Stefano Palminteri

Biographic Data

ID1752964
NAMEStefano Palminteri
GIVEN NAMESStefano
FAMILY NAMEPalminteri
SIGNATUREPALMINTERI S
AFFILIATIONSLaboratoire de Neurosciences Cognitives
ORCID0000-0001-5768-6646
VERIFIEDYes
TOTAL WORKS14
TOTAL CITATIONS9
AUTHOR COUNT14
EDITOR COUNT0
FIRST PUBLICATION YEAR2017
LATEST PUBLICATION YEAR2025
H-INDEX1

  • Relative Value Encoding in Large Language Models: A Multi-Task, Multi-Model Investigation

    Open AccessWilliam M Hayes, Nicolas Yax et al.ARTICLEOpen MIND2025

    Abtract In-context learning enables large language models (LLMs) to perform a variety of tasks, including solving reinforcement learning (RL) problems. Given their potential use as (autonomous) decision-making agents, it is important to understand how these models behave in RL tasks and the extent to which they are susceptible to biases. Motivated by the fact that, in humans, it has been widely documented that the value of a choice outcome depend…

  • Human reinforcement learning processes and biases: Computational Characterization and Possible Applications to Behavioral Public Policy

    Open AccessStefano PalminteriARTICLEMind & Society2025Cited by: 1References: 6

    The reinforcement learning framework provides a computational and behavioral foundation for understanding how agents learn to maximize rewards and minimize punishments through interaction with their environment. This framework has been widely applied across disciplines, including artificial intelligence, animal psychology, and economics. Over the last decade, a growing body of research has shown that human reinforcement learning often deviates fr…

  • Comparing experience- and description-based economic preferences across 11 countries

    Open AccessHernán Anlló, Sophie Bavard et al.ARTICLENature Human Behaviour2024References: 77

  • Author Correction: Comparing experience- and description-based economic preferences across 11 countries

    Open AccessHernán Anlló, Sophie Bavard et al.ARTICLENature Human Behaviour2024

  • Feedback-induced dispositional changes in risk preferences

    Stefano Palminteri, Maël Lebreton et al.PREPRINT2023

    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…

  • Linking confidence biases to reinforcement-learning processes

    Nahuel Salem-Garcia, Stefano Palminteri et al.ARTICLEPsychological Review2023

    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

    Open AccessBasile Garcia, Maël Lebreton et al.ARTICLENature Human Behaviour2023Cited by: 1References: 70

  • The impassable gap between experiential and symbolic values

    Basile Garcia, Maël Lebreton et al.PREPRINT2022

    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

    Sabrine Hamroun, Maël Lebreton et al.PREPRINT2022

    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

    Nahuel Salem-Garcia, Stefano Palminteri et al.PREPRINT2021

    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

    Stefano Palminteri, Maël LebretonPREPRINT2021

    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…

  • Information about action outcomes differentially affects learning from self-determined versus imposed choices

    Open AccessValérian Chambon, Héloïse Théro et al.ARTICLENature Human Behaviour2020References: 40

  • Assessing inter-individual differences with task-related functional neuroimaging

    Open AccessMaël Lebreton, Sophie Bavard et al.ARTICLENature Human Behaviour2019References: 98

  • Behavioural and neural characterization of optimistic reinforcement learning

    Open AccessGermain Lefebvre, Maël Lebreton et al.ARTICLENature Human Behaviour2017Cited by: 7References: 48