Matthew Peterson

1.4k citations
27 papers · 858 indexed · h-index 13
Topics
Face Recognition and Perception (17 papers)Visual Attention and Saliency Detection (8 papers)Face recognition and analysis (7 papers)

In The Last Decade

Matthew Peterson

22 papers receiving 833 citations

Peers

Matthew Peterson
Comparison fields: 5 of 91
  • Cognitive Neuroscience 666
  • Computer Vision and Pattern Recognition 241
  • Experimental and Cognitive Psychology 197
  • Genetics 92
  • Molecular Biology 81
Replace Johan Hulleman with:
Johan Hulleman United Kingdom
M. Palomares United States
Jason M. Scimeca United States
W. Joseph MacInnes Russia
Lionel Granjon France
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Jay A. Edelman United States
Ignacio Vallines Germany
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Citations per field
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Citations per year

Countries citing papers authored by Matthew Peterson

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Peterson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Peterson

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Peterson. A scholar is included among the top collaborators of Matthew Peterson 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 Matthew Peterson. Matthew Peterson 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
#WorkIndexed citations
1
Anticipating Gaze-Based HCI Applications with the Tech Receptivity Interval: Eye Tracking as Input
1
2 18
3 20
4 57
5 0
6 62
7 45
8 1
9 0
10 97
11 1
12 16
13 2
14 228
15 41
16 9
17 1
18 1
19 58
20 6

About Matthew Peterson

Matthew Peterson is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and General Decision Sciences, having authored 27 papers that have together received 858 indexed citations. Recurring topics across this work include Face Recognition and Perception (17 papers), Visual Attention and Saliency Detection (8 papers) and Face recognition and analysis (7 papers). The work is most often cited by research in Cognitive Neuroscience (666 citations), Experimental and Cognitive Psychology (197 citations) and Sensory Systems (62 citations). Matthew Peterson has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Miguel P. Eckstein, Zhongwei Zhang, Wen Zhang, Binh T. Pham, Nancy Kanwisher, Wayne N. Frankel, Barbara Beyer, Jing Lin, Jason A. Droll and Tobias Gerstenberg. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and NeuroImage.

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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