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Michael Moutoussis

Michael Moutoussis

m [dot] moutoussis [at] ucl [dot] ac [dot] uk
Honorary Fellow
London

Short CV

  • PhD in Clinical Psychology, 2011, University of Manchester, England
  • Specialist in Psychiatry (Medical Psychotherapy), GMC Specialist Register, 2009
  • MBBS with Distiction in Medicine and Pathology, 1993, University of London, England
  • BSc in Physics, 1st Class Honours, 1987, University of London, England

Research Interests

  • Computational psychiatry relevant to psychotherapeutic interventions. Specifically, relating learning and inference as functions of the (approximately Bayesian) brain to the theory and practical application of learning and inference within evidence-based psychotherapies.
  • Modeling how people evaluate and represent others in relation to themselves. For example, the computational psychiatry of conditions where others are experienced as threatening.

Key publications

  • Moutoussis, M., Shahar, N., Hauser, T.U. and Dolan, R.J. (2018) Computation in Psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry. https://doi.org/10.1162/CPSY_a_00014
  • Moutoussis, M., Dolan, R. J., & Dayan, P. (2016). How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences. PLoS Computational Biology, 12(7). http://dx.doi.org/10.1371/journal.pcbi.1004965
  • Nord, C. L., Prabhu, G., Nolte, T., Fonagy, P., Dolan, R., & Moutoussis, M. (2017). Vigour in active avoidance. Scientific reports, 7(1), 60. http://dx.doi.org/10.1038/s41598-017-00127-6
  • Moutoussis, M., Fearon P., El-Deredy, W., Dolan R.J. & Friston, K. Bayesian inferences about the self (and others): A review (2014), Consciousness and Cognition, 25, 67-76 (2014), http://dx.doi.org/10.1016/j.concog.2014.01.009
  • Moutoussis M., Bentall, R.P., El-Deredy, W. & Dayan, P., (2011) Bayesian modelling of Jumping-to-Conclusions in delusional patients. Cognitive Neuropsychiatry, 16:5, 422-447,  https://doi.org/10.1080/13546805.2010.548678


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