Aktuelles
Ehrendoktor University of Tartu.
Meine aktuellen zentralen Forschungsinteressen beinhalten das Folgende.
Rechnergestützte Modellierung von lexikalischer Verarbeitung.
Finanziert durch den Europäischen Forschungsgsrat, untersuchen wir das Potential von „Wide Learning“ (Modellierung mit großen linearen Netzwerken), um Prozesse während der lexikalischen Verarbeitung beim Lesen, Hören und Sprechen zu verstehen. Kürzlich haben wir den Wirksamkeitsnachweis erbracht, dass die Verarbeitung von sowohl einfachen, als auch morphologisch komplexen Wörtern, mit hoher Genauigkeit erreicht werden kann, ohne dass es dabei theoretischer Konstrukte wie Morphemen, Stämmen, Exponenten, Inflektionsklassen oder Ausnahmen bedarf.
Unser neues Modell, das „Linear Discriminative Learning“, ist eine Formalisierung von Wort- und Paradigmamorphologie (Blevins, 2016, CUP) die auf diskriminativem Lernern beruht. Eine detaillierte Studie von englischer flektierender und abgeleiteter Morphologie wird in Baayen, Chuang, Shafaei-Bajestan and Blevins (2018, Complexity) bereitgestellt und eine kleine Fallstudie zum Lateinischen steht in Baayen, Chuang und Belvins (2018, The Mental Lexicon) zur Verfügung. Ich bin sowohl angeregt als auch verblüfft darüber, dass die einfachen linearen Mappings, die „Linear Discriminative Learning“ zugrunde liegen, so gut funktionieren.
Phonetik.
In meinem Labor verwenden wir elektromagnetische Artikulographie und Ultraschall, um zu verdeutlichen, wie Sprechende ihren Kiefer und ihre Zunge während der Artikulation verwenden. Wir untersuchten Dialekt geschuldete Unterschiede (Sprecher im Nord-Osten der Niederlande sprechen mit ihrer Zunge im weiter hinteren Teil ihres Mundes, verglichen mit Sprechern im zentralen Osten, Wieling etal. 2016, Journal of Phonetics) und wir haben kürzlich den Nachweis erhalten, dass Übung den Meister macht, auch in Sachen Artikulation (Tomaschek et al., 2018, Linguistic Vanguard). Wir modellieren auch die verschiedenen akustischen Zeitdauern von homophonen Suffixen (z.B. das Englische -s, das an Substantive angehängt den Plural, oder den Genitiv formt und an Verben angehängt die dritte Person Singular ausdrückt) unter Anwendung von „Discriminative Learning“. Statistische Methoden.
Ich hege ein langjähriges Interesse für statistische Methoden, einschließlich „Linear Mixed Effects Models“, „Random Forests“, „Generalized Additive Models“, „Quantile Regression“ und Ereigniszeitanalyse. Besonders beeindruckt bin ich von der Kombination aus „Quantile Regression“ und „Generalized additive Modeling“ als Werkzeug im „qgam package“ für R von Matteo Fasiolo (Universität Bristol). Ich liebe explorative Datenanalyse und habe von den Experimenten am meisten gelernt, die in klarem Widerspruch zu meinen Prognosen standen und unerwartete, neue Trends in meinen Daten offenbarten.
Lehre
SoSe 2025: Linguistics for Cognitive Science
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SoSe 2025: Methods II: Statistics
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SoSe 2024: Methods II: Statistics
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WiSe 2023: Linguistics for Cognitive Science
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SoSe 2023: Methods II: Statistics
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SoSe 2023: Multimodal communication
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WiSe 2022: Linguistics for Cognitive Science
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SoSe 2022: Methods II: Statistics
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WiSe 2021: Linguistics for Cognitive Science
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WiSe 2021: Methods II: Statistics
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WiSe 2020: Linguistics for Cognitive Science
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WiSe 2019: Linguistics for Cognitive Science
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SoSe 2019: Computational Models of Morphological Processing
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SoSe 2019: Introduction to Regression and Data Analysis
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WiSe 2018: Linguistics for Cognitive Science
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SoSe 2018: Introduction to Linguistics for Cognitive Science
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SoSe 2018: Advanced Regression Modeling
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SoSe 2018: Introduction to Regression and Data Analysis
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WiSe 2017: Linguistics for Cognitive Science
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WiSe 2017: Hierarchical Linear Models
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SoSe 2017: Regression modeling strategies for the analysis of linguistic and psycholinguistic data
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SoSe 2016: Introduction to Cognitive Models of Language Processing
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SoSe 2016: Regression modeling strategies for the analysis of linguistic and psycholinguistic data
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WiSe 2015: Linguistik für kognitionswissenschaften
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SoSe 2015: Introduction to Cognitive Models of Language Processing
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SoSe 2015: Regression modeling strategies for the analysis of linguistic and psycholinguistic data
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WiSe 2014: Mathematics for Linguistics
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WiSe 2014: Linguistik für kognitionswissenschaften
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SoSe 2014: Introduction to Cognitive Models of Language Processing
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SoSe 2014: Regression modeling strategies for the analysis of linguistic and psycholinguistic data
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WiSe 2013: Mathematics for Linguistics
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WiSe 2013: Linguistik für Kognitionswissenschaften
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SoSe 2013: Introduction to Cognitive Models of Language Processing
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SoSe 2013: Regression modeling strategies for the analysis of linguistic and psycholinguistic data
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WiSe 2012: Mathematics for Linguistics
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WiSe 2012: Linguistik für Kognitionswissenschaften
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SoSe 2012: Kausalität und Sprache
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SoSe 2012: Regression modeling strategies for the analysis of linguistic and psycholinguistic data
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WiSe 2011: Introduction to cognitive models of language processing
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- WiSe 2011: Linguistik für Kognitionswissenschaften
Veröffentlichungen (Auswahl)
Yang, Y., and Baayen, R. H. (2025). Comparing the semantic structures of lexicon of Mandarin and English. Language and Cognition, 17, e10, 1-30. pdf
Hassan Shahmohammadi, Maria Heitmeier, Elnaz Shafaei-Bajestan, Hendrik P. A. Lensch, R. Harald Baayen (2024). How direct is the link between words and images? The Mental Lexicon, 1-40. Pdf
Shen, T., and Baayen, R. H. (2023). Productivity and semantic transparency: An exploration of word formation in Mandarin Chinese. The Mental Lexicon, 1-22. Pdf
Baayen, R. H., Fasiolo, M., Wood, S., Chuang, Y.-Y. (2022). A note on the modeling of the effects of experimental time in psycholinguistic experiments. The Mental Lexicon, 1-35. Pdf
Shafaei-Bajestan, E., M. Moradipour-Tari, P. Uhrig, and R. H. Baayen (2021). LDL-AURIS: A computational model, grounded in error-driven learning, for the comprehension of single spoken words. Language, Cognition and Neuroscience, 1-28. Pdf
Shen, T., and R. H. Baayen. (2021). Adjective-Noun Compounds in Mandarin: a Study on Productivity. Corpus Linguistics and Linguistic Theory, 1-30. Pdf
Tomaschek, F., Tucker, B.V., Ramscar, M., and Baayen, R. H. (2021). Paradigmatic enhancement of stem vowels in regular English inflected verb forms. Morphology, 31, 171-199. Pdf
Baayen, R. H., and Smolka, E. (2020). Modeling morphological priming in German with naive discriminative learning. Frontiers in Communication, section Language Sciences, 1-40. Pdf
Chuang, Y-Y., Bell, M. J., Banke, I., and Baayen, R. H. (2020). Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning. Language Learning, 1-73. Pdf
Chuang, Y-Y., Vollmer, M-l., Shafaei-Bajestan, E., Gahl, S., Hendrix, P., and Baayen, R. H. (2020). The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning. Behavior Research Methods, 1-51. pdf
Baayen R.H., Chuang Y., Shafaei-Bajestan E., Blevins J.P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 1-39. pdf
Tomaschek, F., Plag, I., Ernestus, M., and Baayen, R. H. (2019). Phonetic effects of morphology and context: Modeling the duration of word-final S in English with naïve discriminative learning. Journal of Linguistics, 1-39. Pdf
Sering K., Milin P., Baayen R. H., (2018). Language comprehension as a multi-label classification problem. Statistica Neerlandica, 72, 339–353. pdf
Tomaschek F., Tucker B.V., Fasiolo M., and Baayen R.H. (2018). Practice makes perfect: The consequences of lexical proficiency for articulation. Linguistics Vanguard, 4, 1-13. pdf
Baayen R.H., Vasishth S., Kliegl R., Bates D. (2017). The cave of shadows. Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 94:206-234. pdf
Linke M., Bröker F., Ramscar M., Baayen R.H. (2017). Are baboons learning "orthographic" representations? Probably not. PLoS ONE 12(8): e0183876. pdf
Baayen R.H., Shaoul C., Willits J., Ramscar M. (2015). Comprehension without segmentation: A proof of concept with naive discriminative learning. Language, Cognition, and Neuroscience, 31:106–128. pdf
Ramscar M., Baayen R.H. (2014). The myth of cognitive decline: why our minds improve as we age. New Scientist, 221(2961):28–29. pdf
Kösling, K., Kunter, G., Baayen, R. H., and Plag, I. (2013). Prominence in triconstituent compounds: Pitch contours and linguistic theory. Language and Speech, 56, 529–554. Pdf
Baayen R.H., Milin P., Durdević D. F., Hendrix P., Marelli M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118:438–482. pdf
Baayen R.H. (2008). Analyzing linguistic data: A practical introduction to statics using R. Cambridge University Press. url
Baayen, R.H. (2001). Word Frequency Distributions, Kluwer Academic Publishers. pdf
Präsentationen der letzten 2 Jahre
2025
Baayen, R. H., How does language work? Challenges and opportunities in the age of deep learning, Colloquium talk University of Lübeck | Institute of Robotics and Cognitive Systems, Lübeck, Germany, March 3, 2025.
Baayen, R. H., How does language work? Challenges and opportunities in the age of deep learning, Colloquium talk Faculty of Behavioural and Movement Sciences Vrije Universiteit, Amsterdam, the Netherlands, February 7, 2025.
2024
Nikolaev, A., Baayen, R. H., and Chuang, Y.-Y., Analyzing Finnish Inflectional Classes through Discriminative Lexicon Models, Digital Research Data and Human Sciences (DRDHum) conference, Joensuu, Finland, December 11, 2024.
Baayen R. H., How can it be so simple? Predicting the FO-contours of Mandarin words in spontaneous speech from their corresponding contextualized embeddings with linear mappings, TÜling, Tartu, Estonia, December 3, 2024.
Beaman, K. V., Sering, K., and Baayen R. H., Lectal coherence in Swabian time and space: a cognitive-computational perspective, Lecture series on Lectal Coherence: Language Variation and Change across Linguistic Disciplines, München, Germany, November 15, 2024.
Baayen, R. H., and Heitmeier, M., Linear Discriminative Learning, Workshop at the International Word Processing Conference (WoProc 2024), Belgrade, Serbia, July 6, 2024.
Nikolaev, A., Chuang, Y.-Y., and Baayen, R. H., Analyzing Finnish Inflectional Classes through Discriminative Lexicon Models. International Word Processing Conference (WoProc 2024), Belgrade, Serbia, July 5, 2024.
Chuang, Y.-Y., Bell, M. J., Tseng, Y.-H., and Baayen, R. H., Word-specific tonal realizations in Mandarin. International Word Processing Conference (WoProc 2024), Belgrade, Serbia, July 5, 2024.
Baayen, R. H., and Berg, K., Historical and psycholinguistic perspectives on morphological productivity: A sketch of an integrative approach. Unraveling linguistic productivity: Insights into usage, processing and variability conference, Ghent, Belgium, May 21, 2024.
Baayen, R. H., Tseng, Y.-H., and Ernestus, M., Age and speech reduction in the Buckeye corpus: the influence of word meaning on word form. Conference on Corpora for Language and Aging Research (CLARe6), Tübingen, Germany, April 10, 2024.
Baayen, R. H., Perspectives on morphological productivity, Colloquium Center for Language and Cognition (CLCG), Groningen, Netherlands, January 22, 2024.
Baayen, R. H., Modeling Mandarin tones on two-word compounds, Colloquium English Language and Linguistics, Düsseldorf, Germany, January 19, 2024.
2023
Baayen, R. H., An introduction to the discriminative lexicon model, MEDAL Methods Workshop 'Modeling lexical processing: A practical Introduction to the JudiLing package', Tartu, Estonia, November 30, 2023.
Baayen, R. H., Writing research proposals for the European Research Council, Workshop on grant writing, Tartu, Estonia, November 29, 2023.
Baayen, R. H., Linguistic Statistics, post-graduate course University of Vienna, Vienna, Austria, November 7 - 9, 2023.
Stupak, I. V., and Baayen, R. H., Challenging Perceptions: Reevaluating Similarities in Ukrainian and Russian Noun Systems, Ukrainistik-Tagung, Dornburg, Germany, October 12, 2023 (poster presentation).
Baayen, R. H., Frequency-Informed Learning, colloquium Out of Our Minds, Birmingham, United Kingdom, October 11, 2023.
Baayen, R. H., Computational modeling of lexical processing, 2nd Joint Workshop on Chinese Lexical Semantic Change (2nd NTü), Tübingen, Germany, September 7, 2023.
Stupak, I. V., and Baayen, R. H., German affixed words: morphological productivity and semantic transparency, 16th International Cognitive Linguistics Conference (ICLC16), Düsseldorf, Germany, August 8, 2023 (poster presentation).
Chuang, Y.-Y., Baayen, R. H., and Bell, M., Do words sing their own tunes? Word-specific pitch realizations in Mandarin and English, 20th International Congress of Phonetic Sciences (ICPhS), Prague, Czech Republic, August 7, 2023 (poster presentation).
Baayen, R. H., Chuang, Y.-Y., and Heitmeier, M., Discriminative learning and the lexicon: NDL and LDL, STEP2023 – CCP Spring Training in Experimental Psycholinguistics, Edmonton, Canada, June 14, 16 (virtual).