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Related Experiment Video

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The Trier Social Stress Test Protocol for Inducing Psychological Stress
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Using machine learning to predict individual differences in psychological reactivities to social interactions.

Ole Hätscher1, Johannes L Klinz1, Niclas Kuper1

  • 1Department of Psychology, University of Munster.

Journal of Personality and Social Psychology
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

Predicting individual differences in psychological reactivities using machine learning showed limited success. Machine learning models slightly outperformed baseline predictions, highlighting the need for replication and further research into person-level variables.

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Area of Science:

  • Psychology
  • Computational Social Science

Background:

  • Individual differences in psychological reactivities are crucial in social interaction research.
  • Previous studies identified significant individual differences but lacked robust predictors.

Purpose of the Study:

  • To determine if individual differences in reactivities to social interactions can be predicted.
  • To identify key person-level variables for predicting these reactivities.

Main Methods:

  • Applied machine learning to three large experience sampling datasets (N=5,047).
  • Extracted individual differences in reactivities using multilevel modeling (random slopes).
  • Predicted reactivities using sociodemographics, personality, and attitudes, validated via cross-validation and independent samples.

Main Results:

  • Machine learning models showed limited overall predictive performance for individual differences in reactivities.
  • Complex models marginally outperformed a baseline model in most analyses.
  • SHapley Additive exPlanations identified predictive patterns and interactions.

Conclusions:

  • Replication of machine learning findings across different outcomes and samples is essential.
  • Identified predictive patterns warrant further investigation.
  • Results inform dynamic conceptualizations of personality and limitations of current ML for within-person data.