The Effect of Effort on the Allocation of Visual Attention During the Induction of General Anesthesia: A SEEV Model Analysis

Abstract

Anesthesiologists work in complex work environments where optimal scanning of information is critical for patient safety. The Salience, Effort, Expectancy, Value (SEEV) model can be used to model attention distributions of individuals. We used an existing data set of eye tracking data of anesthesiologists inducing general anesthesia to (1) develop a method for considering the effort parameter in the model in such an environment and (2) investigate the explanatory power of an EEV model compared to an EV model. To operationalize effort, we created a 3D model using Unreal Engine 4. We used Markov Chain Monte Carlo simulations to obtain EV and EEV model predictions. The inclusion of effort did not yield an advantage over the model which did not include effort. We discuss methodological considerations for future research and suggest to simultaneously consider salience and effort to be able to assess the role of effort more accurately.