| Speed/accuracy trade-off between the habitual and the goal-directed processes M Keramati, A Dezfouli, P Piray PLoS computational biology 7 (5), e1002055, 2011 | 464 | 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 | 106 | 2019 |
| A model for learning based on the joint estimation of stochasticity and volatility P Piray, ND Daw Nature communications 12 (1), 6587, 2021 | 89 | 2021 |
| 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 | 89 | 2014 |
| Neuroeconomics and the study of addiction J Monterosso, P Piray, S Luo Biological psychiatry 72 (2), 107-112, 2012 | 78 | 2012 |
| A simple model for learning in volatile environments P Piray, ND Daw PLoS computational biology, 2020 | 75 | 2020 |
| Linear reinforcement learning in planning, grid fields, and cognitive control P Piray, ND Daw Nature communications 12 (1), 4942, 2021 | 63 | 2021 |
| 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 | 61 | 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 | 57 | 2017 |
| 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 | 54 | 2019 |
| 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 | 48 | 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 | 46 | 2016 |
| Linear reinforcement learning: Flexible reuse of computation in planning, grid fields, and cognitive control P Piray, ND Daw bioRxiv, 856849, 2020 | 26* | 2020 |
| 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 | 23 | 2018 |
| 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 | 14 | 2011 |
| Goal-directed and habitual decision making under stress in gambling disorder: An fMRI study T van Timmeren, P Piray, AE Goudriaan, RJ van Holst Addictive behaviors 140, 107628, 2023 | 6 | 2023 |
| 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 | 6 | 2012 |
| Reinforced Multiple Instance Selection for Speaker Attribute Prediction AS Ziabari, A Omrani, P Hejabi, P Golazizian, B Kennedy, P Piray, ... Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 1 | 2024 |
| Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction A Mahmoodi, S Luo, C Harbison, P Piray, MFS Rushworth Neuron, 2023 | 1 | 2023 |
| Computational processes of simultaneous learning of stochasticity and volatility in humans P Piray, N Daw PsyArXiv, 2023 | 1 | 2023 |
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityVerified email at princeton.edu
