Bo-Yeong Won

757 total citations
25 papers, 491 citations indexed

About

Bo-Yeong Won is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology. According to data from OpenAlex, Bo-Yeong Won has authored 25 papers receiving a total of 491 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cognitive Neuroscience, 5 papers in Computer Vision and Pattern Recognition and 5 papers in Experimental and Cognitive Psychology. Recurrent topics in Bo-Yeong Won's work include Neural and Behavioral Psychology Studies (21 papers), Visual perception and processing mechanisms (17 papers) and Visual Attention and Saliency Detection (5 papers). Bo-Yeong Won is often cited by papers focused on Neural and Behavioral Psychology Studies (21 papers), Visual perception and processing mechanisms (17 papers) and Visual Attention and Saliency Detection (5 papers). Bo-Yeong Won collaborates with scholars based in United States, South Korea and Canada. Bo-Yeong Won's 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 and has published in prestigious journals such as Journal of Experimental Psychology Human Perception & Performance, Journal of Experimental Psychology General and Vision Research.

In The Last Decade

Bo-Yeong Won

24 papers receiving 478 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Bo-Yeong Won United States 12 448 99 72 38 36 25 491
Jason A. Droll United States 9 395 0.9× 52 0.5× 119 1.7× 31 0.8× 56 1.6× 16 457
Jason T. Arita United States 6 573 1.3× 119 1.2× 44 0.6× 23 0.6× 65 1.8× 7 590
Jason Rajsic Canada 13 365 0.8× 77 0.8× 55 0.8× 7 0.2× 58 1.6× 35 413
Sage Boettcher United Kingdom 13 424 0.9× 76 0.8× 176 2.4× 27 0.7× 42 1.2× 36 530
Sabine Born Switzerland 12 337 0.8× 65 0.7× 33 0.5× 26 0.7× 21 0.6× 32 366
Shui-I Shih United Kingdom 8 291 0.6× 87 0.9× 48 0.7× 20 0.5× 37 1.0× 12 319
Dragan Rangelov Australia 11 325 0.7× 60 0.6× 70 1.0× 23 0.6× 43 1.2× 27 362
Jiye Shen Canada 9 349 0.8× 79 0.8× 133 1.8× 22 0.6× 53 1.5× 10 420
Anna Kosovicheva United States 12 340 0.8× 80 0.8× 42 0.6× 17 0.4× 79 2.2× 36 439
Andrew Found United Kingdom 8 385 0.9× 95 1.0× 83 1.2× 13 0.3× 69 1.9× 14 420

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-authorship network of co-authors of Bo-Yeong Won

This figure shows the co-authorship network connecting the top 25 collaborators of Bo-Yeong Won. A scholar is included among the top collaborators of Bo-Yeong Won 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 Bo-Yeong Won. Bo-Yeong Won 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.
Won, Bo-Yeong, et al.. (2025). Search strategy modulates memory-driven capture. Attention Perception & Psychophysics. 88(2). 63–63.
2.
Won, Bo-Yeong, et al.. (2023). Familiarity enhances mnemonic precision but impairs mnemonic accuracy in visual working memory. Psychonomic Bulletin & Review. 30(4). 1452–1462. 2 indexed citations
3.
Won, Bo-Yeong, et al.. (2021). Memory precision for salient distractors decreases with learned suppression. Psychonomic Bulletin & Review. 29(1). 169–181. 11 indexed citations
4.
Won, Bo-Yeong, Jason Haberman, Eliza Bliss‐Moreau, & Joy J. Geng. (2020). Flexible target templates improve visual search accuracy for faces depicting emotion. Attention Perception & Psychophysics. 82(6). 2909–2923. 6 indexed citations
5.
Won, Bo-Yeong & Joy J. Geng. (2020). Passive exposure attenuates distraction during visual search.. Journal of Experimental Psychology General. 149(10). 1987–1995. 48 indexed citations
6.
Won, Bo-Yeong, et al.. (2020). Changes in visual cortical processing attenuate singleton distraction during visual search. Cortex. 132. 309–321. 27 indexed citations
7.
Won, Bo-Yeong, et al.. (2018). Evidence for second-order singleton suppression based on probabilistic expectations.. Journal of Experimental Psychology Human Perception & Performance. 45(1). 125–138. 75 indexed citations
8.
Won, Bo-Yeong & Joy J. Geng. (2018). Learned suppression for multiple distractors in visual search.. Journal of Experimental Psychology Human Perception & Performance. 44(7). 1128–1141. 27 indexed citations
9.
Won, Bo-Yeong & Joy J. Geng. (2018). The role of probabilistic expectations on the suppression of salient distractor. Journal of Vision. 18(10). 455–455. 1 indexed citations
10.
Won, Bo-Yeong & Andrew B. Leber. (2018). Failure to exploit learned spatial value information during visual search. Visual Cognition. 26(7). 482–499. 3 indexed citations
11.
Won, Bo-Yeong & Andrew B. Leber. (2016). How do magnitude and frequency of monetary reward guide visual search?. Attention Perception & Psychophysics. 78(5). 1221–1231. 20 indexed citations
12.
Leber, Andrew B. & Bo-Yeong Won. (2016). Spatial reward guides choice, not visual search. Journal of Vision. 16(12). 1139–1139. 1 indexed citations
13.
Won, Bo-Yeong, et al.. (2015). Statistical learning modulates the direction of the first head movement in a large-scale search task. Attention Perception & Psychophysics. 77(7). 2229–2239. 9 indexed citations
14.
Jiang, Yuhong & Bo-Yeong Won. (2015). Spatial scale, rather than nature of task or locomotion, modulates the spatial reference frame of attention.. Journal of Experimental Psychology Human Perception & Performance. 41(3). 866–878. 7 indexed citations
15.
Won, Bo-Yeong, Khena M. Swallow, & Yi Jiang. (2014). First saccadic eye movement reveals persistent attentional guidance by implicit learning. Journal of Vision. 14(10). 1198–1198. 3 indexed citations
16.
Won, Bo-Yeong & Yuhong Jiang. (2014). Spatial working memory interferes with explicit, but not probabilistic cuing of spatial attention.. Journal of Experimental Psychology Learning Memory and Cognition. 41(3). 787–806. 34 indexed citations
17.
Jiang, Yuhong, et al.. (2014). Task specificity of attention training: the case of probability cuing. Attention Perception & Psychophysics. 77(1). 50–66. 38 indexed citations
18.
Jiang, Yuhong, Bo-Yeong Won, & Khena M. Swallow. (2014). First saccadic eye movement reveals persistent attentional guidance by implicit learning.. Journal of Experimental Psychology Human Perception & Performance. 40(3). 1161–1173. 60 indexed citations
19.
Jiang, Yuhong, et al.. (2014). Spatial reference frame of attention in a large outdoor environment.. Journal of Experimental Psychology Human Perception & Performance. 40(4). 1346–1357. 20 indexed citations
20.
Won, Bo-Yeong & Yuhong Jiang. (2012). Redundancy effects in the processing of emotional faces. Vision Research. 78. 6–13. 12 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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2026