quentin dercon

Hi there! My name is Quentin, and I’m a PhD student studying mental health science, based in the Applied Computational Psychiatry lab at UCL.

My current research interests include:

how psychological treatments for symptoms of mental ill-health alter learning, affect, and other cognitive processes
how we can formalise the mechanisms of individual components of psychological interventions
how mechanistic insights can be used to predict responses to psychological therapies and personalise clinical decision-making

Feel free to follow me on Bluesky to keep up to date with my work, or get in touch directly via the email icon at the bottom of the page.

selected publications

Common psychiatric treatments alter affective dynamics

Quentin Dercon, Quentin J. M. Huys, Robb B. Rutledge, and Camilla L. Nord

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Affective states are dynamic, fluctuating in response to recent events: an unexpected pleasure, a disappointing loss. Affective biases, which cause disruptions in these dynamics, are core components of mental ill-health, but the specific effects of treatments on these biases are poorly understood. Here, we investigate the impact of common psychiatric treatments on subjective assessments of happiness, confidence, and engagement during a reinforcement learning task (N=935; 130 taking antidepressant medications). Half (N=459) of the participants were randomised to practice a common psychotherapeutic technique—cognitive distancing—throughout the task. From a joint computational model of learning and affect, we find evidence for distinct and overlapping impacts of psychiatric treatments on affective dynamics. Cognitive distancing attenuates downward drift in happiness and engagement and increases recency bias in the affective impact of recent choices. Conversely, antidepressant use increases baseline happiness and confidence in individuals with similar levels of current symptoms, and decreases recency bias such that more past events influence affective states. Crucially, both cognitive distancing and antidepressant use converge to dampen negative biases in happiness and confidence specifically in participants experiencing higher levels of anxiety and depression symptoms. Together, our results indicate that common treatments for mental ill-health may alter symptoms through their impact on affective dynamics, but via distinct mechanisms.
Learning training as a cognitive restructuring intervention

Agnes Norbury, Quentin Dercon, Tobias U. Hauser, Raymond J. Dolan, and Quentin J. M. Huys

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Background: A core part of cognitive therapy for low mood is learning to identify and challenge negative beliefs. However, it is currently unclear whether improved ability to recognise such beliefs, and the biased interpretations of events which may maintain them, is a mechanism of symptom change during treatment.

Methods: We investigated the effects of completing a learning task (training to identify and select self-enhancing interpretations of events) and a brief cognitive restructuring intervention (how exploring alternative explanations of events may result in improved mood) on causal attribution tendencies. Studies were conducted online using randomized-controlled experimental designs (N=200 & N=164), and data were analysed using hierarchical Bayesian models.

Results: We found that both learning training and the restructuring intervention decreased tendencies to make unhelpful attributions and increased tendencies to make self-enhancing attributions. Across two studies, changes in attribution tendencies were associated with higher learning rates during learning training, an effect specific to learning about different kinds of event attribution. Contrary to expectation, we found no evidence that faster learning was associated specifically to changes in attribution tendencies following cognitive restructuring. Since participants with higher learning rate estimates also provided explicit ratings and free-text descriptions of event causes which were closer to the ground truth, we interpret this as representing a greater benefit of learning training in individuals who were better able to understand the task state space.

Conclusions: We suggest that personalized training, in conjunction with feedback based on interpretable computational model output, may provide a useful form of augmentation or learning support tool during therapy.
A core component of psychological therapy causes adaptive changes in computational learning mechanisms

Quentin Dercon^†, Sara Z. Mehrhof^†, Timothy R. Sandhu, Caitlin Hitchcock, Rebecca P. Lawson, Diego A. Pizzagalli, Tim Dalgleish, and Camilla L. Nord

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Psychological therapy is an effective treatment for many mental health disorders, but its therapeutic mechanisms are relatively unclear. We investigated whether therapy may affect aspects of learning from rewards and losses, by comparing the performance on a learning task of participants who were asked to try to ’distance’ themselves from task feedback, to that of participants not engaging in so-called ’cognitive distancing’.

Participants engaging in cognitive distancing performed slightly better on the task. Using computational modelling, we found evidence that distancing led to choosing more driven by differences in expected values (i.e., the expected future return of a given option), and adaptive changes in how losses were used to compute and update these expected values. Our results indicate that distancing may improve symptoms of mental health disorders by promoting more effective engagement with negative information.

All analyses were implemented in a custom R package, pstpipeline, and each step is laid out in accompanying Jupyter notebooks alongside methodological motivations.

Background: Cognitive distancing is an emotion regulation strategy commonly used in psychological treatment of various mental health disorders, but its therapeutic mechanisms are unknown.

Methods: 935 participants completed an online reinforcement learning task involving choices between pairs of symbols with differing reward contingencies. Half (49.1%) of the sample was randomised to a cognitive self-distancing intervention and were trained to regulate or ‘take a step back’ from their emotional response to feedback throughout. Established computational (Q-learning) models were then fit to individuals’ choices to derive reinforcement learning parameters capturing clarity of choice values (inverse temperature) and their sensitivity to positive and negative feedback (learning rates).

Results: Cognitive distancing improved task performance, including when participants were later tested on novel combinations of symbols without feedback. Group differences in computational model-derived parameters revealed that cognitive distancing resulted in clearer representations of option values (estimated 0.17 higher inverse temperatures). Simultaneously, distancing caused increased sensitivity to negative feedback (estimated 19% higher loss learning rates). Exploratory analyses suggested this resulted from an evolving shift in strategy by distanced participants: initially, choices were more determined by expected value differences between symbols, but as the task progressed, they became more sensitive to negative feedback, with evidence for a difference strongest by the end of training.

Conclusions: Adaptive effects on the computations that underlie learning from reward and loss may explain the therapeutic benefits of cognitive distancing. Over time and with practice, cognitive distancing may improve symptoms of mental health disorders by promoting more effective engagement with negative information.