Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Human Category Learning
2004642 citationsF. Gregory Ashby, W. Todd Maddoxprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by W. Todd Maddox
Since
Specialization
Citations
This map shows the geographic impact of W. Todd Maddox'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 W. Todd Maddox with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites W. Todd Maddox more than expected).
This network shows the impact of papers produced by W. Todd Maddox. 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 W. Todd Maddox. The network helps show where W. Todd Maddox may publish in the future.
Co-authorship network of co-authors of W. Todd Maddox
This figure shows the co-authorship network connecting the top 25 collaborators of W. Todd Maddox.
A scholar is included among the top collaborators of W. Todd Maddox 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 W. Todd Maddox. W. Todd Maddox 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.
Cormack, Lawrence K., et al.. (2017). Negative Affect-Associated USV Acoustic Characteristics Predict Future Excessive Alcohol Drinking and Alcohol Avoidance in Male P and NP Rats. PMC.2 indexed citations
Glass, Brian D., Marc Tomlinson, W. Todd Maddox, & Bradley C. Love. (2011). Becoming a gamer : cognitive effects of real-time strategy gaming. Cognitive Science. 33(33).2 indexed citations
8.
Maddox, W. Todd, Arthur B. Markman, & Darrell A. Worthy. (2009). Less is More: Stimulus Feedback Co-Occurence in Perceptual Category Learning. eScholarship (California Digital Library). 31(31).1 indexed citations
9.
Worthy, Darrell A., Arthur B. Markman, & W. Todd Maddox. (2009). Choking and Excelling at the Free Throw Line. The International Journal of Creativity and Problem Solving. 19(1). 53–58.23 indexed citations
Markman, Arthur B., et al.. (2007). Using Regulatory Focus to Explore Implicit and Explicit Processing in Concept Learning. Journal of Consciousness Studies. 14. 132–155.15 indexed citations
14.
Nomura, Emi, W. Todd Maddox, & Paul J. Reber. (2007). Mathematical Models of Visual Category Learning Enhance fMRI Data Analysis. eScholarship (California Digital Library). 29(29). 539–544.5 indexed citations
Jones, Matt, Bradley C. Love, & W. Todd Maddox. (2006). The Role of Similarity in Generalization. eScholarship (California Digital Library). 28(28).5 indexed citations
17.
Jones, Matt, Bradley C. Love, & W. Todd Maddox. (2005). Stimulus Generalization in Category Learning. eScholarship (California Digital Library). 27(27).9 indexed citations
18.
Bohil, Corey J., Arthur B. Markman, & W. Todd Maddox. (2005). A Feature-Salience Analogue of the Inverse Base-rate Effect. The International Journal of Creativity and Problem Solving. 15(1). 17–28.6 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.