Perception, learning and decision making

• • Cognitive biases: the blind spot of decision making (UPA, 12/2025)

  • The metacognitive control of decisions: computational, behavioral and neural perspectives (Gershman lab, 01/2023)

  • A (narrow) computational perspective on mentalizing (Google DeepMind, 08/2022, 11/2022)

  • Hebbian plasticity, range adaptation in the OFC and loss aversion (Paris 10/2021, London 12/2021, Saclay 05/2022, Dresden 11/2022)

  • The bayesian perspective on psychiatric disorders (Paris 06/2019, Paris 06/2020)

  • Is laziness contagious? A computational approach to attitude aligngment (Lyon 05/2019, Paris 06/2019, Norwich 11/2019, Paris 06/2023)

  • Playing with Theory of Mind (Marseille 12/2017, Paris 10/2018, Lyon 10/2018)

  • The social sense: lessons from Bayesian Decision Theory (Oxford 02/2017, MSE Paris 03/2017, LPC Marseille 04/2017, ENSAE Paris 05/2017, SMART workshop 09/2017, ICM/CENTRALE workshop 09/2017)

  • Theory of Mind: the social sense (Imagine Institute 06/2016, Ste Anne Hospital 09/2016, Salpêtrière Hospital 01/2017)

  • Meta-Bayesian modelling of mentalizing (Université Cergy-Pontoise, 11/2015)

  • The evolution of mentalizing: a computational, behavioural and ethological approach (Zurich, 09/2014)

  • The Bayesian brain: an evolutionary approach to cognition (Paris, Cogmaster CA4, 04/2014, 04/2015, 04/2016, 04/2017 ; eco&psycho MSc 10/2017, INSEAD 10/2017, ENS-Lyon 10/2018).

  • The social bayesian brain: does mentalizing matter when we learn? (Lyon, 03/2014)

  • Biophysical, computational and statistical models for perception, learning and decision making (HDR viva, Paris, 04/2013)

  • Theory of Mind: lessons from Inverse Bayesian Decision Theory (Berlin, 11/2012, London, 01/2013, Paris 05/2013)

  • The Bayesian brain and the Free Energy Principle (Paris, CogMaster CA4, 04/2011)

  • Observing the observer: meta-Bayesian models of learning and decision making (London, 09/2010)

  • Ensemble coding (London, 03/2009)

  • Observing the observer: neuroimaging insights (Zurich, 06/2008)

  • Observing the observer (London 05/2008)

  • Associative learning, reinforcement learning and Bayesian learning (London, 12/2006)

  • Bayesian perspective on control theory (London, 12/2006)

Classical/Bayesian statistics

• • Bayesian model selection (Zurich, SPM short course, 02/2015, 02/2016)

  • optimizing experimental design for Bayesian model comparison (London, 11/2009)

  • Multivariate Bayesian (MVB) decoding of brain images (Edinburgh SPM short course 2010)

  • First-level analysis and contrasts in SPM (Edinburgh SPM short course 2010)

  • Second-level (random effects) analysis in SPM (Edinburgh SPM short course 2010)

  • Bayesian inference (London/Zurich/Edinburgh, SPM short course 2007/2008/2009/2010)

  • variational Bayesian identification of stochastic systems (Berlin, 01/2009)

  • Bayesian decision theory (London, 03/2008)

  • Approximations to the model evidence (London. 06/2008)

  • an introduction to stochastic dynamics (Melbourne, 06/2008)

  • deterministic annealing (St Petersburg, 06/2007)

  • particle filtering (London, 02/2006)

Dynamical systems

• • Dynamic Causal Modelling of brain-behaviour relationships (London, 12/2014, Paris 01/2016, Lausanne 09/2016, IHPST workshop 10/2017, Montréal 04/2018)

  • Model-based analysis of brain connectivity: where are we going? (Cologne, 10/2012)

  • stochastic DCM for fMRI: should we care about neural noise? (Maastricht, 05/2011)

  • Dynamic Causal Modelling of neuroimaging data (Barcelona, 06/2010)

  • stochastic dynamic causal modelling (Vancouver, 12/2009)

  • Dynamic Causal Modelling: basics (London, 10/2009)

  • interacting neural fields (London, 06/2009)

Data analysis methods for EEG/MEG/fMRI/behavior

• • Model-based assessment of brain-behavior relationships (Marseille, 01/2021)

  • fMRI statistical data analysis: an introduction (Paris, Master 1, faculty of medicine, UP7, 02/2015, 02/2016, 02/2017)

  • introductory slides on SPM for EEG/MEG (Seoul, 11/2008)

  • EEG/MEG source reconstruction in SPM (Seoul, 11/2008)

  • mesostate-space model for EEG/MEG (Rome, 09/2007)

  • PhD  viva on EEG-fMRI fusion (Paris, 11/2005 -short version-)

  • Bayesian model comparison for fMRI-constrained EEG inverse problem (Montreal, 02/2005)

misc

• • Cognitive biases: experimental evidence (Paris 11/2021)

  • BRAiN'US: a tool for computational psychology (Montreal, 04/2018)

  • Crowdsourcing brain experiments: the BRAiN'US project (EpiTech 11/2015, COGITER 10/2016, Pint of Science 03/2017)

  • Maths, brain and behaviour: a few tricks (INS Marseille, 02/2016)