Changes in Preferences Reported After Choices Are Informative, Not Merely Statistical Artifacts

Decades of research has shown that people rate things more favorably if they have recently chosen them and less favorably if they have recently rejected them. This spreading apart of the ratings of the alternative options (“spreading of alternatives” or SoA) has been described as choice-induced preference change, where the act of choosing causes evaluations to systematically differ after choices relative to before. An important study pointed out that SoA can be detected in the data even without it being caused by a choice, as the result of a statistical artifact. This potential alternative source of SoA has encumbered researchers who investigate the phenomenon, impeding progress in the field field – and a deeper investigation of the cognitive mechanisms behind SoA. Here, we present a novel approach to control for the statistical artifact explanation through the use of regression analysis supported by computational simulations. Our approach provides a logical and computational manner of distinguishing the possible sources of SoA in experimental data that is quick and easy to implement. We demonstrate our approach by comparing theoretical predictions of the statistical artifact and other possible explanations of SoA with empirical data, and show that the statistical artifact alone cannot account for the data.