Recommendation as Generalization: Using Big Data to Evaluate Cognitive Models

David D. Bourgin, Joshua T. Abbott, Thomas L. Griffiths

Research output: Contribution to journalArticlepeer-review


The explosion of data generated during human interactions online presents an opportunity for psychologists to evaluate cognitive models outside the confines of the laboratory. Moreover, the size of these online data sets can allow researchers to construct far richer models than would be feasible with smaller in-lab behavioral data. In the current article, we illustrate this potential by evaluating 3 popular psychological models of generalization on 2 web-scale online data sets typically used to build automated recommendation systems. We show that each psychological model can be efficiently implemented at scale and in certain cases can capture trends in human judgments that standard recommendation systems from machine learning miss. We use these results to illustrate the opportunity Internet-scale data sets offer to psychologists and to underscore the importance of using insights from cognitive modeling to supplement the standard predictive-analytic approach taken by many existing machine learning approaches.

Original languageEnglish (US)
Pages (from-to)1398-1409
Number of pages12
JournalJournal of Experimental Psychology: General
Issue number7
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental Neuroscience
  • General Psychology


  • big data
  • cognitive modeling
  • generalization
  • machine learning


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