Convergence Across Multimodal Affect Data in Aviation Training

• In Event: Studies of Learning Simulations

Abstract

Affect impacts learning by influencing various cognitive, psychomotor and motivational processes. Recent research on affect has explored multimodal means of assessment. However, multimodal data convergence and interpretation remains a relatively new field. This paper examines affect’s role in aviation training using a multimodal affect assessment protocol. As participants performed ten simulated aviation tasks, various dimensions of affect were recorded. We examined convergence among subjective report of affective correlates, physiological response (EDA), and behavioral affect changes (facial expression) with and without baseline removal, and the results were compared. We found that data convergence becomes more evident after baseline subtraction from facial expression and from EDA measures. Our findings support use of baseline removal in a multimodal assessment protocol for aviation.

Authors

• Leo Holton, McGill University

• Tianshu Li, McGill University

• Susanne P. Lajoie, McGill University

• Alejandra Ruiz-Segura, McGill University