Sonderforschungsbereich 1287 - Die Grenzen der Variabilität in der Sprache: Kognitive, komputationale und grammatische Aspekte

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Wissenschaftliches Service- und Infrastrukturprojekt

PI(s): Prof. Dr. Shravan Vasishth & Prof. Dr. Ralf Engbert

Dieses Projekt unterstützt den SFB bei der Beratung der Teilprojekte zu experimentellem Design und statistischer Analyse, der Präregistrierung der primären statistischen Analysen mittels der Open Science Foundation und dem Datenmanagement. Diese Ziele werden erreicht durch (1) die Optimierung von experimentellen Designs und statistischen Analysen, (2) die statistische Ausbildung innerhalb des SFBs, (3) die Unterstützung bei der Entwicklung und Pflege von Datenrepositorien inkl. der Präregistrierung und Veröffentlichung von Daten und Code sowie (4) die Entwicklung neuer statistischer Methoden, komputationaler Werkzeuge und Softwarepakete, die auch der Allgemeinheit zur Verfügung gestellt werden.

MitarbeiterInnen

Engbert

Prof. Dr. Ralf Engbert

Universität PotsdamCampus GolmDepartment PsychologieKarl-Liebknecht-Strasse 24-25, Haus 14, Raum 4.0314476 Potsdam
(+49) 331 977-2140E-MailLink
Laurinavichyute

Dr. Anna Laurinavichyute

Universität PotsdamCampus GolmDepartment LinguistikKarl-Liebknecht-Strasse 24-25, Haus 14, Raum 3.3614476 Potsdam
Vasishth

Prof. Dr. Shravan Vasishth

Universität PotsdamCampus GolmDepartment LinguistikKarl-Liebknecht-Strasse 24-25, Haus 14, Raum 2.3414476 Potsdam
(+49) 331 977-2457E-MailLink
Ziegert

David Ziegert

Universität PotsdamCampus GolmDepartment LinguistikKarl-Liebknecht-Strasse 24-25, Haus 14, Raum 3.3414476 Potsdam
(+49) 331 977-231118E-Mail

Publikationen

Author(s)TitleYearPublished inDOILinksTypeProject
Rabe, M. M., Vasishth, S., Hohenstein, S., Kliegl, R. & Schad, D. J.hypr: An R package for hypothesis-driven contrast coding.2020The Journal of Open Source Software, 5(48), 2134.LinkPaper CodePeer-ReviewedB03, Q
Engelmann, F., Jäger, L. A. & Vasishth, S.The effect of prominence and cue association in retrieval processes: A computational account.2019Cognitive Science, 43: e12800.LinkPaper Data CodePeer-ReviewedB03, Q
Vasishth, S., Mertzen, D., Jäger, L.A. & Gelman, A.The statistical significance filter leads to overoptimistic expectations of replicability.2018Journal of Memory and Language, 103, 151-175.LinkPaper Data CodePeer-ReviewedB03, Q
Jäger, L. A., Mertzen, D., Van Dyke, J. A. & Vasishth, S.Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study.2020Journal of Memory and Language, 111, 104063.LinkPaper Data CodePeer-ReviewedB03, Q
Vasishth, S.Using Approximate Bayesian Computation for estimating parameters in the cue-based retrieval model of sentence processing.2020MethodsX, 7: 100850.LinkPaper Data CodePeer-ReviewedB03, Q
Bürki, A., Elbuy, S., Madec, S. & Vasishth, S.What did we learn from forty years of research on semantic interference? A Bayesian meta-analysis.2020Journal of Memory and Language, 114: 104125.LinkPaper Data CodePeer-ReviewedB05, Q
Schad, D. J., Vasishth, S., Hohenstein, S. & Kliegl, R.How to capitalize on a priori contrasts in linear (mixed) models: A tutorial.2020Journal of Memory and Language, 110: 104038.LinkPaper CodePeer-ReviewedC01, Q
Schad, D.J., Betancourt, M. & Vasishth, S.Toward a principled Bayesian workflow: A tutorial for cognitive science.2021Psychological Methods, 26(1), 103-126.LinkPaper Data CodePeer-ReviewedQ
Nicenboim, B.,  Roettger, T.B. & Vasishth, S.Using meta-analysis for evidence synthesis: The case of incomplete neutralization in German.2018Journal of Phonetics, 70 (Special Issue: Emerging Data Analysis in Phonetic Sciences), 39-55.LinkPaper Data CodePeer-ReviewedQ
Schad, D.J., Betancourt, M. & Vasishth, S.Toward a principled Bayesian workflow: A tutorial for cognitive science.2019Data CodeOtherQ
Schad, D. J. & Vasishth, S. The posterior probability of a null hypothesis given a statistically significant research result.2019arXiv preprint.Paper OtherQ
Schad, D. J., Hohenstein, S., Vasishth, S. & Kliegl, R.How to capitalize on a priori contrasts in linear (mixed) models: A tutorial.2018arXiv preprint.Paper OtherQ
Nicenboim, B. & Vasishth, S. Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling. 2018Journal of Memory and Language, 99(April 2018), 1-34.LinkSFB-RelatedQ
Nicenboim, B., Vasishth, S., Engelmann, F. & Suckow, K.Exploratory and confirmatory analyses in sentence processing: A case study of number interference in German. 2018Cognitive Science, 42(Suppl. 4), 1075-1100. LinkSFB-RelatedQ
Vasishth, S., Nicenboim, B., Beckman, M.E., Fangfang, L. & Kong, J.E.Bayesian data analysis in the phonetic sciences: A tutorial introduction.2018Journal of Phonetics, 71 (Special Issue: Emerging Data Analysis in Phonetic Sciences), 147-161.LinkSFB-RelatedQ
Bürki, A., Elbuy, S., Madec, S. & Vasishth, S.What did we learn from forty years of research on semantic interference? A Bayesian meta-analysis.2020Poster presented at the 26th Architectures and Mechanisms of Language Processing (AMLaP 2020), University of Potsdam, Potsdam, Germany. 03 - 05 September.Talk or PresentationB05, Q
Schad, D., Betancourt, M. & Vasishth, S.Toward a principled Bayesian workflow in cognitive science.2019Paper presented at the 14. Tagung der Fachgruppe Methoden & Evaluation der Deutschen Gesellschaft für Psychologie (FGME 2019), Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik (IPN), Kiel, Germany. 15 - 18 September.Talk or PresentationQ
Vasishth, S.The role of replication in Bayesian data analysis.2019Invited talk at the Lecture Series Psychological Science, Winter-Term 2019/2020, University of Hamburg, Hamburg, Germany. 18 December.Paper Talk or PresentationQ
Vasishth, S.Prenominal relatives clauses in Mandarin: Implications for theories of sentence processing.2019Invited talk at the GK Colloquium, Institut für Linguistik, Goethe-Universität Frankfurt, Frankfurt, Germany. 14 MayTalk or PresentationB02, B03, Q
Vasishth, S.Is pre-registration a bad idea or a very bad idea?2019Invited talk at the Humboldt-Universität zu Berlin, Berlin, Germany. 24 January.Talk or PresentationB02, B03, Q
Vasishth, S.Bayesian vs. frequentist data analysis: A comparison.2019Invited talk at the Block Seminar, SFB 1340 ''Matrix in Vision'', Berlin, Germany. 18 JanuarTalk or PresentationQ
Laurinavichyute, A. & von der Malsburg, T.Semantic Attraction in Sentence Comprehension.2022Cognitive Science, 46(2), e13086.LinkPaper Peer-ReviewedQ
Schad, D. J., Nicenboim, B., Bürkner, P.-C., Betancourt, M. & Vasishth, S.Workflow techniques for the robust use of bayes factors.2022Psychological Methods. Advance Online Publication.LinkPeer-ReviewedQ
Vasishth, S., Yadav, H., Schad, D. J. & Nicenboim, B.Sample Size Determination for Bayesian Hierarchical Models Commonly Used in Psycholinguistics.2022Computational Brain & Behavior.LinkPaper Peer-ReviewedQ
Vasishth, S. & Engelmann, F.Sentence Comprehension as a Cognitive Process: A Computational Approach.2021Cambridge: Cambridge University Press.SFB-RelatedB03, Q
Laurinavichyute, A., Yadav, H. & Vasishth, S.Share the code, not just the data: A case study of the reproducibility of articles published in the Journal of Memory and Language under the open data policy.2022Journal of Memory and Language, 125, 104332.LinkPeer-ReviewedQ
Ciaccio, L. A., & Veríssimo, J.Investigating variability in morphological processing with Bayesian distributional models.2022Psychonomic Bulletin & Review.LinkPaper Peer-ReviewedB04, Q