Research interests

RESEARCH INTERESTS

I aim to understand how humans learn to make decisions at the behavioural, computational, and neural levels. I initially worked within the reinforcement learning paradigm. During my PhD, my research focused mainly on understanding the neural bases of reward and punishment learning.

Later, as a postdoctoral researcher and early-career PI, I shifted my focus toward behavioural and computational questions and contributed to establishing a framework for reinforcement learning biases, notably through the discovery and characterization of positivity bias, confirmation bias, and relative value encoding. These remain active lines of research today.

More recently, I have turned my attention to connecting reinforcement learning with other forms of learning and decision-making, including social learning (such as imitation and teaching), as well as decision-making under risk and its interaction with reinforcement learning. The differences between these decision-making modalities are often referred to as the description–experience gap.

My interest in learning and decision-making does not only concern general mechanisms but also interindividual differences. I have devoted many studies to understanding how learning and decision-making vary across neuropsychiatric conditions, across development, and more generally to examining the conceptual and methodological foundations of research on individual differences.

I am also interested in the epistemological foundations of our field—particularly the philosophy of science underlying computational cognitive modelling. This interest is reflected in several papers and in my book, which addresses questions such as: What is a theory? How does it differ from a model? How do we choose between competing models? What assumptions underlie our research framework?

Recently, I have also begun investigating the cognitive and decision-making abilities of large language models. This work includes what is sometimes called machine psychology—using tools from cognitive science to understand LLMs—as well as research on human–machine interaction and the broader philosophical implications of these systems.

Of course, none of this research is carried out alone. On the contrary, I benefit from an amazing network of collaborators and team members—please check the HRL team page!

BOOKS

• Palminteri S, Wyat V. Decision making: A very short introduction. Oxford University Press (2026). 

KEY PEER-REVIEWED PUBLICATIONS

• Palminteri S, Wu CM,. Beyond Computational Equivalence: The Behavioral Inference Principle for Machine Consciousness. Neuroscience of Consciousness (2026).

• Nasioulas A, Potier D, Cerrotti F, Lebreton M, Palminteri S. Feedback-induced attitudinal changes in risk preferences Nature Communications (2026). INSERM press release

• Hoxha I, Sperber L, Palminteri S. Evolving Choice Hysteresis: in reinforcement learning: comparing the adaptive value of positivity bias and gradual perseveration. PNAS (2025).

• Vrizzi S, Najar A, *Palminteri S, & *Lebreton M. Comparing the test-retest reliability of behavioral, computational and self-reported individual measures of reward and punishment sensitivity in relation to mental health symptoms. Nature Mental Health (2025).

• Yax N, *Ourdeyer PY, *Palminteri S. PhyloLM: Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks. ICLR (2025).

• Anllo H, ... & Palminteri S. Comparing experience- and description-based economic preferences across 11 countries. Nature Human Behaviour (2024). 

•  *Yax N, *Anllo H, Palminteri S. Studying and improving reasoning in humans and machines. Communications Psychology (2024).

• Bavard S & Palminteri S. The functional form of value normalization in human reinforcement learning. eLife (2023).

• Garcia B, Bourgeois-Gironde S, Lebreton M and Palminteri S. Experiential values are underweighted in decisions involving symbolic options. Nature Human Behaviour (2023).

• Palminteri S & Lebreton M. The computational roots of positivity and confirmation biases in reinforcement-learning. TICS (2022). INSERM press release

• Palminteri S & Lebreton M. Context-dependent outcome encoding in human reinforcement learning. Current Opinion in Behavioral Sciences (2021)

• Bavard S, Rustichini A, Palminteri S. Two sides of the same coin: beneficial and detrimental consequences of range adaptation in human reinforcement learning. Science Advances  (2021). ENS press release 

• Garcia B,  Cerrotti F, Palminteri S. The description-experience gap: a challenge for the neuroeconomics of decision-making under uncertainty. Philosophical Transactions B (2021).

• Najar A, Bonnet E, Bahador B, Palminteri S. The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning.  Plos Biology (2020).  INSERM press release.

• *Chambon V, *Théro H, Vidal M, Vandendriesch H, Haggard P, Palminteri S. Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behaviour (2020). ENS press release. Scientific American.

• Lebreton M, Bavard S, Daunizeau J, Palminteri S. Assessing inter-individual differences with task-related functional neuroimaging.  Nature Human Behaviour (2019).

• Lefebvre G, Nioche A, Bourgeois-Gironde S, Palminteri S. Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm. PNAS (2018). CNRS press release. 

• *Bavard S, *Lebreton M, Khamassi M, Coricelli G, Palminteri S.   Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nature Communications (2018). INSERM press release. 

• Lefebvre G, Lebreton M, Meyniel F, Bourgeois-Gironde S, Palminteri S. Behavioural and neural characterization of optimistic reinforcement learning. Nature Human Behaviour (2017). INSERM press release .

• *Palminteri S, *Wyart V, Koechlin E. The importance of falsification in computational cognitive modeling. TICS (2017).

• Palminteri S, Khamassi M, Joffily M, Coricelli G. Contextual modulation of value signals in reward and punishment learning. Nature Communications (2015). CNRS press release.

• Palminteri S, Justo D, Jauffret C, Pavlicek B, Dauta A, Delmaire C, Czernecki V, Karachi K, Capelle L, Durr A, Pessiglione M. Critical roles for anterior insula and dorsal striatum in punishment-based avoidance learning. Neuron (2012). INSERM press release.

• Palminteri S, Lebreton M, Worbe Y, Hartmann A, Vidailhet M, Grabli D, Pessiglione M. Dopamine-dependent reinforcement of motor skill learning: evidence from Tourette’s syndrome. Brain (2011).  INSERM press release .

• Palminteri S, Lebreton M, Worbe Y, Grabli D, Hartmann A, Pessiglione M.  Pharmacological modulation ofsubliminal learning in Parkinson's and Tourette's syndromes. PNAS (2009).

PUBLICATIONS IN FRENCH

• Comme faire les bons choix? (avec A. Clavere). Cerveau et Psycho (2025).

• Vers une psychologie des intelligences artificielles. Cerveau et Psycho (2024).

• L'optimisme : une erreur utile ? Cerveau et Psycho (2022).