Using situation awareness measures to characterize mental models in an inductive reasoning task

Abstract

The objective of this study was to develop and validate an empirical method of situation awareness (SA) assessment for characterizing mental models in an inductive reasoning task. We conducted an experiment in which participants watched videos of two detective stories while simultaneously playing a simple shooting game. Participant SA was measured during the trial and post-trial measures included concept mapping and knowledge tests on the stories. A fuzzy inference model was developed to classify SA measurement outcomes according to patterns that matched with hypothesized types of mental models. The model type identification, based on fuzzy inference, revealed utility for predicting knowledge test performance. The fuzzy model outputs and SA measures were found to be independent from (or complementary to) concept map measures. The results provided empirical evidence of the utility of SA measures for inferring forms of mental models and predicting knowledge of an inductive reasoning task, independent of an existing assessment approach (concept mapping).

Keywords:

• Situation awareness
• mental models
• inductive reasoning
• fuzzy logic
• concept mapping

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Tao Zhang

Tao Zhang is a UX designer at Amazon Web Services. He received his PhD in Industrial Engineering from North Carolina State University in 2009. His current research interests include user search behavior, data analytics, and web usability.

David Kaber

David Kaber is the Dean’s leadership professor and chair of industrial and systems engineering department at University of Florida. Dr. Kaber is a Fellow of the Human Factors and Ergonomics Society and the Institute of Industrial and Systems Engineers. He is a Certified Human Factors Professional and a Certified Safety Professional. He received his PhD in Industrial Engineering from Texas Tech University in 1996. His research interests include cognitive engineering and human-automation interaction.

Maryam Zahabi

Maryam Zahabi is an assistant professor of industrial and systems engineering at Texas A&M University and directs the Human-Systems Interaction Laboratory. She received her PhD in Industrial and Systems Engineering from North Carolina State University in 2017. Her research is focused of human performance modeling and human-system interactions in safety critical situations.