Laura Fontanesi


Postdoctoral Researcher

Research Profiles: ResearchGateORCIDGitHub


Research Interests

  • Value-based Decision Making and Reinforcement Learning, in particular:
    • Sequential Sampling Models for Value-Based Decisions
    • Reinforcement Learning Models
    • Neural Bases of Reward- and Punishment-Based Decisions (using fMRI and EEG)
    • Linking Cognitive Models of Learning and Decision Making to Brain Measurements
  • Bayesian Statistics and Bayesian Modeling


Current Projects

  • Adapting sequential sampling models to the reinforcement learning paradigm, explain how learning performance is modulated by different value factors, use cognitive models to predict brain activity during reinforcement learning (in collaboration with Jörg Rieskamp, Sebastian Gluth, and Mikhail S. Spektor)
  • Understanding the role of different dopaminergic nuclei in the human brain in reward- and punishment-based decision making using 7 Tesla MRI (in collaboration with Birte U. Forstmann, Jörg Rieskamp, and Sebastian Gluth)
  • Sequential sampling modeling of foraging tasks (in collaboration with Sebastian Gluth and Amitai Shenhav)
  • Inspecting the reliability of sequential sampling model predictions for single decisions 



  • Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., . . ., Fontanesi, L.,. . .Schonberg, T. (2019). Variability in the analysis of a single neuroimaging dataset by manyteams. bioRxiv, 843193 (Preprint). doi: 10.1101/843193
  • Fontanesi, L., Gluth, S., Rieskamp, J., & Forstmann B. U. (2019). The role of dopaminergic nuclei in predicting and experiencing gains and losses: A 7T human fMRI study. bioRxiv 732560 (Preprint). doi:
  • Fontanesi, L. (2019). Insights into reward-based decisions using computational models and ultra-high field MRI (Doctoral Dissertation). doi: 10.5451/unibas-007108237
  • Fontanesi, L., Palminteri, S. & Lebreton, M. Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling. Cogn Affect Behav Neurosci 19, 490–502 (2019) doi: 10.3758/s13415-019-00723-1
  • Fontanesi, L., Gluth, S., Spektor, M. S., & Rieskamp, J. (2019). A reinforcement learning diffusion decision model for value-based decisions. Psychonomic bulletin & review.1-23. doi: 10.3758/s13423-018-1554-2
  • Spektor, M. S., Gluth, S., Fontanesi, L., & Rieskamp, J. (2019). How similarity between choice options affects decisions from experience: The accentuation-of-differences model. Psychological review126(1), 52. doi: 10.1037/rev0000122
  • Gluth, S., & Fontanesi, L. (2016). Wiring the altruistic brain. Science, 351(6277), 1028-1029. doi:10.1126/science.aaf4688
  • van Maanen, L., Fontanesi, L., Hawkins, G. E., & Forstmann, B. U. (2016). Striatal activation reflects urgency in perceptual decision making. NeuroImage, 139, 294-303. doi:10.1016/j.neuroimage.2016.06.045


Work History

  • Feb 2019-present: Postdoctoral Researcher, University of Basel
  • 2014-2019: Ph.D. Candidate, University of Basel
  • 2017: Visiting Researcher (SNF Doc.Mobility Fellowship), University of Amsterdam



  • spring 2020: Teaching the "Programming in R" master course at the University of Basel (Psychology Faculty)
  • spring 2020: Teaching the "Scientific writing" master course at the University of Basel (Psychology Faculty)
  • autumn 2019: Teaching the "Programming of experiments" research master course at the University of Basel (Psychology Faculty)
  • spring 2019: Teaching the "Programming in R" master course at the University of Basel (Psychology Faculty)
  • autumn 2018 / spring 2019: Teaching the "Project seminar" bachelor course at the University of Basel (Psychology Faculty)                     
  • 2014: Teaching assistant in the "Programming Skills: R & Matlab" research master course at the University of Amsterdam
  • 2011: Teaching assistant in the "Psychometrics" bachelor course at the University of Trento