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Cognitive Therapy and Research

, Volume 42, Issue 6, pp 747–757 | Cite as

Personalizing Affective Stimuli Using a Recommender Algorithm: An Example with Threatening Words for Trauma Exposed Populations

  • Andrea N. Niles
  • Aoife O’Donovan
Original Article

Abstract

Experimental paradigms used in affective and clinical science often use stimuli such as images, scenarios, videos, or words to elicit emotional responses in study participants. Choosing appropriate stimuli that are highly evocative is essential to the study of emotional processes in both healthy and clinical populations. Selecting one set of stimuli that will be relevant for all subjects can be challenging because not every person responds the same way to a given stimulus. Machine learning can facilitate the personalization of such stimuli. The current study applied a novel statistical approach called a recommender algorithm to the selection of highly threatening words for a trauma-exposed population (N = 837). Participants rated 513 threatening words, and we trained a user–user collaborative filtering recommender algorithm. The algorithm uses similarities between individuals to predict ratings for unrated words. We compared threat ratings for algorithm-based word selection to a random word set, a word set previously used in research, and trauma-specific word sets. Algorithm-selected personalized words were more threatening compared to non-personalized words with large effects (ds = 2.10–2.92). Recommender algorithms can automate the personalization of stimuli from a large pool of possible stimuli to maximize emotional reactivity in research paradigms. These methods also hold potential for the personalization of behavioral treatments administered remotely where a provider is not available to tailor an intervention to the individual. The word personalization algorithm is available for use online (https://threat-word-predictor.herokuapp.com/).

Keywords

Anxiety Posttraumatic stress disorder Emotion Affect Recommender algorithm Trauma Personalization Personalized medicine 

Notes

Acknowledgements

The current project was funded by the UCSF Digital Mental Health Resource Allocation Program. Dr. Niles is funded by the San Francisco VA Advanced Women’s Health Fellowship and Dr. O'Donovan is funded by a K01 Award from the National Institute of Mental Health (K01MH109871) and a University of California Hellman Award. We also acknowledge Vivien Li for her significant contributions during the data collection phase of this project.

Funding

Funding was provided by University of California, San Francisco (Grant No. 7710 133105 7500634 45), the San Francisco VA Advanced Women's Health Fellowship, a K01 Career Development Award (K01MH109871) and a University of California Hellman Award.

Compliance with Ethical Standards

Conflict of Interest

Drs. Niles and O’Donovan declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Animal Rights Statements

No animal studies were carried out by the authors for this article.

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2018

Authors and Affiliations

  1. 1.Department of Psychiatry and Weill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoUSA
  2. 2.San Francisco Veterans Affairs Medical CenterSan FranciscoUSA

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