Psychonomic Bulletin & Review

, Volume 25, Issue 2, pp 627–635 | Cite as

Predicting similarity judgments in intertemporal choice with machine learning

Brief Report

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.

Keywords

Classification tree Decision tree Intertemporal choice Judgment Machine learning Similarity 

Notes

Acknowledgements

This research was funded by an Alexander von Humboldt Foundation TransCoop Grant and by National Science Foundation grants (NSF-1062045, NSF-1658837). We would like to thank Isabella Otto for collecting data in Germany; Duy Nguyen for developing the Java-based data collection program and helping analyze data; Nik Leger and Cherylynn Gibson for helping analyze data; Noah Svec for testing participants; and UNL’s CB3 Club for comments on an early draft.

Supplementary material

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Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.Department of Psychology, Center for Brain, Biology & BehaviorUniversity of Nebraska-LincolnLincolnUSA
  2. 2.Department of Computer Science and EngineeringUniversity of Nebraska-LincolnLincolnUSA

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