Kevin Larson

32 papers receiving 1.2k citations

Peers

Kevin Larson
Comparison fields: 5 of 85
  • Human-Computer Interaction 479
  • Information Systems and Management 185
  • Signal Processing 289
  • Information Systems 593
  • Computer Vision and Pattern Recognition 435
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Countries citing papers authored by Kevin Larson

Since Specialization
Citations

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

Fields of papers citing papers by Kevin Larson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kevin Larson, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kevin Larson Line = papers co-authored together Kevin Larson links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1998402
2 2005196
3 1998182
4
Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs)
2005130
5 2005119
6 201028
7 201325
8 201923
9 200023
10 200723
11 200319
12
The Science of Word Recognition
200516
13 200814
14 200713
15 201712
16 201612
17 202010
18
Cognition and the Web: Moving from Theory to Web Design
20029
19 20138
20
The Aesthetics of Reading
20058

About Kevin Larson

Kevin Larson is a scholar working on Human-Computer Interaction, Experimental and Cognitive Psychology, Human Factors and Ergonomics, Cognitive Neuroscience and Computer Vision and Pattern Recognition, having authored 34 papers that have together received 1.3k indexed citations. Recurring topics across this work include Usability and User Interface Design (6 papers), Color Science and Applications (5 papers), Color perception and design (4 papers), Digital Accessibility for Disabilities (4 papers), Multisensory perception and integration (4 papers), Image and Video Quality Assessment (3 papers), Reading and Literacy Development (3 papers) and Visual perception and processing mechanisms (3 papers). The work is most often cited by research in Human-Computer Interaction (479 citations), Information Systems and Management (185 citations), Signal Processing (289 citations), Information Systems (593 citations) and Computer Vision and Pattern Recognition (435 citations). Kevin Larson has collaborated with scholars based in United States, United Kingdom and Denmark. Frequent co-authors include Mary P. Czerwinski, Kumar Chellapilla, Maarten van Dantzich, Daniel C. Robbins, George Robertson, Patrice Y. Simard, Patrice Simard, Sofie Beier, David J. Mowatt and Adam Fourney. Their work appears in journals such as Journal of the Society for Information Display, Journal of Display Technology, Proceedings of the ACM on Human-Computer Interaction, interactions and Journal of Vision.

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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