Predict this: Minimal commitments for testable predictive processing explanations

Predictive processing (PP) is a family of frameworks that link perception, action, learning, and attention to inference under uncertainty. PP terms (e.g., priors, prediction error, precision) are becoming more common, yet commitments often remain implicit. This flexibility allows multiple mappings from PP vocabulary to predictands, measures, and mechanisms, weakening inference. Here I propose five minimal commitments (PP-MC) as a reporting and design checklist for PP-framed mechanistic claims. PP-MC asks authors to specify: (1) the predictand and timescale, (2) an operational proxy for prediction error and its expected direction, (3) the relevant uncertainty or precision construct and how it is manipulated or measured, (4) the pathway by which error is reduced, and (5) at least one discriminating prediction against an alternative. A worked example in interoception illustrates how PP-consistent pathways imply different measures and manipulations. PP-MC aims to make PP-framed explanations auditable and more readily testable in a given study.