Dr. Fritz Günther

University of Tübingen
Department of Psychology
Schleichstraße 4
72076 Tübingen

email: fritz.guentherspam prevention@uni-tuebingen.de


personal website

CV and publications


  • Günther, F., Press, S. A., Dudschig, C., & Kaup, B. (in press). The limits of automatic sensorimotor processing during word processing: Investigations with repeated linguistic experience, memory consolidation during sleep, and rich linguistic learning contexts. Psychological Research.
  • Günther, F., & Marelli, M. (2021). CAOSS and transcendence: Modeling role-dependent constituent meanings in compounds. Morphology, Advance online publication.
  • Capuano, F., Dudschig, C., Günther, F., & Kaup, B. (2021). Semantic Similarity of Alternatives fostered by Conversational Negation. Cognitive Science, 45, e13015.
  • Petilli, M. A., Günther, F., Vergallito, A., Ciapparelli, M., & Marelli, M. (2021). Data-driven computational models reveal perceptual simulation in word processing. Journal of Memory and Language, 117, 104194.
  • Günther, F., Petilli, M. A., Vergallito, A., & Marelli, M. (2020). Images of the unseen: Extrapolating visual representations for abstract and concrete words in a data-driven computational model. Psychological Research, Advance online publication.
  • Amenta, S., Günther, F., & Marelli, M. (2020). A (distributional) semantic perspective on the processing of morphologically complex words. The Mental Lexicon, 15, 62-78.
  • Günther, F., Nguyen, T., Chen, L., Dudschig, C., Kaup, B., & Glenberg, A. M. (2020). Immediate sensorimotor grounding of novel concepts learned from language alone. Journal of Memory and Language, 115, 104172.
  • Günther, F., Petilli, M. A., & Marelli, M. (2020). Semantic transparency is not invisibility: A computational model of perceptually-grounded conceptual combination in word processing. Journal of Memory and Language, 112, 104104.
  • Günther, F., Marelli, M., & Bölte, J. (2020). Semantic transparency effects in German compounds: A large dataset and multiple-task investigation. Behavior Research Methods, 52, 1208-1224.
  • Günther, F., & Marelli, M. (2020). Trying to make it work: Compositional effects in the processing of compound "nonwords". Quarterly Journal of Experimental Psychology, 73, 1082-1091.
  • Günther, F., Rinaldi, L., & Marelli, M. (2019). Vector-space models of semantic representation from a cognitive perspective: A discussion of common misconceptions. Perspectives on Psychological Science, 14, 1006-1033.
  • Günther, F., Smolka, E., & Marelli, M. (2019). 'Understanding' differs between English and German: Capturing Systematic Language Differences of Complex Words. Cortex, 116, 168-175.
  • Günther, F., & Marelli, M. (2019). Enter sand-man: Compound processing and semantic transparency in a compositional perspective. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45, 1872–1882.
  • Forthmann, B., Oyebade, O., Ojo, A., Günther, F., & Holling, H. (2019). Application of latent semantic analysis to divergent thinking is biased by elaboration. Journal of Creative Behavior, 53, 559-575.
  • Günther, F., Dudschig, C., & Kaup, B. (2018). Symbol grounding without direct experience: Do words inherit sensorimotor activation from purely linguistic context? Cognitive Science, 42, 336-374.
  • Günther, F., & Marelli, M. (2018). The language-invariant aspect of compounding: Predicting  compound meanings across languages. In E. Cabrio, A. Mazzei, & F. Tamburini (Eds.), Proceedings of the Fifth Italian Conference on Computational Linguistics (pp. 230-234).  Turin, Italy: Accademia University Press.
  • Günther, F., & Marelli, M. (2016). Understanding Karma Police: The Perceived Plausibility of Noun Compounds as Predicted by Distributional Models of Semantic Representation. PLoS ONE, 11 (10), art.nr. E0163200.
  • Günther, F., Dudschig, C., & Kaup, B. (2016). Predicting lexical priming effects from distributional semantic similarities: A replication with extension. Frontiers in Psychology, 7, art.nr. 1646.
  • Günther, F., Dudschig, C., & Kaup, B. (2016). Latent Semantic Analysis cosines as a cognitive similarity measure: Evidence from priming studies. Quarterly Journal of Experimental Psychology, 69, 626-653.
  • Günther, F., Dudschig, C., & Kaup, B. (2015). LSAfun – An R package for computations based on Latent Semantic Analysis. Behavior Research Methods, 47, 930-944.