Jay G. Rueckl

2.9k total citations
64 papers, 1.9k citations indexed

About

Jay G. Rueckl is a scholar working on Developmental and Educational Psychology, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Jay G. Rueckl has authored 64 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Developmental and Educational Psychology, 37 papers in Cognitive Neuroscience and 16 papers in Artificial Intelligence. Recurrent topics in Jay G. Rueckl's work include Reading and Literacy Development (50 papers), Neurobiology of Language and Bilingualism (31 papers) and Language Development and Disorders (14 papers). Jay G. Rueckl is often cited by papers focused on Reading and Literacy Development (50 papers), Neurobiology of Language and Bilingualism (31 papers) and Language Development and Disorders (14 papers). Jay G. Rueckl collaborates with scholars based in United States, Israel and Canada. Jay G. Rueckl's co-authors include Michal Raveh, Stephen M. Kosslyn, Kyle R. Cave, Kenneth R. Pugh, Stephen J. Frost, W. Einar Mencl, Oliver Sawi, Leonard Katz, Rebecca Sandak and J.L. Eberhardt and has published in prestigious journals such as Proceedings of the National Academy of Sciences, NeuroImage and Child Development.

In The Last Decade

Jay G. Rueckl

62 papers receiving 1.9k citations

Peers

Jay G. Rueckl

DEP

ECP

Ronald Peereman France

Donald J. Bolger United States

Cristina Burani Italy

Sébastien Pacton France

Luca L. Bonatti Spain

George Houghton United Kingdom

Don L. Scarborough United States

Noam Siegelman United States

Philippe Mousty Belgium

Alain Content Belgium

Ronald Peereman France

Jay G. Rueckl

1.2k ×0.9

DEP

1.0k ×0.8

437 ×1.1

ECP

122 ×0.4

193 ×0.8

Citations per year, relative to Jay G. Rueckl Jay G. Rueckl (= 1×) peers Ronald Peereman

Countries citing papers authored by Jay G. Rueckl

Since Specialization

Citations

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

Fields of papers citing papers by Jay G. Rueckl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay G. Rueckl. 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 Jay G. Rueckl. The network helps show where Jay G. Rueckl may publish in the future.

Co-authorship network of co-authors of Jay G. Rueckl

This figure shows the co-authorship network connecting the top 25 collaborators of Jay G. Rueckl. A scholar is included among the top collaborators of Jay G. Rueckl 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 Jay G. Rueckl. Jay G. Rueckl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Edwards, Ashley A., et al.. (2025). Is the Role of Set for Variability in Word Reading Influenced by Conditions Leading to Partial Decoding?. Scientific Studies of Reading. 29(5). 455–469.

Bertram, Raymond, Tuomo Häikiö, Minna Lehtonen, et al.. (2025). Gender and home language effects on vocabulary skills among school children aged 9–15 in Finland. Scientific Reports. 15(1). 44832–44832.

Siegelman, Noam, Jay G. Rueckl, Jason Chor Ming Lo, et al.. (2022). Quantifying the regularities between orthography and semantics and their impact on group- and individual-level behavior.. Journal of Experimental Psychology Learning Memory and Cognition. 48(6). 839–855. 9 indexed citations

Siegelman, Noam, et al.. (2021). Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models. NeuroImage. 242. 118476–118476. 2 indexed citations

Edwards, Ashley A., et al.. (2021). Unpacking the unique relationship between set for variability and word reading development: Examining word- and child-level predictors of performance.. Journal of Educational Psychology. 114(6). 1242–1256. 15 indexed citations

Frost, Stephen J., Atira Bick, Peter J. Molfese, et al.. (2021). Tracking second language immersion across time: Evidence from a bi-directional longitudinal cross-linguistic fMRI study. Neuropsychologia. 154. 107796–107796. 8 indexed citations

Steacy, Laura M., et al.. (2020). The Effect of Facilitative Versus Inhibitory Word Training Corpora on Word Reading Accuracy Growth in Children With Dyslexia. Learning Disability Quarterly. 44(3). 158–169. 2 indexed citations

Malins, Jeffrey G., Nicole Landi, Jan C. Frijters, et al.. (2020). Is that a pibu or a pibo? Children with reading and language deficits show difficulties in learning and overnight consolidation of phonologically similar pseudowords. Developmental Science. 24(2). 7 indexed citations

Edwards, Ashley, Laura M. Steacy, Noam Siegelman, et al.. (2020). Unpacking the Unique Relationship Between Set for Variability and Word Reading Development: Examining Word- and Child-Level Predictors of Performance. PsyArXiv (OSF Preprints). 1 indexed citations

10.

Siegelman, Noam, Devin M. Kearns, & Jay G. Rueckl. (2020). Using information-theoretic measures to characterize the structure of the writing system: the case of orthographic-phonological regularities in English. Behavior Research Methods. 52(3). 1292–1312. 26 indexed citations

11.

Magnuson, James S., Jay G. Rueckl, Paul D. Allopenna, et al.. (2019). EARSHOT: A minimal network model of human speech recognition that operates on real speech.. Cognitive Science. 2248–2253. 1 indexed citations

12.

Malins, Jeffrey G., Peter J. Molfese, Jay G. Rueckl, et al.. (2016). Dough, tough, cough, rough: A “fast” fMRI localizer of component processes in reading. Neuropsychologia. 91. 394–406. 22 indexed citations

13.

Zhao, Jingjing, Xiaoyi Wang, Stephen J. Frost, et al.. (2014). Neural division of labor in reading is constrained by culture: A training study of reading Chinese characters. Cortex. 53. 90–106. 22 indexed citations

14.

Diehl, Joshua J., Stephen J. Frost, Gordon F. Sherman, et al.. (2014). Neural correlates of language and non-language visuospatial processing in adolescents with reading disability. NeuroImage. 101. 653–666. 29 indexed citations

15.

Rueckl, Jay G., et al.. (2010). On the interaction of letter transpositions and morphemic boundaries. Language and Cognitive Processes. 26(4-6). 482–508. 36 indexed citations

16.

Rueckl, Jay G., et al.. (2008). Are CORNER and BROTHER morphologically complex? Not in the long term. Language and Cognitive Processes. 23(7-8). 972–1001. 41 indexed citations

17.

Katz, Leonard, Chang H. Lee, Whitney Tabor, et al.. (2005). Behavioral and neurobiological effects of printed word repetition in lexical decision and naming. Neuropsychologia. 43(14). 2068–2083. 30 indexed citations

18.

Frost, Stephen J., Rebecca Sandak, Jay G. Rueckl, et al.. (2005). A functional magnetic resonance imaging study of the tradeoff between semantics and phonology in reading aloud. Neuroreport. 16(6). 621–624. 48 indexed citations

19.

Feldman, Laurie Beth, et al.. (2002). Morphological analysis by child readers as revealed by the fragment completion task. Psychonomic Bulletin & Review. 9(3). 529–535. 39 indexed citations

20.

Rueckl, Jay G. & Gregg C. Oden. (1986). The integration of contextual and featural information during word identification. Journal of Memory and Language. 25(4). 445–460. 21 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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