Ming Yan

2.1k citations
72 papers · 1.4k · h-index 22

Impact in

Papers in

Ming Yan

67 papers receiving 1.4k citations

Peers

Ming Yan
Comparison fields: 5 of 60
  • Developmental and Educational Psychology 1.1k
  • Cognitive Neuroscience 951
  • Experimental and Cognitive Psychology 485
  • Human-Computer Interaction 214
  • Statistics and Probability 75
Replace Bernhard Angele with:
Bernhard Angele United States
Hazel I. Blythe United Kingdom
Chuanli Zang China
Jinmian Yang United States
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Alexander Pollatsek United States
Wayne S. Murray United Kingdom
Gretchen Kambe United States
Victoria A. McGowan United Kingdom
Katherine J. Midgley United States
Ming Yan relative to Bernhard Angele United States Bernhard Angele's profile →
Citations per field
00.5×1.6×
Bernhard Angele · 1×
Citations per year

Countries citing papers authored by Ming Yan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ming Yan, 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 Ming Yan Line = papers co-authored together Ming Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009170
2 2009154
3 201276
4 201267
5 201361
6 201457
7 201453
8 201038
9 201236
10 200929
11 201829
12 201629
13 201628
14 201428
15 201526
16 202125
17 202025
18 201024
19 201624
20 201322

About Ming Yan

Ming Yan is a scholar working on Developmental and Educational Psychology, Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction and Artificial Intelligence, having authored 72 papers that have together received 1.4k indexed citations. Recurring topics across this work include Reading and Literacy Development (52 papers), Neurobiology of Language and Bilingualism (35 papers), Multisensory perception and integration (16 papers), Gaze Tracking and Assistive Technology (11 papers), Neural and Behavioral Psychology Studies (10 papers), Visual perception and processing mechanisms (10 papers), Language, Metaphor, and Cognition (9 papers) and Tactile and Sensory Interactions (8 papers). The work is most often cited by research in Developmental and Educational Psychology (1.1k citations), Cognitive Neuroscience (951 citations), Experimental and Cognitive Psychology (485 citations), Human-Computer Interaction (214 citations) and Statistics and Probability (75 citations). Ming Yan has collaborated with scholars based in China, Germany and Macao. Frequent co-authors include Reinhold Kliegl, Hua Shu, Jinger Pan, Eike M. Richter, Wei Zhou, Jochen Laubrock, Antje Nuthmann, Aiping Wang, Jie-Li Tsai and Scott A. McDonald. Their work appears in journals such as Journal of Experimental Psychology Learning Memory and Cognition, Reading and Writing, Scientific Studies of Reading, Psychonomic Bulletin & Review and Behavior Research Methods.

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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