John Hale

4.5k total citations · 1 hit paper
52 papers, 2.6k citations indexed

About

John Hale is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Developmental and Educational Psychology. According to data from OpenAlex, John Hale has authored 52 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 31 papers in Cognitive Neuroscience and 16 papers in Developmental and Educational Psychology. Recurrent topics in John Hale's work include Neurobiology of Language and Bilingualism (31 papers), Natural Language Processing Techniques (30 papers) and Topic Modeling (18 papers). John Hale is often cited by papers focused on Neurobiology of Language and Bilingualism (31 papers), Natural Language Processing Techniques (30 papers) and Topic Modeling (18 papers). John Hale collaborates with scholars based in United States, United Kingdom and France. John Hale's co-authors include Jonathan Brennan, Shravan Vasishth, Marisa Ferrara Boston, Reinhold Kliegl, Eugene Charniak, Niyu Ge, Umesh Patil, Christophe Pallier, Edward P. Stabler and Chris Dyer and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

John Hale

47 papers receiving 2.3k citations

Hit Papers

A probabilistic earley parser as a psycholinguistic model 2001 2026 2009 2017 2001 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Hale United States 22 1.7k 1.4k 990 416 358 52 2.6k
Richard Futrell United States 23 785 0.5× 794 0.6× 521 0.5× 458 1.1× 449 1.3× 69 1.8k
Patrick Sturt United Kingdom 28 1.7k 1.0× 695 0.5× 1.2k 1.3× 662 1.6× 669 1.9× 90 2.4k
Matthew W. Crocker Germany 27 1.8k 1.1× 748 0.5× 1.0k 1.1× 486 1.2× 962 2.7× 105 2.5k
Yuki Kamide United Kingdom 13 2.2k 1.3× 602 0.4× 1.4k 1.4× 542 1.3× 1.3k 3.8× 20 2.8k
Kyle Mahowald United States 19 711 0.4× 622 0.5× 452 0.5× 356 0.9× 427 1.2× 45 1.7k
Michael J. Spivey-Knowlton United States 10 2.0k 1.2× 864 0.6× 1.3k 1.3× 589 1.4× 1.2k 3.4× 13 2.9k
Raymond Bertram Finland 28 1.6k 1.0× 634 0.5× 2.0k 2.0× 352 0.8× 570 1.6× 53 2.5k
Kathleen M. Eberhard United States 16 2.3k 1.4× 846 0.6× 1.7k 1.7× 821 2.0× 1.4k 3.9× 30 3.5k
Gerard Kempen Netherlands 19 1.3k 0.8× 589 0.4× 1.1k 1.1× 584 1.4× 525 1.5× 92 2.1k
Fermı́n Moscoso del Prado Martı́n United States 16 1.1k 0.6× 348 0.3× 777 0.8× 206 0.5× 542 1.5× 42 1.6k

Countries citing papers authored by John Hale

Since Specialization
Citations

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

Fields of papers citing papers by John Hale

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Hale

This figure shows the co-authorship network connecting the top 25 collaborators of John Hale. A scholar is included among the top collaborators of John Hale 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 John Hale. John Hale 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.
Wu, Shuyi, et al.. (2024). Le Petit Prince Hong Kong (LPPHK): Naturalistic fMRI and EEG data from older Cantonese speakers. Scientific Data. 11(1). 992–992. 3 indexed citations
2.
Stanojević, Miloš, et al.. (2023). Modeling Structure‐Building in the Brain With CCG Parsing and Large Language Models. Cognitive Science. 47(7). e13312–e13312. 16 indexed citations
3.
Stanojević, Miloš, et al.. (2023). Neural Correlates of Object-Extracted Relative Clause Processing Across English and Chinese. SHILAP Revista de lepidopterología. 4(3). 455–473. 2 indexed citations
4.
Li, Jixing, Wen‐Ming Luh, R. Nathan Spreng, et al.. (2022). Le Petit Prince multilingual naturalistic fMRI corpus. Scientific Data. 9(1). 530–530. 20 indexed citations
5.
Li, Jixing, et al.. (2021). Decoding the silence: Neural bases of zero pronoun resolution in Chinese. Brain and Language. 224. 105050–105050. 5 indexed citations
6.
Brennan, Jonathan, Chris Dyer, Adhiguna Kuncoro, & John Hale. (2020). Localizing syntactic predictions using recurrent neural network grammars. Neuropsychologia. 146. 107479–107479. 32 indexed citations
7.
Brennan, Jonathan & John Hale. (2019). Hierarchical structure guides rapid linguistic predictions during naturalistic listening. PLoS ONE. 14(1). e0207741–e0207741. 64 indexed citations
8.
Brennan, Jonathan, Renée Lajiness-O’Neill, Susan M. Bowyer, Ioulia Kovelman, & John Hale. (2018). Predictive sentence comprehension during story-listening in autism spectrum disorder. Language Cognition and Neuroscience. 34(4). 428–439. 13 indexed citations
9.
Luh, Wen‐Ming, Mathieu Constant, Christophe Pallier, et al.. (2018). Localising memory retrieval and syntactic composition: an fMRI study of naturalistic language comprehension. Language Cognition and Neuroscience. 34(4). 491–510. 37 indexed citations
10.
Hale, John, et al.. (2018). Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs. International Conference on Computational Linguistics. 6–17. 1 indexed citations
11.
Nelson, Matthew J., Imen El Karoui, Kristóf Giber, et al.. (2017). Neurophysiological dynamics of phrase-structure building during sentence processing. Proceedings of the National Academy of Sciences. 114(18). E3669–E3678. 187 indexed citations
12.
Li, Jixing, et al.. (2016). Temporal Lobes as Combinatory Engines for both Form and Meaning. International Conference on Computational Linguistics. 186–191. 1 indexed citations
13.
Hale, John. (2016). Information‐theoretical Complexity Metrics. Language and Linguistics Compass. 10(9). 397–412. 101 indexed citations
14.
Brennan, Jonathan, et al.. (2016). Abstract linguistic structure correlates with temporal activity during naturalistic comprehension. Brain and Language. 157-158. 81–94. 118 indexed citations
15.
Hunter, Tim B., et al.. (2014). Modeling sentence processing difficulty with a conditional probability calculator. Cognitive Science. 36(36). 3 indexed citations
16.
Whitman, John, et al.. (2010). Subject-Object Asymmetries in Korean Sentence Comprehension. eScholarship (California Digital Library). 32(32). 8 indexed citations
17.
Boston, Marisa Ferrara, John Hale, Reinhold Kliegl, Umesh Patil, & Shravan Vasishth. (2008). Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus. publish.UP (University of Potsdam). 82 indexed citations
18.
Hale, John. (2003). The Information Conveyed by Words in Sentences. Journal of Psycholinguistic Research. 32(2). 101–123. 160 indexed citations
19.
Hale, John & Paul Smolensky. (2001). A Parser for Harmonic Context-Free Grammars. eScholarship (California Digital Library). 23(23). 2 indexed citations
20.
Ge, Niyu, John Hale, & Eugene Charniak. (1998). A Statistical Approach to Anaphora Resolution. Meeting of the Association for Computational Linguistics. 148 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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