Peter D. Kvam

1.1k total citations
38 papers, 372 citations indexed

About

Peter D. Kvam is a scholar working on General Decision Sciences, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Peter D. Kvam has authored 38 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in General Decision Sciences, 13 papers in Cognitive Neuroscience and 6 papers in Artificial Intelligence. Recurrent topics in Peter D. Kvam's work include Decision-Making and Behavioral Economics (19 papers), Neural and Behavioral Psychology Studies (11 papers) and Behavioral Health and Interventions (6 papers). Peter D. Kvam is often cited by papers focused on Decision-Making and Behavioral Economics (19 papers), Neural and Behavioral Psychology Studies (11 papers) and Behavioral Health and Interventions (6 papers). Peter D. Kvam collaborates with scholars based in United States, Germany and Australia. Peter D. Kvam's co-authors include Jerome R. Busemeyer, Timothy J. Pleskac, Shuli Yu, Brandon M. Turner, Andrew Heathcote, A. A. J. Marley, Erick Janssen, Stephanie A. Sanders, Jasmin Vassileva and Brandon J. Hill and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Psychological Review and Scientific Reports.

In The Last Decade

Peter D. Kvam

36 papers receiving 349 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter D. Kvam United States 12 143 139 83 60 54 38 372
Stephanie Stolarz‐Fantino United States 10 88 0.6× 180 1.3× 58 0.7× 60 1.0× 12 0.2× 26 404
Valerie M. Chase Switzerland 4 70 0.5× 68 0.5× 39 0.5× 42 0.7× 13 0.2× 5 277
Vincenzo Crupi Italy 15 100 0.7× 219 1.6× 396 4.8× 77 1.3× 16 0.3× 51 758
Peter B. M. Vranas United States 12 183 1.3× 40 0.3× 56 0.7× 81 1.4× 9 0.2× 26 547
Rachael Briggs United States 12 91 0.6× 60 0.4× 102 1.2× 182 3.0× 13 0.2× 20 445
Benjamin M. Rottman United States 12 66 0.5× 85 0.6× 173 2.1× 68 1.1× 3 0.1× 49 572
Jonah N. Schupbach United States 12 92 0.6× 58 0.4× 123 1.5× 67 1.1× 5 0.1× 24 484
Brian Hedden Australia 13 195 1.4× 65 0.5× 45 0.5× 150 2.5× 4 0.1× 26 497
Graham Oddie New Zealand 12 193 1.3× 22 0.2× 106 1.3× 186 3.1× 10 0.2× 45 687
Michael E. Doherty United States 9 80 0.6× 34 0.2× 22 0.3× 57 0.9× 10 0.2× 26 278

Countries citing papers authored by Peter D. Kvam

Since Specialization
Citations

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

Fields of papers citing papers by Peter D. Kvam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter D. Kvam. 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 Peter D. Kvam. The network helps show where Peter D. Kvam may publish in the future.

Co-authorship network of co-authors of Peter D. Kvam

This figure shows the co-authorship network connecting the top 25 collaborators of Peter D. Kvam. A scholar is included among the top collaborators of Peter D. Kvam 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 Peter D. Kvam. Peter D. Kvam 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.
Kvam, Peter D., et al.. (2025). Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice. Behavior Research Methods. 57(9). 252–252.
2.
Haines, Nathaniel, Peter D. Kvam, Colin Tucker Smith, et al.. (2025). A tutorial on using generative models to advance psychological science: Lessons from the reliability paradox.. Psychological Methods. 2 indexed citations
3.
Kvam, Peter D.. (2024). The Tweedledum and Tweedledee of dynamic decisions: Discriminating between diffusion decision and accumulator models. Psychonomic Bulletin & Review. 32(2). 588–613. 1 indexed citations
4.
Kvam, Peter D., et al.. (2023). How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices. Journal of Choice Modelling. 48. 100418–100418. 6 indexed citations
5.
Kertes, Darlene A., et al.. (2023). The Social Environment Matters for Telomere Length and Internalizing Problems During Adolescence. Journal of Youth and Adolescence. 53(1). 21–35. 2 indexed citations
6.
Kvam, Peter D., et al.. (2023). Improving the reliability and validity of the IAT with a dynamic model driven by similarity. Behavior Research Methods. 56(3). 2158–2193. 8 indexed citations
7.
Kvam, Peter D., et al.. (2023). Open system model of choice and response time. Journal of Choice Modelling. 49. 100453–100453. 5 indexed citations
8.
Haines, Nathaniel, Peter D. Kvam, & Brandon M. Turner. (2023). Explaining the description-experience gap in risky decision-making: learning and memory retention during experience as causal mechanisms. Cognitive Affective & Behavioral Neuroscience. 23(3). 557–577. 2 indexed citations
9.
Kvam, Peter D., A. A. J. Marley, & Andrew Heathcote. (2022). A unified theory of discrete and continuous responding.. Psychological Review. 130(2). 368–400. 14 indexed citations
10.
Kvam, Peter D., et al.. (2022). Rational inference strategies and the genesis of polarization and extremism. Scientific Reports. 12(1). 7344–7344. 6 indexed citations
11.
Kvam, Peter D. & Brandon M. Turner. (2021). Reconciling similarity across models of continuous selections.. Psychological Review. 128(4). 766–786. 9 indexed citations
12.
Kvam, Peter D., Jerome R. Busemeyer, & Timothy J. Pleskac. (2021). Temporal oscillations in preference strength provide evidence for an open system model of constructed preference. Scientific Reports. 11(1). 8169–8169. 22 indexed citations
13.
Kvam, Peter D., et al.. (2020). A dynamic model of deciding not to choose.. Journal of Experimental Psychology General. 150(1). 42–66. 4 indexed citations
14.
Kvam, Peter D. & Jerome R. Busemeyer. (2020). A distributional and dynamic theory of pricing and preference.. Psychological Review. 127(6). 1053–1078. 18 indexed citations
15.
Kvam, Peter D., et al.. (2020). Hierarchies improve individual assessment of temporal discounting behavior.. Decision. 7(3). 212–224. 7 indexed citations
16.
Busemeyer, Jerome R., Peter D. Kvam, & Timothy J. Pleskac. (2020). Comparison of Markov versus quantum dynamical models of human decision making. Wiley Interdisciplinary Reviews Cognitive Science. 11(4). e1526–e1526. 23 indexed citations
17.
Kvam, Peter D.. (2019). Modeling accuracy, response time, and bias in continuous orientation judgments.. Journal of Experimental Psychology Human Perception & Performance. 45(3). 301–318. 11 indexed citations
18.
Busemeyer, Jerome R., et al.. (2017). Neural implementation of operations used in quantum cognition. Progress in Biophysics and Molecular Biology. 130(Pt A). 53–60. 23 indexed citations
19.
Kvam, Peter D.. (2016). Geometric representations of evidence in models of decision-making.. Cognitive Science. 1 indexed citations
20.
Janssen, Erick, et al.. (2014). Patterns of Sexual Arousal in Young, Heterosexual Men Who Experience Condom-Associated Erection Problems (CAEP). PMC. 1 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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