Peer-Reviewed
2026
- Social learning dynamically shapes moral decision-making by biasing subjective valuationJulien Benistant, Valentin Guigon, Alain Nicolas , and 2 more authorsPLOS Biology
Observing immoral behavior increases one’s dishonesty by social influence and learning processes. The neurocomputational mechanisms underlying such moral contagion remain unclear. We tested different mechanistic hypotheses to account for moral contagion. We used model-based fMRI and a new cheating game in which participants were sequentially placed in honest and dishonest social norm contexts. Participants’ cheating behavior increased in the dishonest norm context but was unchanged in the honest. The best model to account for behavior indicated that participants’ valuation was dynamically biased by learning that others had cheated. At the time of choice, the internalization of social norms was implemented in the lateral prefrontal cortex and biased valuations of cheating. During learning, simulation of others’ cheating behavior was encoded in the posterior superior temporal sulcus. Together, these findings provide a mechanistic understanding of how learning about others’ dishonesty biases individuals’ valuation of cheating but does not alter one’s established preferences.
- Rethinking misinformation through plausibility estimation and confidence calibrationValentin Guigon, Lucille Geay, and Caroline J. CharpentierCommunications Psychology
Democracies are vulnerable to misinformation. Prevailing interventions emphasize truth detection, but offer no panacea. We argue that strengthening people’s ability to evaluate the plausibility of information and calibrate their confidence under uncertainty offers a complementary route to addressing misinformation.
2024
- Metacognition biases information seeking in assessing ambiguous newsValentin Guigon, Marie Claire Villeval, and Jean-Claude DreherCommunications Psychology
How do we assess the veracity of ambiguous news, and does metacognition guide our decisions to seek further information? In a controlled experiment, participants evaluated the veracity of ambiguous news and decided whether to seek extra information. Confidence in their veracity judgments did not predict accuracy, showing limited metacognitive ability when facing ambiguous news. Despite this, confidence in one’s judgment was the primary driver of the demand for additional information about the news. Lower confidence predicted a stronger desire for extra information, regardless of the veracity judgment. Two key news characteristics led individuals to confidently misinterpret both true and fake news. News imprecision and news tendency to polarize opinions increased the likelihood of misjudgment, highlighting individuals’ vulnerability to ambiguity. Structural equation modeling revealed that the demand for disambiguating information, driven by uncalibrated metacognition, became increasingly ineffective as individuals are drawn in by the ambiguity of the news. Our results underscore the importance of metacognitive abilities in mediating the relationship between assessing ambiguous information and the decision to seek or avoid more information.
2022
- Perturbation of right dorsolateral prefrontal cortex makes power holders less resistant to tempting bribesYang Hu, Valentin Guigon, Rémi Philippe , and 5 more authorsPsychological Science
Bribery is a common form of corruption that takes place when a briber suborns a power holder to achieve an advantageous outcome at the cost of moral transgression. Although bribery has been extensively investigated in the behavioral sciences, its underlying neurobiological basis remains poorly understood. Here, we employed transcranial direct-current stimulation (tDCS) in combination with a novel paradigm (N = 119 adults) to investigate whether disruption of right dorsolateral prefrontal cortex (rDLPFC) causally changed bribe-taking decisions of power holders. Perturbing rDLPFC via tDCS specifically made participants more willing to take bribes as the relative value of the offer increased. This tDCS-induced effect could not be explained by changes in other measures. Model-based analyses further revealed that such neural modulation alters the concern for generating profits for oneself via taking bribes and reshapes the concern for the distribution inequity between oneself and the briber, thereby influencing the subsequent decisions. These findings reveal a causal role of rDLPFC in modulating corrupt behavior.
2021
- Rewards that are near increase impulsive actionDavid A O’Connor, Remi Janet, Valentin Guigon , and 6 more authorsIscience
In modern society, the natural drive to behave impulsively in order to obtain rewards must often be curbed. A continued failure to do so is associated with a range of outcomes including drug abuse, pathological gambling, and obesity. Here, we used virtual reality technology to investigate whether spatial proximity to rewards has the power to exacerbate the drive to behave impulsively toward them. We embedded two behavioral tasks measuring distinct forms of impulsive behavior, impulsive action, and impulsive choice, within an environment rendered in virtual reality. Participants responded to three-dimensional cues representing food rewards located in either near or far space. Bayesian analyses revealed that participants were significantly less able to stop motor actions when rewarding cues were near compared with when they were far. Since factors normally associated with proximity were controlled for, these results suggest that proximity plays a distinctive role in driving impulsive actions for rewards.
Preprints
2026
- Neurodesign-plus: design optimization for constrained event-level fMRI paradigmsAtharv A. Umap, Caroline J. Charpentier, and Valentin Guigon
Functional magnetic resonance imaging (fMRI) experimental paradigms require event schedules whose order, duration, and inter-event spacing determine the design matrices used for statistical analysis. The original Neurodesign package formalized this problem as within-subject design optimization, combining detection efficiency, estimation efficiency, frequency accuracy, and transition/confounding control within genetic-algorithm and simulation-based workflows. Neurodesign-plus is an extended and maintained fork of this framework. It preserves the inherited optimization and scoring machinery while adding support for task structures that are common in event-level fMRI designs but difficult to express through the original global timing and order-generation interface: fixed user-defined event orders, per-stimulus or distribution-based stimulus durations, transition-specific ITIs, and probabilistic template-based event ordering. The contribution is therefore not a new efficiency objective, but an expansion of the admissible design-generation and timing-specification space available to researchers planning constrained fMRI tasks.
Under Review
- Neurocomputational mechanisms of inferring others’ preferences for informationValentin Guigon, Julien Benistant, Marie Claire Villeval , and 1 more authorPresented at: SBDM (2021, 2023); SNE (2021)
The spread of misinformation often arises from uncertainty about the veracity of ambiguous news. How do people decide whether others would value additional information that could reduce such uncertainty? We investigated the neurocomputational mechanisms underlying these inferences using a model-based fMRI coordination task. Participants (Senders) decided whether to send additional information to Receivers who had previously indicated their willingness to receive it, allowing us to isolate the inferential computations preceding information sharing. Decisions depended on two hierarchically related belief types: first-order beliefs about the truthfulness of the news and confidence in that judgment, and second-order beliefs about Receivers’ informational preferences inferred from the distance between their opinions and the news content. Participants shared more often when uncertain about veracity and less when they believed that Receivers’ opinions diverged from the content. A Bayesian model integrating these beliefs best explained behavior. First-order beliefs were represented in ventromedial prefrontal cortex and striatum, second-order beliefs in temporoparietal junction and dorsomedial prefrontal cortex, and their integration in frontopolar cortex. These results identify the computations and neural systems that enable humans to infer others’ informational preferences under uncertainty.
- Ten simple rules for achieving computational reproducibility in neuroscienceGaurav D. Mahajan, Caroline J. Charpentier, and Valentin Guigon
Reproducibility is a cornerstone of scientific rigor. Yet computational research increasingly depends on complex, linked operations. In computational neuroscience, these operations often combine multiple data types, specialized toolboxes, custom scripts, and changing computing environments. Open science practices, FAIR principles, and data standardization have improved how research objects are shared and described. However, they do not guarantee that the same analysis can be rerun to reconstruct the original result. This limitation becomes more consequential as AI-assisted coding tools are accelerating code development, with the potential to strengthen good practices or amplify weak ones. Related problems have long been addressed in software engineering and machine learning operations through practices that make code, data, environments, and execution steps traceable. Drawing from these practices, we present ten simple rules for achieving computational reproducibility in neuroscience. We emphasize simple, low-cost decisions that can be implemented early and scaled as projects become more complex.
- Integrating theory-driven and data-driven computational psychiatrySabrina Mombelli, Valentin Guigon, Daniel Benrimoh , and 1 more authorpsyRxiv
Computational psychiatry has advanced through two parallel traditions: theory-driven models that aim to explain the mechanisms underlying psychiatric symptoms, and data-driven approaches designed to improve prediction and clinical decision-making. Although both approaches have generated important insights, their integration could support clinical translation. Using psychosis onset prediction as a case study, we propose a mechanism-first, iterative framework in which computational models, empirical data, and predictive tools inform and refine one another. Within this framework, theory guides model and task design, while data-driven approaches support stratification, validation, and comparison across computational hypotheses. This integration may improve generalizability across clinical cohorts, support individualized risk estimation, and facilitate the identification of mechanistically informed subgroups. We also discuss the potential role of computational parameters as biomarker-like features within predictive models, alongside challenges related to reliability, scalability, and ethical implementation. Integrating theory- and data-driven approaches may help computational psychiatry develop clinically useful and mechanistically interpretable models.
In preparation
- Individual differences in dynamic belief updating during trust learningValentin Guigon, Selin Topel, and Caroline J. CharpentierPresented at: SNE (2025); CISE (2025)
Trust involves decisions made under uncertainty whereby outcomes depend on the unobservable intentions of others. Unlike natural risk, this uncertainty is relational and contingent on inferred partner reliability. While normative models assume people update beliefs about others based on outcomes, individuals vary in their strategies and sensitivities - especially in populations with social cognitive difficulties. This study aims to characterize individual differences in trust updating and underlying computational mechanisms.
- Testosterone causes decoupling of orbitofrontal cortex-amygdala relationship while anticipating primary and secondary rewardsValentin Guigon, Simon Dunne, Agnieszka Pazderska , and 5 more authorsPresented at: OFC Meeting (2018)
Correlational evidence shows that levels of testosterone are positively related to reward sensitivity in humans. Yet, studies of the direct effects of exogenous testosterone administration on the reward system in human males are scarce. We sought to investigate the effects of testosterone injection on behavior and brain activity while participants were anticipating erotic or monetary rewards. Healthy young male participants (N=40) performed an incentive delay task with cued erotic and monetary rewards in a between-subjects, double-blind, placebocontrolled design. We hypothesized that testosterone administration may: 1) increase posterior lateral orbitofrontal cortex activity, previously observed to be engaged more with erotic as compared to monetary rewards in healthy young men; (2) decrease the functional coupling between the medial part of the orbitofrontal cortex and the amygdala while anticipating rewards. Results show testosterone specifically increased incentive behavior related to erotic stimuli as compared to monetary rewards. This behavioral interaction effect was associated with a higher association between the relative motivational value for erotic as compared to monetary cues in the ventral striatum. No changes were observed after testosterone injection in the posterior lateral orbitofrontal cortex while viewing erotic rewards. However, testosterone injection reduced the functional coupling between the ventromedial prefrontal cortex and the amygdala while anticipating both primary and secondary rewards, showing testosterone affects limbicprefrontal connectivity during reward processing.