Payam Piray
Payam Piray
Princeton Neuroscience Institute
Verified email at - Homepage
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
Impulse control disorders in Parkinson's disease are associated with dysfunction in stimulus valuation but not action valuation
P Piray, Y Zeighami, F Bahrami, AM Eissa, DH Hewedi, AA Moustafa
Journal of Neuroscience 34 (23), 7814-7824, 2014
Neuroeconomics and the study of addiction
J Monterosso, P Piray, S Luo
Biological psychiatry 72 (2), 107-112, 2012
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
Dopaminergic modulation of the functional ventrodorsal architecture of the human striatum
P Piray, HEM den Ouden, ME van der Schaaf, I Toni, R Cools
Cerebral Cortex 27 (1), 485-495, 2017
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
Human choice strategy varies with anatomical projections from ventromedial prefrontal cortex to medial striatum
P Piray, I Toni, R Cools
Journal of Neuroscience 36 (10), 2857-2867, 2016
Emotionally aversive cues suppress neural systems underlying optimal learning in socially anxious individuals
P Piray, V Ly, K Roelofs, R Cools, I Toni
Journal of Neuroscience 39 (8), 1445-1456, 2019
The role of dorsal striatal D2-like receptors in reversal learning: a reinforcement learning viewpoint
P Piray
Journal of Neuroscience 31 (40), 14049-14050, 2011
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
P Piray, A Dezfouli, T Heskes, MJ Frank, ND Daw
PLoS computational biology 15 (6), e1007043, 2019
Mechanisms underlying dopamine-induced risky choice in Parkinson’s disease with and without depression (history)
MHM Timmer, G Sescousse, RAJ Esselink, P Piray, R Cools
Computational Psychiatry 2, 11-27, 2018
Linear reinforcement learning: Flexible reuse of computation in planning, grid fields, and cognitive control
P Piray, ND Daw
bioRxiv, 856849, 2020
A simple model for learning in volatile environments
P Piray, ND Daw
PLoS computational biology, 2020
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
Dopaminergic drugs decrease loss aversion in Parkinson’s disease with but not without depression
MHM Timmer, G Sescousse, RAJ Esselink, P Piray, R Cools
bioRxiv, 069047, 2016
Unpredictability vs. volatility and the control of learning
P Piray, ND Daw
bioRxiv, 2020
Compute to learn: Neural implementation of computations underlying associative learning and decision making
P Piray
[Sl: sn], 2016
Dopaminergic medication increases risky choice via decreasing loss aversion in depressed but not in non-depressed Parkinsonian patients: 166
M Timmer, G Sescousse, P Piray, R Esselink, R Cools
Movement Disorders 30, 2015
Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes. PLoS Comput Biol, 7 (5), e1002055
M Keramati, A Dezfouli, P Piray
Cornell University, Center for, 2011
Supplementary Methods
P Piray, A Dezfouli, T Heskes, MJ Frank, ND Daw
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