Predicting Similarity Judgments in Intertemporal Choice with Machine Learning

Authors

Jeffrey R. Stevens, University of Nebraska-LincolnFollow
Leen-Kiat Soh, University of NebraskaFollow

ORCID IDs

0000-0003-2375-1360

Date of this Version

2018

Document Type

Article

Citation

Stevens, J.R. & Soh, L.-K. (2018). Predicting similarity judgments in intertemporal choice with machine learning. Psychonomic Bulletin & Review, 25(2), 627–635. (doi: 10.3758/s13423-017-1398-1)

https://link.springer.com/article/10.3758/s13423-017-1398-1

Comments

© Psychonomic Society, Inc. 2017. Used by permission.

Abstract

Similarity models of intertemporal choice are heuristics that choose based on similarity judgments of the reward amounts and time delays. Yet, we do not know how these judgments are made. Here, we use machine-learning algorithms to assess what factors predict similarity judgments and whether decision trees capture the judgment outcomes and process. We find that combining small and large values into numerical differences and ratios and arranging them in tree-like structures can predict both similarity judgments and response times. Our results suggest that we can use machine learning to not only model decision outcomes but also model how decisions are made. Revealing how people make these important judgments may be useful in developing interventions to help them make better decisions.

Supplementary Materials attached (below).