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Related Experiment Video

Updated: Jul 9, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Learning Under Uncertainty: Predictive Processing as an Integrative Framework for Educational Research.

Sabrina Trapp¹, Dezso Németh^2,3,4, Karolina Janacsek⁵

¹Macromedia University of Applied Sciences, München-Maxvorstadt, Germany. mailsabrinatrapp@gmail.com.

Integrative Psychological & Behavioral Science

|July 8, 2026

Summary

Predictive processing offers a unifying framework for neuroscience and education research. This approach explains how the brain learns by updating models based on prediction errors, guiding instruction for conceptual change.

Keywords:

Active inference Conceptual change Educational neuroscience Learning Predictive processing Uncertainty

Related Experiment Videos

Last Updated: Jul 9, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Area of Science:

Neuroscience
Education
Cognitive Science

Background:

Neuroscience and education research has yielded many findings but lacks a unifying theoretical framework.
Predictive processing, a theory of brain function, is a candidate framework for cognitive sciences.
Its applicability to educational contexts requires examination.

Purpose of the Study:

To investigate the transferability of predictive processing concepts to educational settings.
To propose constraints for instruction aimed at conceptual model revision under uncertainty.
To interpret developmental differences through the lens of predictive processing parameters.

Main Methods:

Conceptual analysis and integration of existing research from neuroscience and education.
Examination of predictive processing principles in relation to learning and development.
Proposal of a theoretical framework rather than specific instructional strategies.

Main Results:

Predictive processing concepts can be meaningfully applied to educational contexts.
Four key constraints are identified for instruction promoting conceptual change.
Developmental variations can be understood as differences in inferential parameters within this framework.

Conclusions:

Predictive processing provides a potential common vocabulary for diverse educational research findings.
This framework can integrate disparate findings and guide future research in educational neuroscience.
Further empirical work is needed to validate predictions and acknowledge the limitations of the theory.