Participatory Development and Psychometric Evaluation of the Introspective Predictive Processing Inventory (IPPI): A Self-Report Measure for Autistic and Non-autistic Adults

Traditional self-report autism measures are often constructed from an "outside view" by non-autistic researchers rather than reflecting authentic autistic experiences. Predictive processing theory offers a promising framework for understanding autism through internal cognitive processes, but comprehensive tools assessing these characteristics and their associated daily challenges have been lacking. This study aimed to develop and validate the Introspective Predictive Processing Inventory (IPPI), a self-report measure assessing predictive processing characteristics and their subjective consequences in everyday life. Through community-led, participatory research, we developed a 65-item pilot version and employed a five-stage validation approach across three samples (N = 790). We used network-based item optimization, exploratory and confirmatory factor analyses, measurement invariance testing, and convergent validity assessment. Network optimization reduced the scale to 18 items while maintaining excellent reliability and improved discriminative power. Exploratory factor analysis revealed a stable two-factor structure: "Prediction Integration and Interpretation" and "Prediction Error Sensitivity and Stability Needs". The IPPI demonstrated exceptional discriminative validity (AUC > 0.97), strong convergent validity with established measures, measurement invariance across groups, and independence from general cognitive abilities. The IPPI provides a validated tool for assessing internal predictive processing experiences and their daily consequences, advancing autism research that bridges predictive processing theory with lived experiences.