The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

Distorsion de probabilité dans le jugement clinique : étude de terrain et expériences en laboratoire

This thesis studies probability distortion in clinical judgment to compare physicians’ judgment with statistical models. We considered that physicians form their clinical judgment by integrating an analytical component and an intuitive component. We documented that physicians may suffer from several biases in the way they evaluate and integrate the two components. This dissertation gathers findings from the field and the lab. With actual medical data practice, we found that physicians were not as good as the statistical models at integrating consistently medical evidence. They over­estimated small probabilities that the patient had the disease and under­ estimated large probabilities. We found that their biased probability judgment might cause unnecessary health care treatment. How then can we improve physician judgment? First, we considered to replace physician judgment by the probability generated from our statistical model. To actually improve decision it was necessary to develop a statistical score that combines the analytical model, the intuitive component of the physician and his observed deviation from the expected decision. Second, we tested in the lab factors that may affect information processing. We found that participants’ ability to learn about the value of the analytical component, without external feedback, depends on the quality of their intuitive component and their working memory. We also found that participants’ ability to integrate both components together depends on their working memory but not their evaluation of the intuitive component.Cette thèse étudie la distorsion de probabilité dans le jugement clinique afin de comparer le jugement des médecins à des modèles statistiques. Nous supposons que les médecins forment leur jugement clinique en intégrant une composante analytique et une composante intuitive. Dans ce cadre, les médecins peuvent souffrir de plusieurs biais dans la façon dont ils évaluent et intègrent les deux composantes. Cette thèse rassemble les résultats obtenus sur le terrain et en laboratoire. À partir de données médicales, nous avons constaté que les médecins n'étaient pas aussi bons que les modèles statistiques à intégrer des évidences médicales. Ils surestimaient les petites probabilités que le patient soit malade et sous­-estimaient les probabilités élevées. Nous avons constaté que leur jugement biaisé pourrait entraîner un sur­-traitement. Comment améliorer leur jugement? Premièrement, nous avons envisagé de remplacer le jugement du médecin par la probabilité de notre modèle statistique. Pour améliorer la décision, il était nécessaire d'élaborer un score statistique qui combine le modèle analytique, la composante intuitive du médecin et sa déviation observée par rapport à la décision attendue. Deuxièmement, nous avons testé en laboratoire des facteurs qui peuvent influencer le traitement de l'information. Nous avons trouvé que la capacité des participants à apprendre la valeur de la composante analytique, sans feedback externe, dépend de la qualité de leur composante intuitive et de leur mémoire de travail. Nous avons aussi trouvé que la capacité des participants à intégrer les deux composantes dépend de leur mémoire de travail, mais pas de leur évaluation de la composante intuitive

Metacognitive ability predicts learning cue-stimulus associations in the absence of external feedback

Abstract Learning how certain cues in our environment predict specific states of nature is an essential ability for survival. However learning typically requires external feedback, which is not always available in everyday life. One potential substitute for external feedback could be to use the confidence we have in our decisions. Under this hypothesis, if no external feedback is available, then the agents’ ability to learn about predictive cues should increase with the quality of their confidence judgments (i.e. metacognitive efficiency). We tested and confirmed this novel prediction in an experimental study using a perceptual decision task. We evaluated in separate sessions the metacognitive abilities of participants (N = 65) and their abilities to learn about predictive cues. As predicted, participants with greater metacognitive abilities learned more about the cues. Knowledge of the cues improved accuracy in the perceptual task. Our results provide strong evidence that confidence plays an active role in improving learning and performance

How Overconfidence Bias Influences Suboptimality in Perceptual Decision Making

International audienceIn perceptual decision making, it is often found that human observers combine sensory information and prior knowledge suboptimally. Typically, in detection tasks, when an alternative is a priori more likely to occur, observers choose it more frequently to account for the unequal base rate but not to the extent they should, a phenomenon referred to as “conservative decision bias” (i.e., observers do not shift their decision criterion enough). One theoretical explanation of this phenomenon is that observers are overconfident in their ability to interpret sensory information, resulting in overweighting the sensory information relative to prior knowledge. Here, we derived formally this candidate model, and we tested it in a visual discrimination task in which we manipulated the prior probabilities of occurrence of the stimuli. We measured confidence in decisions and decision criterion placement in two separate experimental sessions for the same participants (N = 69). Both overconfidence bias and conservative decision bias were found in our data, but critically the link that was predicted between these two quantities was absent. Our data suggested instead that when informed about the a priori probability, overconfident participants put less effort into processing sensory information. These findings offer new perspectives on the role of overconfidence bias to explain suboptimal decisions

How economic success shapes redistribution: The role of self-serving beliefs, in-group bias and justice principles.

International audienceIn the face of economic inequalities, redistribution of wealth is a key debate forsociety, and understanding the reasons why individuals may support moreor less re-distribution can inform this debate. Here we investigate the mechanisms by whichexperiencing success in a task decreases the support for redistribution of the wealthgenerated by the task, such that overachievers favor less redistribution than under-achievers. In a laboratory experiment, we replicate this effect and explore how it thatmay be mediated by an in-group bias, or by changes in individuals’ principlesof redis-tributive justice. Critically, both in-group favoritism and self-serving adjustments ofjustice principles partially accounted for the effect of status on redistribution choices.Our study thus sheds new light on the various ways by which economic experienceaffects support for redistribution

How economic success shapes redistribution

In the face of economic inequalities, redistribution of wealth is a key debate for society, and understanding the reasons why individuals may support more or less redistribution can inform this debate. Here we investigate the mechanisms by which experiencing success in a task decreases the support for redistribution of the wealth generated by the task, such that overachievers favor less redistribution than under-achievers. In a laboratory experiment, we replicate this effect and explore how it that may be mediated by an in-group bias, or by changes in individuals’ principles of redis-tributive justice. Critically, both in-group favoritism and self-serving adjustments of justice principles partially accounted for the effect of status on redistribution choices. Our study thus sheds new light on the various ways by which economic experience affects support for redistribution.</p

The Confidence Database

Understanding how people rate their confidence is critical for characterizing a wide range of perceptual, memory, motor, and cognitive processes. However, as in many other fields, progress has been slowed by the difficulty of collecting new data and the unavailability of existing data. To address this issue, we created a large database of confidence studies spanning a broad set of paradigms, participant populations, and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analyzed in multiple software packages. Each dataset is further accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (available at osf.io/s46pr) contained 145 datasets with data from over 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. We show the usefulness of this large collection of datasets in four different analyses that provide precise estimation for several foundational confidence-related effects and lead to new findings that depend on the availability of large quantity of data. This Confidence Database will continue to enable new discoveries and can serve as a blueprint for similar databases in related fields

The Confidence Database.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects