The impact of engineering information format on task performance: Gaze scanning pattern analysis

The emergence of new visualization technologies such as Virtual Reality (VR) and Augmented Reality (AR) had been widely implemented in the Architecture, Engineering, and Construction (AEC) industry. Although cumulative evidence pointed out a positive impact of these visualization technologies on construction task performance, there is still an obvious disagreement on the benefits or implications of these new visualization technologies, due to the lack of understanding of the mechanisms in which the visualization affects cognitive processes related to information processing. To obtain more evidence, this paper presents a human-subject experiment (n = 90) to investigate the impact of information format on the performance of an industrial pipeline maintenance task. The investigation centers around how different engineering information formats affect the attention patterns as a potential explanation for the changes in performance. A between-group experiment design was used where the participants were randomly assigned to one of the three groups (2D group, 3D group, and VR group) depending on what type of information was given to review the pipe operation instruction. After the review session, the participants were asked to perform the operation task in the virtual environment based on their memory. The results showed that the 3D and VR groups outperformed the 2D group in task performance. The analysis of eye-tracking data further indicated that the information format significantly changed the gaze scanning pattern when participants were reviewing the operational instructions. We also found that the task performance was correlated with eye-tracking features including gaze movement and pupil dilation. Our findings provided more evidence about the mechanisms in which new visualization technologies affect the attention patterns, helped resolve the current disagreement within the literature. In addition, a prediction model was proposed to use eye-tracking features to predict construction task performance.