Psychology | Methods
Cognitive Modeling
Multinomial Processing Tree Models
Multinomial processing tree (MPT) models are substantively motivated stochastic models for categorical data. MPT models assume that observed responses are based on specific sequences of cognitive states (processes). By formalizing the relationship between observed response categories and latent processing states, MPT models allow for estimating and testing the influence of certain cognitive processes. In my research, I use MPT models to approach substantive research questions and I also work on improving statistical inference in the context of these models.
- Erdfelder, E., Quevedo Pütter, J., & Schnuerch, M. (2023). On aggregation invariance of multinomial processing tree models. Manuscript submitted for publication.
- Reiber, F., Schnuerch, M., & Ulrich, R. (2022). Improving the efficiency of surveys with randomized response models: A sequential approach based on curtailed sampling. Psychological Methods, 27, 198–211.
- Schnuerch, M., & Erdfelder, E., Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326.
Computational Process Models
Computational models formalize the structure and processing architecture of cognitive mechanisms. For example, computational memory models describe the structural and procedural components underlying human memory. The precise formalization of assumptions about the underlying mechanisms allows for precise predictions, which contributes to our understanding of cognitive processes and their interactions.
- Brandt, M., Zaiser, A.-K., & Schnuerch, M. (2019). Homogeneity of item material boosts the list length effect in recognition memory: A global matching perspective. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45, 834–850. [doi]
