Classification Of Brain Signals Collected During A Rule Learning Paradigm

We propose incorporating biophysical data with behavioral data to inform digital learning environments on an individual’s current cognitive state and how it relates to their learning. We used a rule learning paradigm drawn from cognitive psychology to define phases of rule learning across multiple domains. This paradigm can simulate an inductive reasoning framework seen during mathematics education while reducing the number of covariates compared to real-world settings. We combined the time series brain data with behavioral and contextual data in machine learning models for prediction of rule learning phases with the aim of developing approaches to incorporate a mixture of behavioral and neural data into digital learning designs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.