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Related Experiment Video

Updated: Jan 6, 2026

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Personalizing Affective Stimuli Using a Recommender Algorithm: An Example with Threatening Words for Trauma Exposed

Andrea N Niles1,2, Aoife O'Donovan1,2

  • 1Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 4150 Clement Street, San Francisco, CA, USA.

Cognitive Therapy and Research
|October 2, 2019
PubMed
Summary

Machine learning algorithms personalize threatening words for trauma research. This approach enhances emotional reactivity in participants, outperforming generic word sets for more effective studies and potential remote treatments.

Keywords:
AffectAnxietyEmotionPersonalizationPersonalized medicinePosttraumatic stress disorderRecommender algorithmTrauma

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Area of Science:

  • Affective science
  • Clinical psychology
  • Computational psychiatry

Background:

  • Experimental research in affective and clinical science relies on evocative stimuli like words to elicit emotional responses.
  • Individual differences in emotional reactivity to stimuli pose a challenge for selecting universally effective materials.
  • Personalizing stimuli is crucial for accurately studying emotional processes in diverse populations.

Purpose of the Study:

  • To apply a novel recommender algorithm for selecting highly threatening words tailored to individuals within a trauma-exposed population.
  • To evaluate the efficacy of algorithm-driven personalized stimuli compared to non-personalized word sets.

Main Methods:

  • A user-user collaborative filtering recommender algorithm was trained on threat ratings from 837 participants for 513 threatening words.
  • The algorithm predicts individual word threat ratings based on similarities between participants.
  • Algorithm-selected words were compared against random, previously used, and trauma-specific word sets.

Main Results:

  • Personalized word sets selected by the recommender algorithm elicited significantly higher threat ratings than non-personalized sets.
  • Large effect sizes (d = 2.10–2.92) demonstrated the substantial impact of personalized stimuli.
  • The algorithm effectively identified words that maximized emotional reactivity for individuals.

Conclusions:

  • Recommender algorithms offer an automated method for personalizing stimuli to maximize emotional reactivity in research.
  • This approach has significant implications for enhancing experimental paradigms in affective and clinical science.
  • The developed algorithm shows promise for personalizing remote behavioral treatments, improving accessibility and efficacy.