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Order-Constrained Models of Memory

Dr. Joseph Sommer
Rutgers University ~ Psychology
Prof. Pernille Hemmer
Rutgers University ~ Psychology
Michel Regenwetter
University of Illinois at Urbana-Champaign ~ Psychology
Daniel Cavagnaro
California State University, Fullerton ~ Department of Information Systems and Decision Sciences

Hypotheses in free recall experiments often predict a greater average recall for one type of stimuli compared to another type. A frequent assumption – often implicit in statistical tests of these hypotheses – is that item recall is normally distributed. However, this assumption can be problematic in the domain of memory. Additionally, common statistical testing methods for testing theories can be blunt instruments. Researchers may be interested in more nuanced hypotheses that are cumbersome to test with traditional methods. For example, ideal theories might even make granular predictions about the memorability of each studied item, including that certain individual items are equally memorable. Here, we propose order-constrained models for recall data as a fruitful method of analysis that allows researchers to formulate, and test, nuanced and fine-grained hypotheses about recall. We illustrate the benefits of order-constrained modeling by re-analyzing data from a pre-registered experiment on the memorability of supernatural, bizarre, and natural concepts. We formulate and test a series of plausible and nuanced hypotheses. Order-constrained inference reveals differences in evidential support between different possible mathematical formulations of a single verbal theory.



Order-constrained models
Theory testing
Hypothesis formulation

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Cite this as:

Sommer, J., Hemmer, P., Regenwetter, M., & Cavagnaro, D. (2023, July). Order-Constrained Models of Memory. Abstract published at MathPsych/ICCM/EMPG 2023. Via