Journal article
Development of a Self-Report Measure of Prediction in Daily Life: The Prediction-Related Experiences Questionnaire
Journal of autism and developmental disorders
07 May 2024
PMID: 38713266
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Predictions are complex, multisensory, and dynamic processes involving real-time adjustments based on environmental inputs. Disruptions to prediction abilities have been proposed to underlie characteristics associated with autism. While there is substantial empirical literature related to prediction, the field lacks a self-assessment measure of prediction skills related to daily tasks. Such a measure would be useful to better understand the nature of day-to-day prediction-related activities and characterize these abilities in individuals who struggle with prediction.
An interdisciplinary mixed-methods approach was utilized to develop and validate a self-report questionnaire of prediction skills for adults, the Prediction-Related Experiences Questionnaire (PRE-Q). Two rounds of online field testing were completed in samples of autistic and neurotypical (NT) adults. Qualitative feedback from a subset of these participants regarding question content and quality was integrated and Rasch modeling of the item responses was applied.
The final PRE-Q includes 19 items across 3 domains (Sensory, Motor, Social), with evidence supporting the validity of the measure's 4-point response categories, internal structure, and relationship to other outcome measures associated with prediction. Consistent with models of prediction challenges in autism, autistic participants indicated more prediction-related difficulties than the NT group.
This study provides evidence for the validity of a novel self-report questionnaire designed to measure the day-to-day prediction skills of autistic and non-autistic adults. Future research should focus on characterizing the relationship between the PRE-Q and lab-based measures of prediction, and understanding how the PRE-Q may be used to identify potential areas for clinical supports for individuals with prediction-related challenges.
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1 citations in Scopus
Details
- Title
- Development of a Self-Report Measure of Prediction in Daily Life: The Prediction-Related Experiences Questionnaire
- Creators
- Amanda M O'Brien - Massachusetts Institute of TechnologyToni A May - Drexel UniversityKristin L K Koskey - Drexel UniversityLindsay Bungert - McGovern Institute for Brain ResearchAnnie Cardinaux - McGovern Institute for Brain ResearchJonathan Cannon - Massachusetts Institute of TechnologyIsaac N Treves - McGovern Institute for Brain ResearchAnila M D'Mello - Department of Psychiatry and O'Donnell Brain Institute, UT Southwestern Medical Center, Dallas, TX, USARobert M Joseph - Boston UniversityCindy Li - Center for Autism and Related DisordersSidney Diamond - Massachusetts Institute of TechnologyJohn D E Gabrieli - Center for Autism and Related DisordersPawan Sinha - Massachusetts Institute of Technology
- Publication Details
- Journal of autism and developmental disorders
- Publisher
- Springer Nature
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Education
- Web of Science ID
- WOS:001215334000001
- Scopus ID
- 2-s2.0-85192248939
- Other Identifier
- 991021876015504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Psychology, Developmental