Mehdi Keramati

1.6k total citations
19 papers, 874 citations indexed

About

Mehdi Keramati is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and General Decision Sciences. According to data from OpenAlex, Mehdi Keramati has authored 19 papers receiving a total of 874 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 7 papers in Cellular and Molecular Neuroscience and 7 papers in General Decision Sciences. Recurrent topics in Mehdi Keramati's work include Neural and Behavioral Psychology Studies (10 papers), Decision-Making and Behavioral Economics (7 papers) and Neurotransmitter Receptor Influence on Behavior (7 papers). Mehdi Keramati is often cited by papers focused on Neural and Behavioral Psychology Studies (10 papers), Decision-Making and Behavioral Economics (7 papers) and Neurotransmitter Receptor Influence on Behavior (7 papers). Mehdi Keramati collaborates with scholars based in United Kingdom, France and Germany. Mehdi Keramati's co-authors include Boris Gutkin, Amir Dezfouli, Payam Piray, Raymond J. Dolan, Peter Dayan, Peter Smittenaar, Rani Moran, Ádám Kepecs, Armin Lak and Masamichi Sakagami and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.

In The Last Decade

Mehdi Keramati

19 papers receiving 861 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mehdi Keramati United Kingdom 12 598 189 162 116 107 19 874
Payam Piray United States 13 508 0.8× 186 1.0× 157 1.0× 88 0.8× 75 0.7× 20 767
Aaron M. Bornstein United States 14 661 1.1× 131 0.7× 154 1.0× 104 0.9× 59 0.6× 32 842
Amir Dezfouli Australia 14 778 1.3× 215 1.1× 329 2.0× 104 0.9× 143 1.3× 23 1.2k
Wako Yoshida Japan 12 822 1.4× 165 0.9× 85 0.5× 95 0.8× 110 1.0× 22 1.2k
Alireza Soltani United States 19 1.3k 2.2× 155 0.8× 217 1.3× 314 2.7× 105 1.0× 44 1.6k
Reka Daniel United States 8 582 1.0× 142 0.8× 73 0.5× 64 0.6× 59 0.6× 9 757
Florent Meyniel France 20 1.2k 2.0× 243 1.3× 88 0.5× 142 1.2× 136 1.3× 32 1.5k
Nicolas W. Schuck Germany 18 1.1k 1.9× 155 0.8× 214 1.3× 54 0.5× 120 1.1× 45 1.3k
Andra Geana United States 6 428 0.7× 141 0.7× 53 0.3× 99 0.9× 76 0.7× 9 681
Jiefeng Jiang United States 22 975 1.6× 245 1.3× 52 0.3× 104 0.9× 49 0.5× 50 1.3k

Countries citing papers authored by Mehdi Keramati

Since Specialization
Citations

This map shows the geographic impact of Mehdi Keramati'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 Mehdi Keramati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mehdi Keramati more than expected).

Fields of papers citing papers by Mehdi Keramati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mehdi Keramati. 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 Mehdi Keramati. The network helps show where Mehdi Keramati may publish in the future.

Co-authorship network of co-authors of Mehdi Keramati

This figure shows the co-authorship network connecting the top 25 collaborators of Mehdi Keramati. A scholar is included among the top collaborators of Mehdi Keramati 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 Mehdi Keramati. Mehdi Keramati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Beheshti, Mohammad Taghi Hamidi, et al.. (2021). Developing a Reinforcement Learning Algorithm to Model Pavlovian Approach Bias on Bidirectional Planning. The Neuroscience Journal of Shefaye Khatam. 9(4). 51–59. 1 indexed citations
2.
Moran, Rani, Mehdi Keramati, & Raymond J. Dolan. (2021). Model based planners reflect on their model-free propensities. PLoS Computational Biology. 17(1). e1008552–e1008552. 6 indexed citations
3.
Moran, Rani, Mehdi Keramati, Peter Dayan, & Raymond J. Dolan. (2019). Retrospective model-based inference guides model-free credit assignment. Nature Communications. 10(1). 750–750. 26 indexed citations
4.
Shahar, Nitzan, Tobias U. Hauser, Michael Moutoussis, et al.. (2019). Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling. PLoS Computational Biology. 15(2). e1006803–e1006803. 89 indexed citations
5.
Dezfouli, Amir, et al.. (2019). Optimizing the depth and the direction of prospective planning using information values. PLoS Computational Biology. 15(3). e1006827–e1006827. 12 indexed citations
6.
Hertz, Uri, Bahador Bahrami, & Mehdi Keramati. (2018). Stochastic satisficing account of confidence in uncertain value-based decisions. PLoS ONE. 13(4). e0195399–e0195399. 9 indexed citations
7.
Keramati, Mehdi, et al.. (2018). Behavioural signatures of backward planning in animals. European Journal of Neuroscience. 47(5). 479–487. 5 indexed citations
8.
Keramati, Mehdi, et al.. (2017). Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans. PLoS Computational Biology. 13(9). e1005753–e1005753. 6 indexed citations
9.
Navajas, Joaquín, et al.. (2017). The idiosyncratic nature of confidence. Nature Human Behaviour. 1(11). 810–818. 63 indexed citations
10.
Keramati, Mehdi, et al.. (2017). Cocaine addiction as a homeostatic reinforcement learning disorder.. Psychological Review. 124(2). 130–153. 35 indexed citations
11.
Keramati, Mehdi, Serge H. Ahmed, & Boris Gutkin. (2017). Misdeed of the need: towards computational accounts of transition to addiction. Current Opinion in Neurobiology. 46. 142–153. 13 indexed citations
12.
Lak, Armin, Kensaku Nomoto, Mehdi Keramati, Masamichi Sakagami, & Ádám Kepecs. (2017). Midbrain Dopamine Neurons Signal Belief in Choice Accuracy during a Perceptual Decision. Current Biology. 27(6). 821–832. 100 indexed citations
13.
Keramati, Mehdi, Peter Smittenaar, Raymond J. Dolan, & Peter Dayan. (2016). Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum. Proceedings of the National Academy of Sciences. 113(45). 12868–12873. 112 indexed citations
14.
Keramati, Mehdi & Boris Gutkin. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. eLife. 3. 114 indexed citations
15.
Gutkin, Boris & Mehdi Keramati. (2013). Drug dominated dopamine circuits spiral addicts into a cognitive behavioral conflict.. PLoS ONE. 8(4). 1 indexed citations
16.
Keramati, Mehdi & Boris Gutkin. (2013). Imbalanced Decision Hierarchy in Addicts Emerging from Drug-Hijacked Dopamine Spiraling Circuit. PLoS ONE. 8(4). e61489–e61489. 34 indexed citations
17.
Keramati, Mehdi & Boris Gutkin. (2012). Drug-dominated dopamine circuits spiral addicts down to a cognitive/behavioral conflict: a neurocomputational theory. BMC Neuroscience. 13(S1). 1 indexed citations
18.
Keramati, Mehdi & Boris Gutkin. (2011). A Reinforcement Learning Theory for Homeostatic Regulation. City Research Online (City University London). 24. 82–90. 18 indexed citations
19.
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

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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