Scanpath analysis into the wild: the spatiotemporal distribution of fixations as an indicator of driver’s mental workload

Past studies using the distribution of eye fixations as an indicator of mental workload are limited to simulations and laboratory tasks. Hence, this assessment strategy has not yet been proven useful in real- world settings. In order to bridge this gap, in this study eye movements of a group of individuals were recorded while driving a car in a suburban road. Drivers’ scanpaths during driving and during driving while performing mundane secondary tasks were compared in this study. A more grouped pattern of fixations was expected in the dual-task condition than in the driving-only condition. As expected, results showed the effectiveness the spatiotemporal distribution of fixations in correctly discriminating between task load conditions, therefore indicating its usefulness for assessing mental workload also in complex real-world tasks.

Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison

Eye tracking has commonly been used to investigate how users interact with web pages, with the goal of improving their usability. This article comprehensively revisits the techniques that could be applicable to eye tracking data for analysing user scanpaths on web pages. It also uses a third-party eye tracking study to compare these techniques. This allows researchers to recognise existing techniques for their goals, understand how they work and know their strengths and limitations so that they can make an efficient choice for their studies. These techniques can mainly be used for calculating similarities/dissimilarities between scanpaths, computing transition probabilities between web page elements, detecting patterns in scanpaths and identifying common scanpaths. The scanpath analysis techniques are classified into four groups by their goals so that researchers can directly focus on the appropriate techniques for a sequential analysis of user scanpaths on web pages. This article also suggests dealing with the limitations of these techniques by pre-processing eye tracking data, considering cognitive processing and addressing their reductionist approach.