Predict this: Minimal commitments for testable predictive processing explanations.

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Theoretical Psychology

Background:

• Predictive processing (PP) frameworks integrate perception, action, learning, and attention through inference under uncertainty.
• Key PP terms like priors, prediction error, and precision are increasingly used, but their application remains implicit and variable.
• This implicit flexibility weakens scientific inference by allowing multiple interpretations of PP concepts.

Purpose of the Study:

• To propose a standardized checklist, five minimal commitments (PP-MC), for reporting and designing mechanistic claims within predictive processing frameworks.
• To enhance the testability and auditability of research framed by predictive processing.
• To provide a clear structure for specifying core components of PP-based mechanistic explanations.

Main Methods:

• The study outlines five essential components for PP-MC: specifying the predictand and timescale, defining prediction error proxies and direction, clarifying uncertainty/precision constructs, detailing error reduction pathways, and including discriminating predictions.
• A worked example using interoception demonstrates the application of PP-MC.
• The proposed checklist serves as a design and reporting tool.

Main Results:

• The PP-MC checklist provides a structured approach to operationalize PP concepts.
• Application of PP-MC clarifies how different pathways within PP imply distinct measures and manipulations.
• The framework enhances the clarity and testability of mechanistic claims in PP research.

Conclusions:

• The five minimal commitments (PP-MC) offer a practical solution to the ambiguity in current predictive processing research.
• Implementing PP-MC will improve the rigor, reproducibility, and scientific value of studies using predictive processing frameworks.
• This standardization facilitates clearer communication and more robust testing of hypotheses in cognitive science and related fields.