebo group, after one participant was found to be ineligible after randomisation). 84 (56%) participants were female and 66 (44%) were male and the mean age of participants was 44.9 years (SD 1.0). Ethnicity data were not available. The mean QIDS-SR (16)total score at baseline was 16.4 (SD 0.4) in the pramipexole group and 16.2 (3.5) in the placebo group. The mean dose of pramipexole received at week 12 was 2.3 mg (SD 0.45). Adjusted mean decrease from baseline to week 12 of the QIDS-SR (16) total score was 6.4 (SD 0.9) for the pramipexole group and 2.4 (4.0) for the placebo group; the mean difference between groups was -3.91 (95% CI -.37 to -2.45; p<0·0001). Termination of trial treatment due to adverse events was more frequent in the pramipexolegroup (15 participants [20%]) than in the placebo group (four participants [5%]), with reported adverse events consistent with known side-effects of pramipexole, in particular nausea, headache, and sleep disturbance or somnolence. In this trial involving participants with treatment-resistant depression, pramipexole augmentation of antidepressant treatment, at a target dose of 2.5 mg, demonstrated a reduction in symptoms relative to placebo at 12 weeks but was associated with some adverse effects. These results suggest that pramipexole is a clinically effective option for reducing symptoms in patients with treatment-resistant depression. Future trials directly comparing pramipexole with existing treatments for this disorder are needed. National Institute of Health and Care Research, Efficacy and Mechanism Evaluation Programme.},

keywords = {},

pubstate = {ppublish},

tppubtype = {article}

}

@article{HallHuys24,

title = {The computational structure of consummatory anhedonia},

author = {Anna Hall and Michael Browning and Q. J. M. Huys},

url = {https://acplab.org/wp-content/uploads/publications/HallHuys24.pdf},

doi = {10.1016/j.tics.2024.01.006},

year  = {2024},

date = {2024-01-19},

urldate = {2024-01-19},

journal = {Trends in Cognitive Sciences},

volume = {28},

issue = {6},

pages = {541-553},

abstract = {Anhedonia is a reduction in enjoyment, motivation or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed ’consummatory anhedonia’, in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement- learning account of consummatory anhedonia with a reconceptualisation of subjective hedonic expe- rience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NorburyHuys24,

title = {Different components of cognitive-behavioural therapy affect specific cognitive mechanisms},

author = {Agnes Norbury and Tobias Hauser and Stephen Fleming and Raymond Dolan and Quentin J. M. Huys},

url = {https://acplab.org/wp-content/uploads/publications/NorburyHuys24.pdf},

doi = {10.1126/sciadv.adk3222},

year  = {2024},

date = {2024-01-14},

urldate = {2024-01-14},

journal = {Science Advances},

volume = {10},

issue = {13},

pages = {eadk3222},

abstract = {Psychological therapies are among the most effective treatments for a range of common mental health problems – however, we still know relatively little about how exactly they improve symp- toms. Here, we demonstrate the power of combing theory with computational methods to parse effects of different components of cognitive-behavioural therapies on to underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the ef- fects of components of behavioural and cognitive therapies on different cognitive processes, us- ing well-validated behavioural measures and associated computational models (total N=807). We found that a goal-setting intervention, based on behavioural activation therapy, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, we found that a cognitive restructuring intervention, based on cognitive therapy, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. Importantly, the effects of each intervention were specific to these respective measures. Our approach provides a basis for understanding how different elements of common psychotherapy programs work, which may enable theoretically-informed treatment targeting in the future.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BerwianHuys20,

title = {Computational Mechanisms of Effort and Reward Decisions in Patients With Depression and Their Association With Relapse After Antidepressant Discontinuation},

author = {Isabel M. Berwian and Julia G. Wenzel and Anne G. E. Collins and Erich Seifritz and Klaas E. Stephan and Henrik Walter and Quentin J. M. Huys},

url = {https://acplab.org/wp-content/uploads/publications/BerwianHuys20.pdf},

doi = {10.1001/jamapsychiatry.2019.4971},

year  = {2020},

date = {2020-02-01},

urldate = {2020-02-01},

journal = {JAMA Psychiatry},

volume = {77},

issue = {5},

pages = {513-522},

publisher = {American Medical Association (AMA)},

abstract = {Importance Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario. 

Objectives To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation. 

Design, Setting, and Participants This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018. Exposure Discontinuation of antidepressants. Main Outcomes and Measures Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model. Results A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SchadHuys20,

title = {Dissociating neural learning signals in human sign- and goal-trackers},

author = {Daniel J Schad and Michael A Rapp and Maria Garbusow and Stephan Nebe and Miriam Sebold and Elisabeth Obst and Christian Sommer and Lorenz Deserno and Milena Rabovsky and Eva Friedel and Nina Romanczuk-Seiferth and Hans-Ulrich Wittchen and Ulrich S Zimmermann and Henrik Walter and Philipp Sterzer and Michael N Smolka and Florian Schlagenhauf and Andreas Heinz and Peter Dayan and Quentin J M Huys},

url = {https://acplab.org/wp-content/uploads/publications/SchadHuys20.pdf},

doi = {10.1038/s41562-019-0765-5},

year  = {2020},

date = {2020-02-01},

urldate = {2020-02-01},

journal = {Nature Human Behaviour},

volume = {4},

issue = {2},

pages = {201-214},

abstract = {Individuals differ in how they learn from experience. In Pavlovian conditioning models, where cues predict reinforcer delivery at a different goal location, some animals-called sign-trackers-come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimuli 'wanted'. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 129 male humans using eye-tracking, pupillometry and functional magnetic resonance imaging informed by computational models of sign- and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HuysRenz17,

title = {A Formal Valuation Framework for Emotions and Their Control},

author = {Quentin J M Huys and Daniel Renz},

url = {https://acplab.org/wp-content/uploads/publications/HuysRenz17.pdf},

doi = {10.1016/j.biopsych.2017.07.003},

year  = {2017},

date = {2017-09-01},

urldate = {2017-09-01},

journal = {Biological Psychiatry},

volume = {82},

issue = {6},

pages = {413–420},

abstract = {Computational psychiatry aims to apply mathematical and computational techniques to help improve psychiatric care. To achieve this, the phenomena under scrutiny should be within the scope of formal methods. As emotions play an important role across many psychiatric disorders, such computational methods must encompass emotions. Here, we consider formal valuation accounts of emotions. We focus on the fact that the flexibility of emotional responses and the nature of appraisals suggest the need for a model-based valuation framework for emotions. However, resource limitations make plain model-based valuation impossible and require metareasoning strategies to apportion cognitive resources adaptively. We argue that emotions may implement such metareasoning approximations by restricting the range of behaviors and states considered. We consider the processes that guide the deployment of the approximations, discerning between innate, model-free, heuristic, and model-based controllers. A formal valuation and metareasoning framework may thus provide a principled approach to examining emotions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HuysFrank16,

title = {Computational psychiatry as a bridge from neuroscience to clinical applications},

author = {Quentin J M Huys and Tiago V Maia and Michael J Frank},

url = {https://acplab.org/wp-content/uploads/publications/HuysFrank16.pdf},

doi = {10.1038/nn.4238},

year  = {2016},

date = {2016-02-01},

urldate = {2016-02-01},

journal = {Nature Neuroscience},

volume = {19},

number = {3},

pages = {404–413},

publisher = {Springer Science and Business Media LLC},

abstract = {Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HuysRoiser12,

title = {Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees},

author = {Quentin J M. Huys and Neir Eshel and Elizabeth O'Nions and Luke Sheridan and Peter Dayan and Jonathan P. Roiser},

url = {https://acplab.org/wp-content/uploads/publications/HuysRoiser12.pdf},

doi = {10.1371/journal.pcbi.1002410},

year  = {2012},

date = {2012-03-01},

urldate = {2012-03-01},

journal = {PLoS Comput Biol},

volume = {8},

number = {3},

pages = {e1002410},

school = {Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.},

abstract = {When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}