The Amorphous Fixation Measure Revisited: with Applications to Autism

The analysis of eye-tracking data hinges on the ability of automated algorithms to separate rapid saccadic eye movements from stable eye fixations. However, though it has long been known that changing the parameters of fixationidentification algorithms can lead to very different qualitative impressions, less is known about how algorithmic parameters interact with quantitative eye-tracking measures. In this study we show that by manipulating aspects of fixation identification, we can completely reverse the patterns of observed results for mean fixation duration, a measure traditionally associated with cognitive load. However, by linearly mapping mean fixation duration over its parameter space, we obtain a new formulation which addresses many of the deficits of the standard analysis. We use our methods to analyze the gaze patterns of toddlers with autism spectrum disorder and control populations and discuss the observed differences in terms of the physical and cognitive ramifications of our methodology.