Michelle Dawson posts

Post published on Bluesky on 7 May 2024 12:21

Bluesky DOI work crossref Extract quoted in the post External link integrated into the post Autism terms

1 integrated reply by Michelle Dawson

7 May 2024 12:22

Note: "While the development team aimed to objectively represent the diversity and nuance of the prediction construct, there may be unaddressed bias in selection of items and content that align with their own proposed framework"

View reply on Bluesky

Structured cited links

1 cited resource

DOI work Fetched Post crossref

Amanda M. O’Brien, Toni A. May, Kristin L. K. Koskey, Lindsay Bungert, Annie Cardinaux, Jonathan Cannon, Isaac N. Treves, Anila M. D’Mello, et al. (2025). Development of a Self-Report Measure of Prediction in Daily Life: The Prediction-Related Experiences Questionnaire. Journal of Autism and Developmental Disorders, 55(7), 2550-2565. Springer Science and Business Media LLC.

Publication date
7 May 2024
Identifier
10.1007/s10803-024-06379-2
Authors
Amanda M. O’Brien, Toni A. May, Kristin L. K. Koskey, Lindsay Bungert, Annie Cardinaux, Jonathan Cannon, Isaac N. Treves, Anila M. D’Mello, Robert M. Joseph, Cindy Li, Sidney Diamond, John D. E. Gabrieli, Pawan Sinha
Source
Journal of Autism and Developmental Disorders
Details
55(7), 2550-2565
Reference type
article
Publisher
Springer Science and Business Media LLC
Metadata source
crossref

Abstract

Abstract Purpose 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. Methods 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. Results 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. Conclusions 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.

Study authors in this cited reference