Payam Piray

1.4k total citations
20 papers, 767 citations indexed

About

Payam Piray is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Payam Piray has authored 20 papers receiving a total of 767 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 6 papers in Cellular and Molecular Neuroscience and 5 papers in Artificial Intelligence. Recurrent topics in Payam Piray's work include Neural and Behavioral Psychology Studies (7 papers), Neural dynamics and brain function (6 papers) and Neurotransmitter Receptor Influence on Behavior (6 papers). Payam Piray is often cited by papers focused on Neural and Behavioral Psychology Studies (7 papers), Neural dynamics and brain function (6 papers) and Neurotransmitter Receptor Influence on Behavior (6 papers). Payam Piray collaborates with scholars based in United States, Netherlands and Iran. Payam Piray's co-authors include Amir Dezfouli, Nathaniel D. Daw, Mehdi Keramati, Roshan Cools, Ivan Toni, Shan Luo, John Monterosso, Tom Heskes, Michael J. Frank and Caro Lucas and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Payam Piray

19 papers receiving 753 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Payam Piray United States 13 508 186 157 88 78 20 767
Klaus Wunderlich United Kingdom 13 984 1.9× 147 0.8× 215 1.4× 111 1.3× 59 0.8× 16 1.3k
Michael J. Frank United States 4 513 1.0× 184 1.0× 63 0.4× 93 1.1× 54 0.7× 5 761
Wolfgang M. Pauli United States 12 555 1.1× 154 0.8× 144 0.9× 34 0.4× 108 1.4× 17 817
Mehdi Keramati United Kingdom 12 598 1.2× 189 1.0× 162 1.0× 116 1.3× 45 0.6× 19 874
Reka Daniel United States 8 582 1.1× 142 0.8× 73 0.5× 64 0.7× 48 0.6× 9 757
Archy O. de Berker United Kingdom 13 746 1.5× 148 0.8× 93 0.6× 55 0.6× 57 0.7× 15 1.0k
Aaron M. Bornstein United States 14 661 1.3× 131 0.7× 154 1.0× 104 1.2× 44 0.6× 32 842
Jacqueline Scholl United Kingdom 13 599 1.2× 138 0.7× 83 0.5× 63 0.7× 55 0.7× 25 759
Jeffrey Cockburn United States 11 600 1.2× 151 0.8× 72 0.5× 73 0.8× 163 2.1× 16 824
Ian C. Ballard United States 12 405 0.8× 123 0.7× 97 0.6× 93 1.1× 38 0.5× 20 589

Countries citing papers authored by Payam Piray

Since Specialization
Citations

This map shows the geographic impact of Payam Piray's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Payam Piray with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Payam Piray more than expected).

Fields of papers citing papers by Payam Piray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Payam Piray. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Payam Piray. The network helps show where Payam Piray may publish in the future.

Co-authorship network of co-authors of Payam Piray

This figure shows the co-authorship network connecting the top 25 collaborators of Payam Piray. A scholar is included among the top collaborators of Payam Piray based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Payam Piray. Payam Piray is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Piray, Payam & Nathaniel D. Daw. (2025). Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map. Nature Communications. 16(1). 7444–7444. 1 indexed citations
2.
Piray, Payam. (2025). Addressing low statistical power in computational modelling studies in psychology and neuroscience. Nature Human Behaviour. 10(2). 347–356.
3.
Mahmoodi, Ali, et al.. (2024). Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction. Neuron. 112(22). 3796–3809.e9. 3 indexed citations
4.
Piray, Payam & Nathaniel D. Daw. (2024). Computational processes of simultaneous learning of stochasticity and volatility in humans. Nature Communications. 15(1). 9073–9073. 6 indexed citations
5.
Kennedy, Brendan, et al.. (2024). Reinforced Multiple Instance Selection for Speaker Attribute Prediction. 3307–3321. 1 indexed citations
6.
Piray, Payam, et al.. (2023). Goal-directed and habitual decision making under stress in gambling disorder: An fMRI study. Addictive Behaviors. 140. 107628–107628. 9 indexed citations
7.
Piray, Payam & Nathaniel D. Daw. (2021). A model for learning based on the joint estimation of stochasticity and volatility. Nature Communications. 12(1). 6587–6587. 66 indexed citations
8.
Piray, Payam & Nathaniel D. Daw. (2021). Linear reinforcement learning in planning, grid fields, and cognitive control. Nature Communications. 12(1). 4942–4942. 37 indexed citations
9.
Piray, Payam & Nathaniel D. Daw. (2020). A simple model for learning in volatile environments. PLoS Computational Biology. 16(7). e1007963–e1007963. 42 indexed citations
10.
Piray, Payam, Amir Dezfouli, Tom Heskes, Michael J. Frank, & Nathaniel D. Daw. (2019). Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Computational Biology. 15(6). e1007043–e1007043. 70 indexed citations
11.
Piray, Payam, Verena Ly, Karin Roelofs, Roshan Cools, & Ivan Toni. (2018). Emotionally Aversive Cues Suppress Neural Systems Underlying Optimal Learning in Socially Anxious Individuals. Journal of Neuroscience. 39(8). 1445–1456. 33 indexed citations
12.
Timmer, Monique H.M., Guillaume Sescousse, Rianne A.J. Esselink, Payam Piray, & Roshan Cools. (2017). Mechanisms Underlying Dopamine-Induced Risky Choice in Parkinson’s Disease With and Without Depression (History). SHILAP Revista de lepidopterología. 2(0). 11–11. 13 indexed citations
13.
Piray, Payam, Ivan Toni, & Roshan Cools. (2016). Human Choice Strategy Varies with Anatomical Projections from Ventromedial Prefrontal Cortex to Medial Striatum. Journal of Neuroscience. 36(10). 2857–2867. 27 indexed citations
14.
Piray, Payam, Hanneke E.M. den Ouden, Marieke E. van der Schaaf, Ivan Toni, & Roshan Cools. (2015). Dopaminergic Modulation of the Functional Ventrodorsal Architecture of the Human Striatum. Cerebral Cortex. bhv243–bhv243. 44 indexed citations
15.
Piray, Payam, Yashar Zeighami, Fariba Bahrami, et al.. (2014). Impulse Control Disorders in Parkinson's Disease Are Associated with Dysfunction in Stimulus Valuation But Not Action Valuation. Journal of Neuroscience. 34(23). 7814–7824. 62 indexed citations
16.
Monterosso, John, Payam Piray, & Shan Luo. (2012). Neuroeconomics and the Study of Addiction. Biological Psychiatry. 72(2). 107–112. 53 indexed citations
17.
Piray, Payam. (2011). The Role of Dorsal Striatal D2-Like Receptors in Reversal Learning: A Reinforcement Learning Viewpoint: Figure 1.. Journal of Neuroscience. 31(40). 14049–14050. 8 indexed citations
18.
Keramati, Mehdi, Amir Dezfouli, & Payam Piray. (2011). Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes. PLoS Computational Biology. 7(5). e1002055–e1002055. 229 indexed citations
19.
Piray, Payam, et al.. (2010). Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-Like Behavior: A Computational Approach. Neural Computation. 22(9). 2334–2368. 30 indexed citations
20.
Dezfouli, Amir, et al.. (2009). A Neurocomputational Model for Cocaine Addiction. Neural Computation. 21(10). 2869–2893. 33 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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