Ian M. Lyons

4.3k total citations · 1 hit paper
58 papers, 2.9k citations indexed

About

Ian M. Lyons is a scholar working on Statistics and Probability, Education and Experimental and Cognitive Psychology. According to data from OpenAlex, Ian M. Lyons has authored 58 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistics and Probability, 25 papers in Education and 20 papers in Experimental and Cognitive Psychology. Recurrent topics in Ian M. Lyons's work include Cognitive and developmental aspects of mathematical skills (37 papers), Mathematics Education and Teaching Techniques (22 papers) and Spatial Cognition and Navigation (10 papers). Ian M. Lyons is often cited by papers focused on Cognitive and developmental aspects of mathematical skills (37 papers), Mathematics Education and Teaching Techniques (22 papers) and Spatial Cognition and Navigation (10 papers). Ian M. Lyons collaborates with scholars based in United States, Canada and United Kingdom. Ian M. Lyons's co-authors include Sian L. Beilock, Daniel Ansari, Anniek Vaessen, Leo Blomert, Gavin R. Price, Andrew Mattarella-Micke, Steven L. Small, Howard C. Nusbaum, H. Moriah Sokolowski and Richard J. Daker and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Ian M. Lyons

54 papers receiving 2.8k citations

Hit Papers

Numerical predictors of arithmetic success in grades 1–6 2014 2026 2018 2022 2014 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ian M. Lyons United States 28 1.9k 1.5k 1.1k 892 755 58 2.9k
Evelyn H. Kroesbergen Netherlands 35 2.4k 1.3× 2.1k 1.4× 2.0k 1.9× 1.1k 1.3× 756 1.0× 105 4.1k
Ann Dowker United Kingdom 27 2.0k 1.1× 1.9k 1.3× 1.2k 1.1× 1.2k 1.3× 427 0.6× 80 3.4k
Johannes E. H. Van Luit Netherlands 34 2.1k 1.1× 1.9k 1.2× 1.7k 1.6× 611 0.7× 455 0.6× 76 3.3k
Mary K. Hoard United States 27 3.5k 1.8× 2.6k 1.7× 2.5k 2.4× 694 0.8× 476 0.6× 40 4.3k
Orly Rubinsten Israel 23 1.5k 0.8× 988 0.7× 993 0.9× 443 0.5× 647 0.9× 55 2.1k
Melissa E. Libertus United States 30 2.7k 1.5× 2.2k 1.5× 1.5k 1.4× 289 0.3× 859 1.1× 100 3.5k
Marina Vasilyeva United States 24 730 0.4× 1.1k 0.8× 1.6k 1.5× 434 0.5× 431 0.6× 60 2.8k
Marie‐Pascale Noël Belgium 33 3.7k 2.0× 2.6k 1.7× 2.5k 2.3× 410 0.5× 1.0k 1.4× 107 4.3k
Lara Nugent United States 21 1.9k 1.0× 1.5k 1.0× 1.5k 1.4× 488 0.5× 724 1.0× 33 2.8k
Daniela Lucangeli Italy 27 1.7k 0.9× 1.5k 1.0× 1.4k 1.3× 229 0.3× 410 0.5× 77 2.4k

Countries citing papers authored by Ian M. Lyons

Since Specialization
Citations

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

Fields of papers citing papers by Ian M. Lyons

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian M. Lyons. 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 Ian M. Lyons. The network helps show where Ian M. Lyons may publish in the future.

Co-authorship network of co-authors of Ian M. Lyons

This figure shows the co-authorship network connecting the top 25 collaborators of Ian M. Lyons. A scholar is included among the top collaborators of Ian M. Lyons 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 Ian M. Lyons. Ian M. Lyons 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.
Sokolowski, H. Moriah, Richard J. Daker, Ian M. Lyons, et al.. (2025). Visual imagery and STEM occupational attainment: Gender matters. Personality and Individual Differences. 250. 113552–113552.
2.
Evans, Tanya M., et al.. (2025). Math anxiety and arithmetic learning: Evidence for impaired procedural learning and enhanced retrieval learning.. Journal of Experimental Psychology Learning Memory and Cognition. 51(12). 1909–1925. 1 indexed citations
3.
Johnson, Anna D., et al.. (2024). Public Preschool Predicts Stronger Third-Grade Academic Skills. AERA Open. 10. 2 indexed citations
4.
Daker, Richard J., H. Moriah Sokolowski, Adam E. Green, et al.. (2024). Examining the Interplay between the Cognitive and Emotional Aspects of Gender Differences in Spatial Processing. Journal of Intelligence. 12(3). 30–30. 1 indexed citations
5.
Daker, Richard J., et al.. (2023). Does anxiety explain why math-anxious people underperform in math?. npj Science of Learning. 8(1). 6–6. 8 indexed citations
6.
Daker, Richard J., et al.. (2023). Evidence for avoidance tendencies linked to anxiety about specific types of thinking. Scientific Reports. 13(1). 3294–3294. 4 indexed citations
7.
Cortes, Robert A., David J. M. Kraemer, Robert A Kolvoord, et al.. (2022). Transfer from spatial education to verbal reasoning and prediction of transfer from learning-related neural change. Science Advances. 8(32). eabo3555–eabo3555. 13 indexed citations
9.
Lyons, Ian M., et al.. (2018). More Similar Than Different: Gender Differences in Children's Basic Numerical Skills Are the Exception Not the Rule. Child Development. 90(1). e66–e79. 63 indexed citations
10.
Daker, Richard J. & Ian M. Lyons. (2018). Numerical and Non-numerical Predictors of First Graders’ Number-Line Estimation Ability. Frontiers in Psychology. 9. 2336–2336. 4 indexed citations
11.
Sasanguie, Delphine, Ian M. Lyons, Bert De Smedt, & Bert Reynvoet. (2017). Unpacking symbolic number comparison and its relation with arithmetic in adults. Cognition. 165. 26–38. 52 indexed citations
12.
Lyons, Ian M., Stephan Vogel, & Daniel Ansari. (2016). On the ordinality of numbers. Progress in brain research. 227. 187–221. 65 indexed citations
13.
Necka, Elizabeth A., H. Moriah Sokolowski, & Ian M. Lyons. (2015). The role of self-math overlap in understanding math anxiety and the relation between math anxiety and performance. Frontiers in Psychology. 6. 1543–1543. 26 indexed citations
14.
Lyons, Ian M. & Sian L. Beilock. (2013). Ordinality and the Nature of Symbolic Numbers. Journal of Neuroscience. 33(43). 17052–17061. 84 indexed citations
15.
Lyons, Ian M., Daniel Ansari, & Sian L. Beilock. (2012). Symbolic estrangement: Evidence against a strong association between numerical symbols and the quantities they represent.. Journal of Experimental Psychology General. 141(4). 635–641. 138 indexed citations
16.
Lyons, Ian M.. (2011). Biomedical science : lecture notes. Wiley-Blackwell eBooks.
17.
Lyons, Ian M., et al.. (2011). Evidence against a strong association between numerical symbols and the quantities they represent. Cognitive Science. 33(33).
18.
Lyons, Ian M. & Sian L. Beilock. (2011). Mathematics Anxiety: Separating the Math from the Anxiety. Cerebral Cortex. 22(9). 2102–2110. 167 indexed citations
19.
Lyons, Ian M. & Sian L. Beilock. (2009). Beyond quantity: Individual differences in working memory and the ordinal understanding of numerical symbols. Cognition. 113(2). 189–204. 54 indexed citations
20.
Lyons, Ian M., Andrew Mattarella-Micke, Matthew Cieslak, et al.. (2009). The role of personal experience in the neural processing of action-related language. Brain and Language. 112(3). 214–222. 61 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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