I am a doctoral researcher working on the WIDE (Wide Incremental learning with Discrimination nEtworks) project funded by the European Research Council Advanced Grant to Harald Baayen.
My project focuses on the functional and distributional aspects of form variety in the speech signal. I am interested in the influence of contextual uncertainty on the observed variation in the speech signal and how experience affects the variance in articulatory gestures across population samples and contexts. I use computer simulations and statistical modeling of non-linear patterns to explore large collections of (spoken) language data and experimental data targeting aspects of information processing, articulation, response times and occurrence of disfluencies and pauses in conversational speech.
I hold degrees in computer science (BSc) and computational linguistics (MA). My professional background is in human-computer interaction and information design of natural user interfaces for learning environments. Before my doctoral studies, I worked as an interface prototype and experiment developer at the Leibniz-Institut für Wissensmedien in Tübingen (IWM). During my graduate studies, I narrowed my focus on the cognitive aspects of human and animal learning, information processing and the information structure of language. I am interested in a variety of topics related to thinking and talking, such as decision-making, problem-solving, human and animal organization and the evolution of cultural conventions, science, language and thought.
Problem Solving for Linguists
This course is about language science and introduces topics from a wide variety of disciplines relevant to computational modeling of language as a method of scientific inquiry. The first four sessions provide an overview of ideas underlying language theory, their impact on linguistic terminology, the design of language resources and the operationalization of linguistic abstractions, addressing the strengths and weaknesses of methods and abstractions commonly used in modeling language. In addition to this, we will review ideas and technological developments that have influenced the tradition of computational modeling of aspects of human cognitive performance and discuss them from the perspective of philosophy of science and human cognition. In the main part, we discuss readings on a variety of topics closely connected to language, covering behavior, aspects of decision making, measurement and the fundamental assumptions of quantitative methods. The objective of the course is to give a broader perspective on language, culture and thought, similarities and differences in the evolution of human and animal societies and the particulars of the human brain relevant to language, efficient performance, and organization. The idea behind all of this is that insights from a wide variety of disciplines can help understand and frame language questions in more interesting and productive ways.
Linke, M., Bröker, F., Ramscar, M., & Baayen, H. (2017). Are baboons learning “orthographic” representations? Probably not. PLoS ONE. https://doi.org/10.1371/journal.pone.0183876
Kammerer, Y., & Bohnacker, M. (2012). Children's web search with Google: the effectiveness of natural language queries. In H. Schelhowe (Ed.), Proceedings of the 11th International Conference on Interaction Design and Children IDC '12 (pp. 184-187). New York, NY: ACM Press.
Kammerer, Y., & Bohnacker, M. (2012). Unterstützung der Informationssuche von Grundschulkindern im Internet: Empfehlungen zur Gestaltung von Kinder-Suchmaschinen.In H. Gapski & T. Tekster (Eds.), Informationskompetenz von Kindern und Jugendlichen. Schriftenreihe Medienkompetenz des Landes Nordrhein-Westfalen (Bd. 14, pp. 51-65). Düsseldorf, München: kopaed.