Philip J. Kellman

5.8k total citations · 1 hit paper
127 papers, 3.6k citations indexed

About

Philip J. Kellman is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology. According to data from OpenAlex, Philip J. Kellman has authored 127 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Cognitive Neuroscience, 20 papers in Computer Vision and Pattern Recognition and 18 papers in Experimental and Cognitive Psychology. Recurrent topics in Philip J. Kellman's work include Visual perception and processing mechanisms (68 papers), Neural dynamics and brain function (15 papers) and Visual and Cognitive Learning Processes (14 papers). Philip J. Kellman is often cited by papers focused on Visual perception and processing mechanisms (68 papers), Neural dynamics and brain function (15 papers) and Visual and Cognitive Learning Processes (14 papers). Philip J. Kellman collaborates with scholars based in United States, Italy and United Kingdom. Philip J. Kellman's co-authors include Thomas F. Shipley, Elizabeth S. Spelke, Patrick Garrigan, Kenneth R. Short, Nicholas Baker, Gennady Erlikhman, Hongjing Lu, Christine Massey, Carol Yin and Sally Krasne and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Psychological Review.

In The Last Decade

Philip J. Kellman

116 papers receiving 3.4k citations

Hit Papers

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What are hit papers?

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.

A theory of visual interpolation in object perception

1991 540 citations Philip J. Kellman et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Philip J. Kellman	2.3k	712	662	587	485	127	3.6k
James R. Pomerantz	2.9k 1.2×	622 0.9×	467 0.7×	1.4k 2.3×	759 1.6×	53	4.1k
Michael Kubovy	3.4k 1.5×	362 0.5×	699 1.1×	1.5k 2.5×	910 1.9×	92	4.8k
H. Bouma	3.6k 1.5×	1.3k 1.8×	507 0.8×	1.1k 1.8×	492 1.0×	83	5.0k
Jacob Feldman	2.0k 0.8×	780 1.1×	684 1.0×	602 1.0×	618 1.3×	101	3.7k
Janette Atkinson	5.1k 2.2×	1.4k 1.9×	286 0.4×	632 1.1×	597 1.2×	187	8.5k
Ralph Norman Haber	2.7k 1.2×	835 1.2×	514 0.8×	1.8k 3.0×	869 1.8×	107	5.1k
Mary K. Kaiser	1.2k 0.5×	476 0.7×	297 0.4×	493 0.8×	621 1.3×	84	2.8k
Fred Attneave	3.1k 1.3×	370 0.5×	1.1k 1.6×	914 1.6×	764 1.6×	31	4.9k
Mary A. Peterson	2.9k 1.3×	287 0.4×	557 0.8×	801 1.4×	596 1.2×	126	3.9k
Walter F. Bischof	2.2k 0.9×	228 0.3×	853 1.3×	453 0.8×	394 0.8×	146	3.5k

Countries citing papers authored by Philip J. Kellman

Since Specialization

Citations

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

Fields of papers citing papers by Philip J. Kellman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip J. Kellman. 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 Philip J. Kellman. The network helps show where Philip J. Kellman may publish in the future.

Co-authorship network of co-authors of Philip J. Kellman

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

All Works

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20 of 20 papers shown

Ashraf, Davin C., et al.. (2023). Training visual pattern recognition in ophthalmology using a perceptual and adaptive learning module. Canadian Journal of Ophthalmology. 59(2). e135–e141. 2 indexed citations

DiGirolamo, Gregory J., et al.. (2023). Multiple expressions of “expert” abnormality gist in novices following perceptual learning. Cognitive Research Principles and Implications. 8(1). 10–10. 1 indexed citations

Chang, Aileen Y., Victoria Williams, Oathokwa Nkomazana, et al.. (2021). Novel Education Modules Addressing the Underrepresentation of Skin of Color in Dermatology Training. Journal of Cutaneous Medicine and Surgery. 26(1). 17–24. 11 indexed citations

Krasne, Sally, Carl D. Stevens, Philip J. Kellman, & James T. Niemann. (2020). Mastering Electrocardiogram Interpretation Skills Through a Perceptual and Adaptive Learning Module. AEM Education and Training. 5(2). e10454–e10454. 10 indexed citations

Kellman, Philip J. & Sally Krasne. (2018). Accelerating expertise: Perceptual and adaptive learning technology in medical learning. Medical Teacher. 40(8). 797–802. 30 indexed citations

Baker, Nicholas, Gennady Erlikhman, Philip J. Kellman, & Hongjing Lu. (2018). Deep Convolutional Networks do not Perceive Illusory Contours.. Cognitive Science. 11 indexed citations

Massey, Christine, et al.. (2018). Perceptual Learning in Correlation Estimation: The Role of Learning Category Organization.. Cognitive Science. 2 indexed citations

Massey, Christine, et al.. (2018). Enhancing Adaptive Learning through Strategic Scheduling of Passive and Active Learning Modes.. Cognitive Science. 1 indexed citations

Lerner, Neil, et al.. (2017). Development of a Novice Driver Training Module to Accelerate Driver Perceptual Expertise. 1 indexed citations

10.

Massey, Christine, et al.. (2016). A comparison of adaptive and fixed schedules of practice.. Journal of Experimental Psychology General. 145(7). 897–917. 33 indexed citations

11.

Thai, Khanh-Phuong, Sally Krasne, & Philip J. Kellman. (2015). Adaptive Perceptual Learning in Electrocardiography: The Synergy of Passive and Active Classification.. Cognitive Science. 5 indexed citations

12.

Thai, Khanh-Phuong, et al.. (2014). Perceptual Learning in Early Mathematics: Interacting with Problem Structure Improves Mapping, Solving and Fluency.. Society for Research on Educational Effectiveness. 1 indexed citations

13.

Kellman, Philip J., et al.. (2014). The Psychophysics of Algebra Expertise: Mathematics Perceptual Learning Interventions Produce Durable Encoding Changes.. eScholarship (California Digital Library).

14.

Caplovitz, Gideon P., et al.. (2013). Neural Correlates of Spatiotemporal Boundary Formation (SBF). Journal of Vision. 13(9). 58–58. 1 indexed citations

15.

Kellman, Philip J., et al.. (2012). Modeling Spatiotemporal Boundary Formation. Journal of Vision. 12(9). 881–881. 2 indexed citations

16.

Thai, Khanh-Phuong & Philip J. Kellman. (2011). Basic Information Processing Effects from Perceptual Learning in Complex, Real-World Domains. Journal of Vision. 11(11). 1028–1028. 1 indexed citations

17.

Keane, Brian P., et al.. (2011). Automatic feature-based grouping during multiple object tracking. Journal of Vision. 11(11). 287–287. 2 indexed citations

18.

Massey, Christine, et al.. (2011). Improving Adaptive Learning Technology through the Use of Response Times. Cognitive Science. 33(33). 17 indexed citations

19.

Keane, Brian P., Hongjing Lu, & Philip J. Kellman. (2007). Classification images reveal spatiotemporal contour interpolation. Vision Research. 47(28). 3460–3475. 20 indexed citations

20.

Shipley, Thorne & Philip J. Kellman. (1988). Discontinuity theory and the perception of illusory figures. International Surgery. 58(3). 174–7. 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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