Maël Lebreton

Senior Research Affiliate – ERC Starting Grant (INFORL)

PSE Professor

  • Paris School of Economics
Research themes
  • Behavioral economics
  • Economic psychology
  • Experimental Economics
Contact

Address :48 Boulevard Jourdan,
75014 Paris, France

Publications HAL

  • How large is “large enough” ? Large-scale experimental investigation of the reliability of confidence measures Pre-print, Working paper

    Whether individuals feel confident about their own actions, choices, or statements being correct, and how these confidence levels differ between individuals are two key primitives for countless behavioral theories and phenomena. In cognitive tasks, individual confidence is typically measured as the average of reports about choice accuracy, but how reliable is the resulting characterization of within-and between-individual confidence remains surprisingly undocumented. Here, we perform a large-scale resampling exercise in the Confidence Database to investigate the reliability of individual confidence estimates, and of comparisons across individuals’ confidence levels. Our results show that confidence estimates are more stable than their choice-accuracy counterpart, reaching a reliability plateau after roughly 50 trials, regardless of a number of task design characteristics. While constituting a reliability upper-bound for task-based confidence measures, and thereby leaving open the question of the reliability of the construct itself, these results characterize the robustness of past and future task designs.

    Published in

  • Anticipatory Anxiety and Wishful Thinking Journal article

    Across five experiments (N = 1,714), we test whether people engage in wishful thinking to alleviate anxiety about adverse future outcomes. Participants perform pattern recognition tasks in which some patterns may result in an electric shock or a monetary loss. Diagnostic of wishful thinking, participants are less likely to correctly identify patterns that are associated with a shock or loss. Wishful thinking is more pronounced under more ambiguous signals and only reduced by higher accuracy incentives when participants’ cognitive effort reduces ambiguity. Wishful thinking disappears in the domain of monetary gains, indicating that negative emotions are important drivers of the phenomenon.

    Journal: American Economic Review

    Published in

  • Specificity and sensitivity of the fixed-point test for binary mixture distributions Journal article

    When two cognitive processes contribute to a behavioral output—each process producing a specific distribution of the behavioral variable of interest—and when the mixture proportion of these two processes varies as a function of an experimental condition, a common density point should be present in the observed distributions of the data across said conditions. In principle, one can statistically test for the presence (or absence) of a fixed point in experimental data to provide evidence in favor of (or against) the presence of a mixture of processes, whose proportions are affected by an experimental manipulation. In this paper, we provide an empirical diagnostic of this test to detect a mixture of processes. We do so using resampling of real experimental data under different scenarios, which mimic variations in the experimental design suspected to affect the sensitivity and specificity of the fixed-point test (i.e., mixture proportion, time on task, and sample size). Resampling such scenarios with real data allows us to preserve important features of data which are typically observed in real experiments while maintaining tight control over the properties of the resampled scenarios. This is of particular relevance considering such stringent assumptions underlying the fixed-point test. With this paper, we ultimately aim at validating the fixed-point property of binary mixture data and at providing some performance metrics to researchers aiming at testing the fixed-point property on their experimental data.

    Journal: Behavior Research Methods

    Published in

  • Decision-making under risk and ambiguity in adults with Tourette syndrome Journal article

    Background Tourette syndrome (TS) as well as its most common comorbidities are associated with a higher propensity for risky behaviour in everyday life. However, it is unclear whether this increased risk propensity in real-life contexts translates into a generally increased attitude towards risk. We aimed to assess decision-making under risk and ambiguity based on prospect theory by considering the effects of comorbidities and medication. Methods Fifty-four individuals with TS and 32 healthy controls performed risk and ambiguity decision-making tasks under both gains and losses conditions. Behavioural and computational parameters were evaluated using (i) univariate analysis to determine parameters difference taking independently; (ii) supervised multivariate analysis to evaluate whether our parameters could jointly account for between-group differences (iii) unsupervised multivariate analysis to explore the potential presence of sub-groups. Results Except for general ‘noisier’ (less consistent) decisions in TS, we showed no specific risk-taking behaviour in TS or any relation with tics severity or antipsychotic medication. However, the presence of comorbidities was associated with distortion of decision-making. Specifically, TS with obsessive–compulsive disorder comorbidity was associated with a higher risk-taking profile to increase gain and a higher risk-averse profile to decrease loss. TS with attention-deficit hyperactivity disorder comorbidity was associated with risk-seeking in the ambiguity context to reduce a potential loss. Conclusions Impaired valuation of risk and ambiguity was not related to TS per se . Our findings are important for clinical practice: the involvement of individuals with TS in real-life risky situations may actually rather result from other factors such as psychiatric comorbidities.

    Journal: Psychological Medicine

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  • Linking confidence biases to reinforcement-learning processes. Journal article

    We systematically misjudge our own performance in simple economic tasks. First, we generally overestimate our ability to make correct choices-a bias called overconfidence. Second, we are more confident in our choices when we seek gains than when we try to avoid losses-a bias we refer to as the valence-induced confidence bias. Strikingly, these two biases are also present in reinforcement-learning (RL) contexts, despite the fact that outcomes are provided trial-by-trial and could, in principle, be used to recalibrate confidence judgments online. How confidence biases emerge and are maintained in reinforcement-learning contexts is thus puzzling and still unaccounted for. To explain this paradox, we propose that confidence biases stem from learning biases, and test this hypothesis using data from multiple experiments, where we concomitantly assessed instrumental choices and confidence judgments, during learning and transfer phases. Our results first show that participants’ choices in both tasks are best accounted for by a reinforcement-learning model featuring context-dependent learning and confirmatory updating. We then demonstrate that the complex, biased pattern of confidence judgments elicited during both tasks can be explained by an overweighting of the learned value of the chosen option in the computation of confidence judgments. We finally show that, consequently, the individual learning model parameters responsible for the learning biases-confirmatory updating and outcome context-dependency-are predictive of the individual metacognitive biases. We conclude suggesting that the metacognitive biases originate from fundamentally biased learning computations.

    Journal: Psychological Review

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