Frank Burchert

1.2k total citations
45 papers, 562 citations indexed

About

Frank Burchert is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Language and Linguistics. According to data from OpenAlex, Frank Burchert has authored 45 papers receiving a total of 562 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Cognitive Neuroscience, 31 papers in Developmental and Educational Psychology and 14 papers in Language and Linguistics. Recurrent topics in Frank Burchert's work include Neurobiology of Language and Bilingualism (36 papers), Reading and Literacy Development (21 papers) and Language Development and Disorders (16 papers). Frank Burchert is often cited by papers focused on Neurobiology of Language and Bilingualism (36 papers), Reading and Literacy Development (21 papers) and Language Development and Disorders (16 papers). Frank Burchert collaborates with scholars based in Germany, Netherlands and United States. Frank Burchert's co-authors include Ria De Bleser, Shravan Vasishth, Felix Engelmann, Bruno Nicenboim, Antje Lorenz, Irina A. Sekerina, Hauke R. Heekeren, Isabell Wartenburger, Arno Villringer and David Caplan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Trends in Cognitive Sciences and Neuropsychologia.

In The Last Decade

Frank Burchert

40 papers receiving 535 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Frank Burchert Germany 15 514 435 96 77 62 45 562
Masako Hirotani Canada 10 279 0.5× 261 0.6× 80 0.8× 66 0.9× 132 2.1× 16 411
Kristen M. Tooley United States 13 464 0.9× 367 0.8× 95 1.0× 51 0.7× 147 2.4× 17 532
Ilse Van Wijnendaele Belgium 7 422 0.8× 412 0.9× 25 0.3× 56 0.7× 122 2.0× 8 502
Benjamin Swets United States 8 445 0.9× 354 0.8× 192 2.0× 123 1.6× 203 3.3× 11 605
C. Christine Camblin United States 8 407 0.8× 272 0.6× 63 0.7× 75 1.0× 148 2.4× 10 459
Wing-Yee Chow United Kingdom 11 309 0.6× 210 0.5× 59 0.6× 71 0.9× 122 2.0× 19 375
Susan Dunlap United States 12 432 0.8× 406 0.9× 71 0.7× 53 0.7× 92 1.5× 23 526
David January United States 4 320 0.6× 224 0.5× 80 0.8× 38 0.5× 222 3.6× 4 472
Kinsey Bice United States 6 353 0.7× 317 0.7× 79 0.8× 24 0.3× 74 1.2× 8 450
Gail Mauner United States 11 333 0.6× 266 0.6× 199 2.1× 131 1.7× 158 2.5× 19 499

Countries citing papers authored by Frank Burchert

Since Specialization
Citations

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

Fields of papers citing papers by Frank Burchert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Frank Burchert

This figure shows the co-authorship network connecting the top 25 collaborators of Frank Burchert. A scholar is included among the top collaborators of Frank Burchert 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 Frank Burchert. Frank Burchert 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.
Jonkers, Roel, et al.. (2024). The cortical representation of transitivity: Insights from tractography-based inhibitory nTMS. Neuropsychologia. 201. 108940–108940.
2.
Burchert, Frank, et al.. (2023). Similarity-Based Interference in Sentence Comprehension in Aphasia: a Computational Evaluation of Two Models of Cue-Based Retrieval. Computational Brain & Behavior. 6(3). 473–502. 2 indexed citations
3.
Burchert, Frank, et al.. (2023). Mapping action naming in patients with gliomas: The influence of transitivity. SHILAP Revista de lepidopterología. 3(4). 100184–100184.
4.
Burchert, Frank, et al.. (2021). Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Direct-access Model of Retrieval. Acceda (Universidad de Las Palmas de Gran Canaria). 177–185. 1 indexed citations
5.
Vasishth, Shravan, et al.. (2021). Variability in sentence comprehension in aphasia in German. Brain and Language. 222. 105008–105008. 9 indexed citations
6.
Burchert, Frank, et al.. (2020). Derivational Morphology in Agrammatic Aphasia: A Comparison Between Prefixed and Suffixed Words. Frontiers in Psychology. 11. 1070–1070. 1 indexed citations
7.
Burchert, Frank, et al.. (2019). What matters in processing German object relative clauses in aphasia – timing or morpho-syntactic cues?. Aphasiology. 34(8). 970–998. 4 indexed citations
8.
Vasishth, Shravan, Bruno Nicenboim, Felix Engelmann, & Frank Burchert. (2019). Computational Models of Retrieval Processes in Sentence Processing. Trends in Cognitive Sciences. 23(11). 968–982. 43 indexed citations
9.
Burchert, Frank, et al.. (2014). Training-induced improvement of noncanonical sentence production does not generalize to comprehension: evidence for modality-specific processes. Cognitive Neuropsychology. 32(3-4). 195–220. 9 indexed citations
10.
Malsburg, Titus von der, et al.. (2012). Tense morphology in German agrammatism. The Mental Lexicon. 7(3). 351–380. 4 indexed citations
11.
Bleser, Ria De, et al.. (2011). Breakdown at the morphological level in agrammatism. 13(1).
12.
Burchert, Frank. (2007). Production of non-canonical sentences in agrammatic aphasia: Limits in representation or rule application. Brain and Language. 104(2). 170–179. 20 indexed citations
13.
Lorenz, Antje, et al.. (2007). Unambiguous generalization effects after treatment of non-canonical sentence production in German agrammatism. Brain and Language. 104(3). 211–229. 18 indexed citations
14.
Burchert, Frank, et al.. (2005). Tense and Agreement dissociations in German agrammatic speakers: Underspecification vs. hierarchy. Brain and Language. 94(2). 188–199. 66 indexed citations
15.
Wartenburger, Isabell, et al.. (2004). Neural correlates of syntactic transformations. Human Brain Mapping. 22(1). 72–81. 35 indexed citations
16.
Burchert, Frank, et al.. (2003). Does morphology make the difference? Agrammatic sentence comprehension in German. Brain and Language. 87(2). 323–342. 44 indexed citations
17.
Wartenburger, Isabell, Hauke R. Heekeren, Frank Burchert, Ria De Bleser, & Arno Villringer. (2003). Grammaticality judgments on sentences with and without movement of phrasal constituents—an event-related fMRI study. Journal of Neurolinguistics. 16(4-5). 301–314. 9 indexed citations
18.
Burchert, Frank, Naama Friedmann, & Ria De Bleser. (2003). Morphology does not help comprehension in agrammatism: A study of German and Hebrew. Brain and Language. 87(1). 52–52. 5 indexed citations
19.
Burchert, Frank, et al.. (2001). Does Case Make the Difference? Agrammatic Comprehension of Case-Inflected Sentences in German. Cortex. 37(5). 700–702. 6 indexed citations
20.
Druks, Judit & Frank Burchert. (2000). Agrammatismus: Ein Uberblick über Studien zu syntaktischen Verständnisstörungen. Linguistische Berichte (LB). 423–440. 1 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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