Mehdi Keramati
Mehdi Keramati
MaxPlanck Centre for Computational Psychiatry, UCL
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Cited by
Cited by
Speed/accuracy trade-off between the habitual and the goal-directed processes
M Keramati, A Dezfouli, P Piray
PLoS Comput Biol 7 (5), e1002055, 2011
Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum
M Keramati, P Smittenaar, RJ Dolan, P Dayan
Proceedings of the National Academy of Sciences 113 (45), 12868-12873, 2016
Midbrain Dopamine Neurons Signal Belief in Choice Accuracy during a Perceptual Decision
A Lak, K Nomoto, M Keramati, M Sakagami, A Kepec
Current Biology, 2017
The idiosyncratic nature of confidence
J Navajas, C Hindocha, H Foda, M Keramati, PE Latham, B Bahrami
Nature Human Behavior 1 (11), 810-818, 2017
Homeostatic reinforcement learning for integrating reward collection and physiological stability
M Keramati, B Gutkin
Elife 3, e04811, 2014
A neurocomputational model for cocaine addiction
A Dezfouli, P Piray, MM Keramati, H Ekhtiari, C Lucas, A Mokri
Neural computation 21 (10), 2869-2893, 2009
Imbalanced decision hierarchy in addicts emerging from drug-hijacked dopamine spiraling circuit
M Keramati, B Gutkin
PloS one 8 (4), e61489, 2013
Individual differences in nucleus accumbens dopamine receptors predict development of addiction-like behavior: a computational approach
P Piray, MM Keramati, A Dezfouli, C Lucas, A Mokri
Neural computation 22 (9), 2334-2368, 2010
A reinforcement learning theory for homeostatic regulation
M Keramati, B Gutkin
Advances in neural information processing systems 24, 82-90, 2011
Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling
N Shahar, TU Hauser, M Moutoussis, R Moran, M Keramati, RJ Dolan
PLoS computational biology 15 (2), e1006803, 2019
Cocaine Addiction as a Homeostatic Reinforcement Learning Disorder
M Keramati, A Durand, P Girardeau, B Gutkin, A Serge
Psychological Review 125, 2017
Misdeed of the need: towards computational accounts of transition to addiction
M Keramati, S Ahmed, B Gutkin
Current Opinion in Neurobiology 46, 142–153, 2017
Retrospective model-based inference guides model-free credit assignment
R Moran, M Keramati, P Dayan, RJ Dolan
Nature communications 10 (1), 1-14, 2019
Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans
J Lee, M Keramati
PLoS computational biology 13 (9), 2017
Consortium, NSPN, Dolan, RJ (2019). Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion …
N Shahar, TU Hauser, M Moutoussis, R Moran, M Keramati
PLoS Comput. Biol 15, 1-25, 0
Optimizing the depth and the direction of prospective planning using information values
CE Sezener, A Dezfouli, M Keramati
PLoS computational biology 15 (3), e1006827, 2019
Stochastic satisficing account of confidence in uncertain value-based decisions
U Hertz, B Bahrami, M Keramati
PLoS One 13 (4), 2018
Understanding addiction as a pathological state of multiple decision making processes: a neurocomputational perspective
M Keramati, A Dezfouli, P Piray
Computational neuroscience of drug addiction, 205-233, 2012
Understanding Addictive Behavior on the Iowa Gambling Task UsingReinforcement Learning Framework
A Dezfouli, MM Keramati, H Ekhtiari, H Safaei, C Lucas
30th Annual Conference of the Cognitive Science Society, 1094-1099, 2007
Behavioural signatures of backward planning in animals
A Afsardeir, M Keramati
European Journal of Neuroscience 47 (5), 479-487, 2018
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