Bo-Yeong Won

757 citations
25 papers · 491 · h-index 12

Impact in

    • Neural and Behavioral Psychology Studies
    • Visual perception and processing mechanisms
    • Neural dynamics and brain function
    • EEG and Brain-Computer Interfaces
    • Decision-Making and Behavioral Economics

Papers in

Bo-Yeong Won

24 papers receiving 478 citations

Peers

Bo-Yeong Won
Comparison fields: 5 of 48
  • Cognitive Neuroscience 448
  • General Decision Sciences 27
  • Sensory Systems 38
  • Experimental and Cognitive Psychology 99
  • Human-Computer Interaction 25
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Citations per field
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Citations per year

Countries citing papers authored by Bo-Yeong Won

Since Specialization
Citations

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

Fields of papers citing papers by Bo-Yeong Won

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bo-Yeong Won. 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 Bo-Yeong Won. The network helps show where Bo-Yeong Won may publish in the future.

Co-authors

The 12 scholars most cited alongside Bo-Yeong Won, 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 Bo-Yeong Won Line = papers co-authored together Bo-Yeong Won links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201875
2 201967
3 201460
4 202048
5 201438
6 201434
7 201827
8 202027
9 201620
10 201420
11 201212
12 202111
13 201011
14 20159
15 20157
16 20206
17 20105
18 20143
19 20183
20 20232

About Bo-Yeong Won

Bo-Yeong Won is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction and Automotive Engineering, having authored 25 papers that have together received 491 indexed citations. Recurring topics across this work include Neural and Behavioral Psychology Studies (21 papers), Visual perception and processing mechanisms (17 papers), Visual Attention and Saliency Detection (5 papers), Face Recognition and Perception (4 papers), Neural dynamics and brain function (4 papers), Gaze Tracking and Assistive Technology (3 papers), Multisensory perception and integration (3 papers) and EEG and Brain-Computer Interfaces (3 papers). The work is most often cited by research in Cognitive Neuroscience (448 citations), General Decision Sciences (27 citations), Sensory Systems (38 citations), Experimental and Cognitive Psychology (99 citations) and Human-Computer Interaction (25 citations). Bo-Yeong Won has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Joy J. Geng, Yuhong Jiang, Khena M. Swallow, Nancy B. Carlisle, Andrew B. Leber, Gail Rosenbaum, Zhiheng Zhou, Won Mok Shim, Eliza Bliss‐Moreau and Jason Haberman. Their work appears in journals such as Attention Perception & Psychophysics, Journal of Experimental Psychology Human Perception & Performance, Journal of Vision, Visual Cognition and Psychonomic Bulletin & Review.

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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