John C. Dunn

4.2k total citations
103 papers, 2.4k citations indexed

About

John C. Dunn is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Social Psychology. According to data from OpenAlex, John C. Dunn has authored 103 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Cognitive Neuroscience, 18 papers in Artificial Intelligence and 15 papers in Social Psychology. Recurrent topics in John C. Dunn's work include Memory Processes and Influences (24 papers), Deception detection and forensic psychology (11 papers) and Child and Animal Learning Development (9 papers). John C. Dunn is often cited by papers focused on Memory Processes and Influences (24 papers), Deception detection and forensic psychology (11 papers) and Child and Animal Learning Development (9 papers). John C. Dunn collaborates with scholars based in Australia, United States and United Kingdom. John C. Dunn's co-authors include Kim Kirsner, Ben R. Newell, Michael L. Kalish, Christopher Lee, Graham W. Taylor, Brett K. Hayes, Rachel Stephens, Laura Mickes, John T. Wixted and Carolyn Semmler and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Psychological Review and Trends in Cognitive Sciences.

In The Last Decade

John C. Dunn

89 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John C. Dunn Australia 24 1.6k 559 538 419 395 103 2.4k
Thomas D. Wickens United States 26 1.2k 0.7× 416 0.7× 396 0.7× 565 1.3× 390 1.0× 50 2.5k
Guy Lories Belgium 10 1.9k 1.2× 531 0.9× 449 0.8× 774 1.8× 221 0.6× 21 2.9k
Miguel A. Vadillo Spain 29 1.4k 0.9× 606 1.1× 647 1.2× 568 1.4× 270 0.7× 137 3.0k
Steve Joordens Canada 27 1.8k 1.2× 465 0.8× 759 1.4× 506 1.2× 249 0.6× 62 2.5k
Jeffrey P. Toth United States 23 2.0k 1.3× 532 1.0× 646 1.2× 416 1.0× 219 0.6× 31 2.3k
Christopher R. Madan United Kingdom 33 1.7k 1.1× 422 0.8× 556 1.0× 458 1.1× 180 0.5× 160 3.1k
Joachim Vandekerckhove United States 33 1.7k 1.1× 373 0.7× 259 0.5× 949 2.3× 402 1.0× 86 3.3k
Anjali Thapar United States 19 1.6k 1.0× 312 0.6× 340 0.6× 482 1.2× 190 0.5× 25 2.0k
Narayanan Srinivasan India 33 1.5k 1.0× 547 1.0× 231 0.4× 625 1.5× 135 0.3× 151 2.8k
Michaël Stevens Belgium 21 1.8k 1.2× 320 0.6× 1.2k 2.2× 769 1.8× 400 1.0× 28 3.3k

Countries citing papers authored by John C. Dunn

Since Specialization
Citations

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

Fields of papers citing papers by John C. Dunn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John C. Dunn. 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 John C. Dunn. The network helps show where John C. Dunn may publish in the future.

Co-authorship network of co-authors of John C. Dunn

This figure shows the co-authorship network connecting the top 25 collaborators of John C. Dunn. A scholar is included among the top collaborators of John C. Dunn 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 John C. Dunn. John C. Dunn 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.
Kellen, David, Clintin P. Davis‐Stober, John C. Dunn, et al.. (2025). Discourse on measurement. Proceedings of the National Academy of Sciences. 122(5). e2401229121–e2401229121. 1 indexed citations
2.
Hayes, Brett K., et al.. (2022). Always look on the bright side of logic? Testing explanations of intuitive sensitivity to logic in perceptual tasks.. Journal of Experimental Psychology Learning Memory and Cognition. 48(11). 1598–1617. 7 indexed citations
3.
Kellen, David, et al.. (2021). Testing the foundations of signal detection theory in recognition memory.. Psychological Review. 128(6). 1022–1050. 30 indexed citations
4.
Semmler, Carolyn, John C. Dunn, Laura Mickes, & John T. Wixted. (2018). The role of estimator variables in eyewitness identification.. Journal of Experimental Psychology Applied. 24(3). 400–415. 47 indexed citations
5.
Chan, Andrew G., et al.. (2018). Incidence of Cubital Tunnel Syndrome in the U.S. Military Population. The Journal Of Hand Surgery. 44(6). 516.e1–516.e7. 7 indexed citations
6.
Stephens, Rachel, John C. Dunn, & Brett K. Hayes. (2017). A Two-Step Signal Detection Model of Belief Bias.. Cognitive Science. 1 indexed citations
7.
Dunn, John C.. (2016). How reliable is an eyewitness. 37(4). 18. 1 indexed citations
8.
Dunn, John C., et al.. (2016). Vascular Injuries in Combat-Specific Soldiers during Operation Iraqi Freedom and Operation Enduring Freedom. Annals of Vascular Surgery. 35. 30–37. 21 indexed citations
9.
Kalish, Michael L., Ben R. Newell, & John C. Dunn. (2016). More is generally better: Higher working memory capacity does not impair perceptual category learning.. Journal of Experimental Psychology Learning Memory and Cognition. 43(4). 503–514. 13 indexed citations
10.
Stephens, Rachel, et al.. (2015). Exploring the knowledge behind predictions in everyday cognition: an iterated learning study. Memory & Cognition. 43(7). 1007–1020.
11.
Dunn, John C., et al.. (2015). The neural correlates of risky decision making across short and long runs. Scientific Reports. 5(1). 15831–15831. 5 indexed citations
12.
Donkin, Chris, Ben R. Newell, Michael L. Kalish, John C. Dunn, & Robert M. Nosofsky. (2014). Identifying strategy use in category learning tasks: A case for more diagnostic data and models.. Journal of Experimental Psychology Learning Memory and Cognition. 41(4). 933–948. 28 indexed citations
13.
Marmolejo‐Ramos, Fernando & John C. Dunn. (2013). Sobre la activación de sistemas sensorimotores durante el procesamiento de estímulos emocionalmente cargados. Universitas Psychologica. 12. 1515–1546. 2 indexed citations
14.
Dunn, John C., Ben R. Newell, & Michael L. Kalish. (2012). The effect of feedback delay and feedback type on perceptual category learning: The limits of multiple systems.. Journal of Experimental Psychology Learning Memory and Cognition. 38(4). 840–859. 42 indexed citations
15.
Kalish, Michael L. & John C. Dunn. (2011). What could cognitive neuroscience tell us about recognition memory?. Australian Journal of Psychology. 64(1). 29–36. 4 indexed citations
16.
Newell, Ben R., John C. Dunn, & Michael L. Kalish. (2010). The dimensionality of perceptual category learning: A state-trace analysis. Memory & Cognition. 38(5). 563–581. 52 indexed citations
17.
Dunn, John C., et al.. (2008). Decision making in civil disputes: The effects of legal role, frame, and perceived chance of winning. Judgment and Decision Making. 3(7). 512–527. 2 indexed citations
18.
Newell, Ben R. & John C. Dunn. (2008). Dimensions in data: testing psychological models using state-trace analysis. Trends in Cognitive Sciences. 12(8). 285–290. 71 indexed citations
19.
Dunn, John C., et al.. (2006). Russian language policy. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1 indexed citations
20.
Dunn, John C.. (2000). Model complexity: The fit to random data reconsidered. Psychological Research. 63(2). 174–182. 13 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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