Predicting similarity judgments in intertemporal choice with machine learning

Jeffrey R. Stevens, Leen Kiat Soh

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)627-635
Number of pages9
JournalPsychonomic Bulletin and Review
Issue number2
StatePublished - Apr 1 2018


  • Classification tree
  • Decision tree
  • Intertemporal choice
  • Judgment
  • Machine learning
  • Similarity

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)


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