Ya‐Ning Chang

516 total citations
28 papers, 338 citations indexed

About

Ya‐Ning Chang is a scholar working on Developmental and Educational Psychology, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Ya‐Ning Chang has authored 28 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Developmental and Educational Psychology, 20 papers in Cognitive Neuroscience and 7 papers in Artificial Intelligence. Recurrent topics in Ya‐Ning Chang's work include Reading and Literacy Development (18 papers), Neurobiology of Language and Bilingualism (18 papers) and Second Language Acquisition and Learning (5 papers). Ya‐Ning Chang is often cited by papers focused on Reading and Literacy Development (18 papers), Neurobiology of Language and Bilingualism (18 papers) and Second Language Acquisition and Learning (5 papers). Ya‐Ning Chang collaborates with scholars based in United Kingdom, Taiwan and Netherlands. Ya‐Ning Chang's co-authors include Stephen Welbourne, Padraic Monaghan, Chia‐Ying Lee, Chun-Hsien Hsu, Chia‐Ying Lee, Matthew A. Lambon Ralph, Steve Furber, Marc Brysbaert, Jie-Li Tsai and Chien‐Liang Chen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Scientific Reports and Neuropsychologia.

In The Last Decade

Ya‐Ning Chang

25 papers receiving 324 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ya‐Ning Chang United Kingdom 11 238 217 78 74 28 28 338
Say Young Kim South Korea 9 173 0.7× 183 0.8× 29 0.4× 55 0.7× 15 0.5× 20 240
Pierre Barrouillet Switzerland 8 103 0.4× 178 0.8× 40 0.5× 105 1.4× 39 1.4× 10 281
Gabriela Meade United States 13 311 1.3× 314 1.4× 30 0.4× 92 1.2× 9 0.3× 38 396
Jin Xue China 9 250 1.1× 153 0.7× 39 0.5× 67 0.9× 57 2.0× 24 349
Samantha F. McCormick United Kingdom 8 350 1.5× 290 1.3× 107 1.4× 127 1.7× 35 1.3× 13 472
Jonathan Mirault France 11 264 1.1× 251 1.2× 73 0.9× 88 1.2× 13 0.5× 42 349
Ilse Van Wijnendaele Belgium 7 412 1.7× 422 1.9× 56 0.7× 122 1.6× 10 0.4× 8 502
Mandy Ghyselinck Belgium 7 391 1.6× 408 1.9× 68 0.9× 126 1.7× 11 0.4× 7 502
Susan Dunlap United States 12 406 1.7× 432 2.0× 53 0.7× 92 1.2× 38 1.4× 23 526
Eddy Cavalli France 11 342 1.4× 236 1.1× 40 0.5× 36 0.5× 95 3.4× 22 398

Countries citing papers authored by Ya‐Ning Chang

Since Specialization
Citations

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

Fields of papers citing papers by Ya‐Ning Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ya‐Ning Chang. 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 Ya‐Ning Chang. The network helps show where Ya‐Ning Chang may publish in the future.

Co-authorship network of co-authors of Ya‐Ning Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Ya‐Ning Chang. A scholar is included among the top collaborators of Ya‐Ning Chang 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 Ya‐Ning Chang. Ya‐Ning Chang 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.
Chang, Ya‐Ning, et al.. (2025). Investigating the effects of semantic radical consistency in chinese character naming with a corpus-based measure.. Journal of Experimental Psychology Learning Memory and Cognition. 51(8). 1347–1362.
2.
Chang, Ya‐Ning, et al.. (2024). Screening for early Alzheimer’s disease: enhancing diagnosis with linguistic features and biomarkers. Frontiers in Aging Neuroscience. 16. 1451326–1451326. 3 indexed citations
3.
Chang, Ya‐Ning, Stephen Welbourne, Steve Furber, & Matthew A. Lambon Ralph. (2024). Simultaneous simulations of pure, surface and phonological acquired dyslexia within a full computational model of the primary systems hypothesis. Cortex. 179. 112–125. 3 indexed citations
4.
5.
Chang, Ya‐Ning, et al.. (2024). Modelling individual differences in reading using an optimised MikeNet simulator: the impact of reading instruction. Frontiers in Human Neuroscience. 18. 1356483–1356483. 1 indexed citations
6.
Chang, Ya‐Ning. (2023). The influence of oral vocabulary knowledge on individual differences in a computational model of reading. Scientific Reports. 13(1). 1680–1680. 1 indexed citations
7.
Chang, Ya‐Ning & Matthew A. Lambon Ralph. (2020). A unified neurocomputational bilateral model of spoken language production in healthy participants and recovery in poststroke aphasia. Proceedings of the National Academy of Sciences. 117(51). 32779–32790. 21 indexed citations
8.
Chang, Ya‐Ning & Chia‐Ying Lee. (2020). Age of acquisition effects on traditional Chinese character naming and lexical decision. Psychonomic Bulletin & Review. 27(6). 1317–1324. 23 indexed citations
9.
Monaghan, Padraic, Ya‐Ning Chang, & Stephen Welbourne. (2017). Different processes for reading words learned before and after onset of literacy. Cognitive Science. 2 indexed citations
10.
Chang, Ya‐Ning, Joanne E. Taylor, Kathleen Rastle, & Padraic Monaghan. (2017). Exploring the relations between oral language and reading instruction in a computational model of reading. Cognitive Science. 1 indexed citations
11.
Chang, Ya‐Ning & Chia‐Ying Lee. (2017). Semantic ambiguity effects on traditional Chinese character naming: A corpus-based approach. Behavior Research Methods. 50(6). 2292–2304. 9 indexed citations
12.
Chang, Ya‐Ning & Padraic Monaghan. (2016). Effects of experience in a developmental model of reading. Cognitive Science. 1 indexed citations
13.
Chang, Ya‐Ning, Stephen Welbourne, & Chia‐Ying Lee. (2016). Exploring orthographic neighborhood size effects in a computational model of Chinese character naming. Cognitive Psychology. 91. 1–23. 18 indexed citations
14.
Chen, Wei-Fan, et al.. (2016). Effects of orthographic consistency and homophone density on Chinese spoken word recognition. Brain and Language. 157-158. 51–62. 30 indexed citations
15.
Chang, Ya‐Ning, Chun-Hsien Hsu, Jie-Li Tsai, Chien‐Liang Chen, & Chia‐Ying Lee. (2015). A psycholinguistic database for traditional Chinese character naming. Behavior Research Methods. 48(1). 112–122. 45 indexed citations
16.
Lin, Chien‐Chung, Ya‐Ning Chang, Chao-Lin Liu, Chia‐Ying Lee, & Jane Yung-jen Hsu. (2014). Semantic clustering of morphologically related chinese words. National Conference on Artificial Intelligence. 4. 3116–3117. 1 indexed citations
17.
Chang, Ya‐Ning, Matthew A. Lambon Ralph, Steve Furber, & Stephen Welbourne. (2013). Modelling Graded Semantic Effects in Lexical Decision. Cognitive Science. 35(35). 5 indexed citations
18.
Chang, Ya‐Ning, Steve Furber, & Stephen Welbourne. (2012). Generating realistic semantic codes for use in neural network models. Cognitive Science. 34(34). 2 indexed citations
19.
Chang, Ya‐Ning, Steve Furber, & Stephen Welbourne. (2012). “Serial” effects in parallel models of reading. Cognitive Psychology. 64(4). 267–291. 19 indexed citations
20.
Chang, Ya‐Ning, Steve Furber, & Stephen Welbourne. (2012). Modelling normal and impaired letter recognition: Implications for understanding pure alexic reading. Neuropsychologia. 50(12). 2773–2788. 10 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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