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

Personalizing Affective Stimuli Using a Recommender Algorithm: An Example with Threatening Words... 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 algo- rithms can automate the personalization of stimuli from a large pool of possible stimuli to maximize emotional reactivity in research paradigms. These http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cognitive Therapy and Research Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply
Subject
Medicine & Public Health; Quality of Life Research; Clinical Psychology; Cognitive Psychology
ISSN
0147-5916
eISSN
1573-2819
D.O.I.
10.1007/s10608-018-9923-8
Publisher site
See Article on Publisher Site

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 algo- rithms can automate the personalization of stimuli from a large pool of possible stimuli to maximize emotional reactivity in research paradigms. These

Journal

Cognitive Therapy and ResearchSpringer Journals

Published: Jun 2, 2018

References

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