Development of a novel hybrid cognitive model validation framework for implementation under COVID-19 restrictions

Area of Science:

• Cognitive Engineering
• Human-Computer Interaction
• Research Methodology

Background:

• COVID-19 restrictions necessitated remote research methods.
• Traditional cognitive model validation often requires in-person observation.
• Developing effective cognitive engineering systems demands efficient validation processes.

Purpose of the Study:

• To develop a novel framework for validating cognitive models adaptable to remote work scenarios.
• To establish an objective method for assessing initial model validity with minimal reliance on direct observation.
• To reduce costs and accelerate validation timelines in cognitive engineering.

Main Methods:

• A three-stage hybrid validation framework was designed, integrating argument-based validation, cognitive walkthroughs, and reflexivity assessments.
• The framework was adapted to comply with COVID-19-related remote working restrictions.
• A case study involving a cognitive model for cardiovascular surgery demonstrated the framework's application.

Main Results:

• The proposed framework enables objective validity assessment without in-person observations.
• It minimizes the need for remote usability and observational studies.
• The framework proved adaptable and effective in a real-world validation task.

Conclusions:

• The developed framework offers a structured and efficient approach to cognitive model validation in remote settings.
• It can be readily implemented by small research teams, enhancing confidence in early-stage model assumptions.
• This methodology supports faster development cycles for cognitive engineering systems post-COVID-19.