David Mathar

976 total citations
20 papers, 566 citations indexed

About

David Mathar is a scholar working on Cognitive Neuroscience, Clinical Psychology and Cellular and Molecular Neuroscience. According to data from OpenAlex, David Mathar has authored 20 papers receiving a total of 566 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 10 papers in Clinical Psychology and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in David Mathar's work include Neural and Behavioral Psychology Studies (11 papers), Eating Disorders and Behaviors (7 papers) and Neurotransmitter Receptor Influence on Behavior (5 papers). David Mathar is often cited by papers focused on Neural and Behavioral Psychology Studies (11 papers), Eating Disorders and Behaviors (7 papers) and Neurotransmitter Receptor Influence on Behavior (5 papers). David Mathar collaborates with scholars based in Germany, France and United Kingdom. David Mathar's co-authors include Annette Horstmann, Arno Villringer, Jane Neumann, Jan Peters, Karima Chakroun, Burkhard Pleger, Antonius Wiehler, Florian Ganzer, Arne Dietrich and Michael Stümvoll and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

David Mathar

19 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Mathar Germany 13 245 195 96 89 83 20 566
Andrea Kobiella Germany 13 313 1.3× 212 1.1× 69 0.7× 147 1.7× 186 2.2× 17 804
Savani Bartholdy United Kingdom 17 241 1.0× 723 3.7× 135 1.4× 66 0.7× 31 0.4× 27 997
Aidan Makwana United Kingdom 8 110 0.4× 131 0.7× 54 0.6× 179 2.0× 103 1.2× 10 596
Ilka Boehm Germany 15 266 1.1× 695 3.6× 98 1.0× 79 0.9× 25 0.3× 25 829
Melanie Canterberry United States 16 442 1.8× 155 0.8× 20 0.2× 30 0.3× 126 1.5× 25 798
Jeffrey M. Engelmann United States 16 371 1.5× 160 0.8× 128 1.3× 23 0.3× 210 2.5× 26 935
Jennifer Hagman United States 17 232 0.9× 842 4.3× 56 0.6× 74 0.8× 39 0.5× 26 1.1k
Ignacio Martínez‐Zalacaín Spain 21 494 2.0× 451 2.3× 37 0.4× 34 0.4× 75 0.9× 61 984
Ozlem Korucuoglu United States 11 193 0.8× 155 0.8× 106 1.1× 14 0.2× 40 0.5× 13 432
Daisy G.Y. Thompson-Lake United States 17 189 0.8× 67 0.3× 56 0.6× 17 0.2× 128 1.5× 25 696

Countries citing papers authored by David Mathar

Since Specialization
Citations

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

Fields of papers citing papers by David Mathar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Mathar

This figure shows the co-authorship network connecting the top 25 collaborators of David Mathar. A scholar is included among the top collaborators of David Mathar 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 David Mathar. David Mathar 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.
Zhang, Lei, et al.. (2025). Dopamine and Temporal Discounting: Revisiting Pharmacology and Individual Differences. Journal of Neuroscience. 45(49). e0786252025–e0786252025.
2.
Mathar, David, et al.. (2025). Signatures of heuristic-based directed exploration in two-step sequential decision task behaviour. PubMed. 9(1). 39–62. 1 indexed citations
3.
Chakroun, Karima, Antonius Wiehler, David Mathar, et al.. (2023). Dopamine regulates decision thresholds in human reinforcement learning in males. Nature Communications. 14(1). 5369–5369. 17 indexed citations
4.
5.
Mathar, David, et al.. (2022). Gambling Environment Exposure Increases Temporal Discounting but Improves Model-Based Control in Regular Slot-Machine Gamblers. SHILAP Revista de lepidopterología. 6(1). 142–165. 9 indexed citations
6.
Mathar, David, et al.. (2022). The catecholamine precursor Tyrosine reduces autonomic arousal and decreases decision thresholds in reinforcement learning and temporal discounting. PLoS Computational Biology. 18(12). e1010785–e1010785. 10 indexed citations
7.
Mathar, David, et al.. (2022). Parameter and Model Recovery of Reinforcement Learning Models for Restless Bandit Problems. Computational Brain & Behavior. 5(4). 547–563. 12 indexed citations
8.
Horstmann, Annette, et al.. (2020). Dopamine release, diffusion and uptake: A computational model for synaptic and volume transmission. PLoS Computational Biology. 16(11). e1008410–e1008410. 16 indexed citations
9.
Chakroun, Karima, David Mathar, Antonius Wiehler, Florian Ganzer, & Jan Peters. (2020). Dopaminergic modulation of the exploration/exploitation trade-off in human decision-making. eLife. 9. 66 indexed citations
10.
Mathar, David, et al.. (2018). A potential link between gambling addiction severity and central dopamine levels: Evidence from spontaneous eye blink rates. Scientific Reports. 8(1). 13371–13371. 7 indexed citations
11.
Mathar, David, et al.. (2018). Retraining automatic action tendencies in obesity. Physiology & Behavior. 192. 50–58. 27 indexed citations
12.
Mathar, David, et al.. (2017). Altered monetary loss processing and reinforcement-based learning in individuals with obesity. Brain Imaging and Behavior. 12(5). 1431–1449. 34 indexed citations
13.
Mathar, David, Jane Neumann, Arno Villringer, & Annette Horstmann. (2017). Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism. Cortex. 95. 222–237. 35 indexed citations
14.
15.
Mathar, David, Annette Horstmann, Burkhard Pleger, Arno Villringer, & Jane Neumann. (2016). Is it Worth the Effort? Novel Insights into Obesity-Associated Alterations in Cost-Benefit Decision-Making. Frontiers in Behavioral Neuroscience. 9. 360–360. 31 indexed citations
16.
Dietrich, Arne, et al.. (2016). Brain regulation of food craving: relationships with weight status and eating behavior. International Journal of Obesity. 40(6). 982–989. 52 indexed citations
17.
Mathar, David, et al.. (2016). Stopping at the sight of food – How gender and obesity impact on response inhibition. Appetite. 107. 663–676. 20 indexed citations
18.
Horstmann, Annette, et al.. (2014). Slave to habit? Obesity is associated with decreased behavioural sensitivity to reward devaluation. Appetite. 87. 175–183. 87 indexed citations
19.
Godemann, F., et al.. (2013). Behandlungspfade in der stationären Alkoholentzugsbehandlung – Effekte auf die Prozess- und Ergebnisqualität. SUCHT - Zeitschrift für Wissenschaft und Praxis / Journal of Addiction Research and Practice. 59(2). 81–89. 1 indexed citations
20.
Horstmann, Annette, Franziska P. Busse, David Mathar, et al.. (2011). Obesity-Related Differences between Women and Men in Brain Structure and Goal-Directed Behavior. Frontiers in Human Neuroscience. 5. 58–58. 124 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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