Maël Lebreton
Biographic Data
| ID | 1752961 |
|---|---|
| NAME | Maël Lebreton |
| GIVEN NAMES | Maël |
| FAMILY NAME | Lebreton |
| SIGNATURE | LEBRETON M |
| AFFILIATIONS | University of Geneva |
| ORCID | 0000-0002-2071-4890 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 10 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Behavioral and computational signatures of reinforcement learning and confidence biases in gambling disorder
Background and aims: Gambling Disorder (GD) is associated with maladaptive decision-making, possibly driven by biases in learning and confidence judgments. While prior research report abnormal learning rates and heightened overconfidence in GD, the affected cognitive mechanism producing these joint deficits has so far remained unidentified. Our study aims to fill this gap using a recently established reinforcement learning (RL) experimental and c…
Anticipatory Anxiety and Wishful Thinking
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 ambiguou…
Feedback-induced dispositional changes in risk preferences
Contrary to the normative decision-making standpoint, empirical studies have repeatedly reported that risk preferences are affected by the disclosure of choice outcomes (feedback). Although no consensus has yet emerged regarding the properties and mechanisms of this effect, a widespread and intuitive hypothesis is that repeated feedback affects risk preferences by means of learning, which alters the representation of subjective probabilities. Her…
Specificity and sensitivity of the fixed-point test for binary mixture distributions
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 …
Linking confidence biases to reinforcement-learning processes
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 …
Experiential values are underweighted in decisions involving symbolic options
The impassable gap between experiential and symbolic values
To choose between options of different natures, standard decision models presume that a single representational system ultimately indexes their subjective values on a common scale, regardless of how they are constructed. To challenge this assumption, we systematically investigated hybrid decisions between experiential options, whose value is built from past outcomes experience, and symbolic options which describe probabilistic outcomes. We show t…
Dissociation between task structure learning and performance in human model-based reinforcement learning
The multi-step learning paradigm has become the dominant paradigm to investigate the trade-off between model-free reinforcement learning – which only leverages state-action-reward associations – and model-based reinforcement learning – which additionally builds on an explicit representation of state-transitions. Experimentally, while reward values usually have to be learned by trial-and-errors, state-transitions are customarily provided by instru…
SalemGarcia_2021
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 contexts, despite the fact that outcomes are p…
Context-dependent outcome encoding in human reinforcement learning
A wealth of evidence in perceptual and economic decision-making research suggests that the subjective value of one option is determined by other available options (i.e. the context). A series of studies provides evidence that the same coding principles apply to situations where decisions are shaped by past outcomes, i.e. in reinforcement-learning situations. In bandit tasks, human behavior is explained by models assuming that individuals do not l…
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 analys…
Assessing inter-individual differences with task-related functional neuroimaging
Behavioural and neural characterization of optimistic reinforcement learning
Behavioural and neural characterization of optimistic reinforcement learning
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 analys…
Experiential values are underweighted in decisions involving symbolic options
Behavioural and neural characterization of optimistic reinforcement learning
Assessing inter-individual differences with task-related functional neuroimaging
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 analys…
SalemGarcia_2021
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 contexts, despite the fact that outcomes are p…
Context-dependent outcome encoding in human reinforcement learning
A wealth of evidence in perceptual and economic decision-making research suggests that the subjective value of one option is determined by other available options (i.e. the context). A series of studies provides evidence that the same coding principles apply to situations where decisions are shaped by past outcomes, i.e. in reinforcement-learning situations. In bandit tasks, human behavior is explained by models assuming that individuals do not l…
The impassable gap between experiential and symbolic values
To choose between options of different natures, standard decision models presume that a single representational system ultimately indexes their subjective values on a common scale, regardless of how they are constructed. To challenge this assumption, we systematically investigated hybrid decisions between experiential options, whose value is built from past outcomes experience, and symbolic options which describe probabilistic outcomes. We show t…
Dissociation between task structure learning and performance in human model-based reinforcement learning
The multi-step learning paradigm has become the dominant paradigm to investigate the trade-off between model-free reinforcement learning – which only leverages state-action-reward associations – and model-based reinforcement learning – which additionally builds on an explicit representation of state-transitions. Experimentally, while reward values usually have to be learned by trial-and-errors, state-transitions are customarily provided by instru…
Feedback-induced dispositional changes in risk preferences
Contrary to the normative decision-making standpoint, empirical studies have repeatedly reported that risk preferences are affected by the disclosure of choice outcomes (feedback). Although no consensus has yet emerged regarding the properties and mechanisms of this effect, a widespread and intuitive hypothesis is that repeated feedback affects risk preferences by means of learning, which alters the representation of subjective probabilities. Her…
Specificity and sensitivity of the fixed-point test for binary mixture distributions
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 …
Linking confidence biases to reinforcement-learning processes
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 …
Experiential values are underweighted in decisions involving symbolic options
Anticipatory Anxiety and Wishful Thinking
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 ambiguou…
Behavioral and computational signatures of reinforcement learning and confidence biases in gambling disorder
Background and aims: Gambling Disorder (GD) is associated with maladaptive decision-making, possibly driven by biases in learning and confidence judgments. While prior research report abnormal learning rates and heightened overconfidence in GD, the affected cognitive mechanism producing these joint deficits has so far remained unidentified. Our study aims to fill this gap using a recently established reinforcement learning (RL) experimental and c…
Cognitive psychology (11 works) · Computer Science (11 works) · Psychology (11 works) · Artificial Intelligence (10 works) · Decision-Making and Behavioral Economics (9 works) · Social Psychology (9 works) · Reinforcement learning (6 works) · Machine learning (5 works) · Reinforcement (5 works) · Cognition (4 works)