Professor Michel (Mike) Regenwetter (UIUC Psychology, Political Science, and Electrical and Computer Engineering) and his co-author, Assistant Professor Daniel Cavagnaro of California State University, Fullerton, received the 2025 annual award for the most outstanding paper published in Computational Brain & Behavior in the preceding three years. The paper is titled Probabilistic Choice Induced by Strength of Preference. It provides new distribution-free generalizations to the ubiquitous Logit and Probit choice models. The paper provides useful generalizations to the ubiquitous Logit, Luce, Softmax, and Probit choice models that are extremely popular across numerous scientific disciplines.
On a personal note, what does this achievement mean to you? How does it contribute to your own growth and aspirations?
In addition to peer review, this provides another level of support from colleagues for the quality and soundness of this research. Mathematical modeling and quantitative methods papers do not easily rake up huge citation numbers, so a recognition like this is nice to experience.
Looking ahead, how do you see this project evolving or expanding? What do you hope it will achieve in the long term?
I hope it helps scholars across behavioral, economic, neuro-, and social sciences to improve the precision of their research. The models in this paper have the potential to enhance reproducibility of research by helping scholars avoid unnecessary (and potentially tenuous) assumptions in their modeling or in their data analytics.
Is there anyone you would like to acknowledge for their contributions to this project’s success?
This paper is dedicated to the fond memory of William H. Batchelder (1940–2018). This project was born out of Bill’s stimulating questions about polyhedral representations for (standard) Fechnerian models at a U.C. Irvine-IMBS conference in 2014.
