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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 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/ ). 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
DOI
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 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/ ).

Journal

Cognitive Therapy and Research – Springer Journals

Published: Jun 2, 2018

References

  • Recommender systems: The textbook
    Aggarwal, C
  • Attention modification program in individuals with generalized anxiety disorder
    Amir, N; Beard, C; Burns, M; Bomyea, J
  • A pilot study of an adaptive, idiographic, and multi-component attention bias modification program for social anxiety disorder
    Amir, N; Kuckertz, JM; Strege, MV
  • Combining computerized home-based treatments for generalized anxiety disorder: An attention modification program and cognitive behavioral therapy
    Amir, N; Taylor, CT
  • Specificity of Stroop interference in patients with pain and PTSD
    Beck, JG; Freeman, JB; Shipherd, JC; Hamblen, JL; Lackner, JM
  • Intentional forgetting of emotional words after trauma: A study with victims of sexual assault
    Blix, I; Brennen, T
  • Measuring emotion: The self-assessment manikin and the semantic differential
    Bradley, M; Lang, P
  • Affective norms for English words (ANEW): Instruction manual and affective ratings
    Bradley, M; Lang, P
  • Amazon’s Mechanical Turk
    Buhrmester, M; Kwang, T; Gosling, SD
  • Development and validation of a brief self-report measure of trauma exposure: The trauma history screen
    Carlson, EB; Smith, SR; Palmieri, PA; Dalenberg, C; Ruzek, JI; Kimerling, R; Spain, DA
  • On the psychometric principles of affect
    Carroll, JM; Yik, MSM; Russell, JA; Barrett, LF
  • The impact of intensive mindfulness training on attentional control, cognitive style, and affect
    Chambers, R; Lo, BCY; Allen, NB
  • Internet-delivered assessment and manipulation of anxiety-linked attentional bias: Validation of a free-access attentional probe software package
    Colin, M; Lih, YS; Elizabeth, MR; Lynlee, WC
  • Enhanced priming for trauma-related words predicts posttraumatic stress disorder
    Ehring, T; Ehlers, A
  • Inattentive responding in MTurk and other online samples
    Fleischer, A; Mead, AD; Huang, J
  • The individualized alcohol Stroop task: No attentional bias toward personalized stimuli in alcohol-dependents
    Fridrici, C; Leichsenring-Driessen, C; Driessen, M; Wingenfeld, K; Kremer, G; Beblo, T
  • Attentional retraining: A randomized clinical trial for pathological worry
    Hazen, RA; Vasey, MW; Schmidt, NB
  • Memory enhancement for emotional words: Are emotional words more vividly remembered than neutral words?
    Kensinger, EA; Corkin, S
  • National estimates of exposure to traumatic events and PTSD prevalence using DSM-IV and DSM-5 criteria
    Kilpatrick, DG; Resnick, HS; Milanak, ME; Miller, MW; Keyes, KM; Friedman, MJ
  • Matrix factorization techniques for recommender systems
    Koren, Y; Bell, R; Volinsky, C
  • Emotion words, regardless of polarity, have a processing advantage over neutral words
    Kousta, S-T; Vinson, DP; Vigliocco, G
  • International affective picture system (IAPS): Technical manual and affective ratings
    Lang, PJ; Bradley, MM; Cuthbert, BN
  • Selective attention and emotional vulnerability: Assessing the causal basis of their association through the experimental manipulation of attentional bias
    MacLeod, C; Rutherford, E; Campbell, L; Ebsworthy, G; Holker, L
  • Computer-delivered, home-based, attentional retraining reduces drinking behavior in heavy drinkers
    McGeary, JE; Meadows, SP; Amir, N; Gibb, BE
  • Cold-start problem in collaborative recommender systems: Efficient methods based on ask-to-rate technique
    Nadimi-Shahraki, M-H; Bahadorpour, M
  • R: A language and environment for statistical computing
  • Attentional biases to social and health threat words in individuals with and without high social anxiety or depression
    Roberts, KE; Hart, TA; Eastwood, JD
  • Attentional bias modification in Dutch veterans with posttraumatic stress disorder: A case series with a personalized treatment version
    Schoorl, M; Putman, P; Mooren, TM; Werff, S; Does, W
  • What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults
    Shao, Z; Janse, E; Visser, K; Meyer, AS
  • Can’t shake that feeling: event-related fMRI assessment of sustained amygdala activity in response to emotional information in depressed individuals
    Siegle, GJ; Steinhauer, SR; Thase, ME; Stenger, VA; Carter, CS
  • Stata statistical software: Release 14