A tutorial on fitting joint models of M/EEG and behavior to understand cognition

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Modeling

Background:

• Joint modeling of human behavior and neural electrophysiological data (EEG/MEG) is crucial for understanding cognition.
• Researchers require knowledge of computational theories, M/EEG artifact correction, analysis techniques, cognitive modeling, and programming for implementation.
• Existing methods often analyze behavior and neural data separately, limiting comprehensive understanding.

Purpose of the Study:

• To provide practical steps and motivation for estimating parameters in joint models of human behavior and M/EEG data.
• To introduce researchers to techniques for building, evaluating, and comparing neurocognitive models.
• To facilitate the application of these methods to specific hypotheses in human decision-making theory.

Main Methods:

• The tutorial outlines parameter estimation techniques for joint models.
• It covers essential aspects including M/EEG artifact correction and analysis.
• Programming implementation using Python (with R links) is demonstrated through code examples.

Main Results:

• The paper presents a framework for joint modeling of behavior and M/EEG data.
• It offers practical guidance and code examples for researchers.
• The methodology is applicable across various modeling procedures and applications.

Conclusions:

• Joint modeling offers a powerful approach to understanding human cognition by integrating behavioral and neural data.
• The tutorial equips researchers with the necessary skills and tools to implement these advanced modeling techniques.
• This work supports hypothesis testing in cognitive neuroscience, particularly in decision-making research.