Cognitive Modeling – Understanding Behavior Before It Happens
In our research on cognitive modeling, we go beyond merely fitting models to behavioral data. Our goal is to develop computational models that represent cognitive processes so precisely that they can predict a person’s behavior – even before a response is given.
This approach combines insights from cognitive science and artificial intelligence to create models of human thinking that are not just reactive but predictive. In doing so, we contribute to a deeper understanding of how people think, decide, and learn – opening new perspectives for personalized AI systems.Overview of Different Modeling Approaches
Here we collect interesting publications on various modeling approaches.
- Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59
- Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922. https://doi.org/10.1162/neco.2008.12-06-420
- Shinn, M., Lam, N. H., & Murray, J. D. (2020). A flexible framework for simulating and fitting generalized drift-diffusion models. ELife, 9. https://doi.org/10.7554/eLife.56938
Modeling of linguistic concepts, e.g., for use in verbal questionnaire scales and in cross-cultural research
- Bocklisch, F. (2019). An Different or the Same? Determination of Discriminatory Power Threshold and Category Formation for Vague Linguistic Frequency Expressions. Frontiers in Psychology, 10, https://doi.org/10.3389/fpsyg.2019.01559
- Bocklisch, F., Georg, A., Bocklisch, S.F. & Krems, J.F. (2013). Do you mean what you say? The effect of uncertainty avoidance on the interpretation of probability expressions - A comparative study between Spanish and German. In Knauff, M. Pauen, N.Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 1917-1922). Austin, TX: Cognitive Science Society
- Bocklisch, F., Bocklisch, S. F., & Krems, J.F. (2012). Sometimes, often, and always: Exploring the vague meanings of frequency expressions. Behaviour Research Methods, 44(1), 144-157. https://doi.org/10.3758/s13428-011-0130-8
Self-learning fuzzy algorithms for the recognition of driver intentions
- Bocklisch, F., Bocklisch, S.F., Beggiato, M., & Krems, J. F. (2017). Adaptive fuzzy pattern classification for the online detection of driver lane change intention. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.02.089
Fuzzy classification sequences for diagnostic reasoning (e.g., in medicine) and modeling of human expert knowledge
- Bocklisch, F. & Hausmann, D. (2018). Multidimensional fuzzy pattern classifier sequences for medical diagnostic reasoning. Applied Soft Computing, 66, 297-310. https://doi.org/10.1016/j.asoc.2018.02.041
- Bocklisch, F., Stephan, M., Wulfken, B., Bocklisch, S.F., & Krems, J.F. (2011). How Medical Expertise Influences the Understanding of Symptom Intensities – A Fuzzy Approach . In A. Holzinger and K.-M. Simonic (Hrsg.), Information Quality in e-Health: USAB 2011, LNCS 7058 (pp. 703-706). Springer: Heidelberg.
- Schmidt, K., & Hoffmann, K. H. (2019). Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure. In International Conference on Intelligent Human Systems Integration (pp. 497-503). Springer, Cham.
Website: https://www.nengo.ai/
