Close
This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Return to Session

A robust Bayesian test for context effects in multi-attribute decision making

Authors
Dimitris Katsimpokis
University of Basel
Dr. Laura Fontanesi
University of Basel ~ Psychology
Jorg Rieskamp
University of Basel ~ Department of Psychology
Abstract

In the past decades, context effects have been crucial in the development of cognitive models of decisions between multi-attribute alternatives. Nevertheless, to this date, only few studies have discussed what the best practices to analyze context effects are. Context effects occur when participants prefer identical options more or less depending on the choice set they are embedded in. Context effects are measured using what is called the Relative choice Share of the Target (RST), i.e., the change in preference of a target option from one choice set to the next. In this talk, we discuss two ways of calculating the RST: one frequently used in the literature, and a novel one we propose. Through simulations, we show that our proposed RST analyses overcome shortcomings of the more traditional approach. In particular, it is resistant to biases due to unequal sample size across choice sets. Furthermore, we apply our model to four previously published context effect studies, and we show that some reported context effects can change substantially (from significant to non-significant and vice versa). Implications of these results for cognitive modeling and empirical research on context effects are further discussed.

Tags

Keywords

context effects
decision-making
Bayesian inference

Topics

Decision Making
Bayesian Modeling
Hypothesis Testing
Probabilistic Models
Discussion
New

There is nothing here, yet. Be the first to create a thread