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Mehdi Keramati

Mehdi Keramati

mehdi [dot] keramati [at] city [dot] ac [dot] uk


Short CV

  1. Lecturer in Psychology, 2018-present, City University London, UK
    Research Associate, 2017, Max Planck UCL Centre, UK

    Research Associate, 2016, Gatsby Computational Neuroscience Unit, UK
    PhD in Computational Neuroscience, 2013, Ecole Normale Superieure, France
    MSc in Economics, 2009, Sharif University, Iran
    BSc in Compute Engineering, 2006, University of Tehran, Iran


Research Interests

  • Neurocomputational mechanisms of interaction between habitual and goal-directed decision-making
  • Neurocomputational mechanisms of planning under cognitive limitations (plan-until-habit, forward/backward planning, pruning, successor representation, hierarchical RL, etc)
  • Neurocomputational mechanisms of homeostatic regulation and it's interaction with the brain reward system
  • Neurocomputational mechanisms underlying drug addiction

Selected Publications

Keramati, M., Durand, A., Girardeau, P., Gutkin, B., & Ahmed, S. H. (2017). Cocaine addiction as a homeostatic reinforcement learning disorder. Psychological Review, 124, 130–153. doi:10.1037/rev0000046

Keramati, M., Smittenaar, P., Dolan, R. J., & Dayan, P. (2016). Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum. Proceedings of the National Academy of Sciences of the United States of America, 113(45), 12868-12873. doi:10.1073/pnas.1609094113

Keramati, M., & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. eLife, 3, e04811. doi:10.7554/eLife.04811

Keramati, M., Dezfouli, A., & Piray, P. (2011). Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes. PLOS Computational Biology, 7(5), e1002055. doi:10.1371/journal.pcbi.1002055