Flipping the stimulus: Effects on scanpath coherence?

Abstract

In experiments investigating dynamic tasks, it is often useful to examine eye movement scan patterns. We can present trials repeatedly and compute within-subjects/conditions similarity in order to distinguish between signal and noise in gaze data. To avoid obvious repetitions of trials, filler trials must be added to the experimental protocol, resulting in long experiments. Alternatively, trials can be modified to reduce the chances that the participant will notice the repetition, while avoiding significant changes in the scan patterns. In tasks in which the stimuli can be geometrically transformed without any loss of meaning, flipping the stimuli around either of the axes represents a candidate modification. In this study, we examined whether flipping of stimulus object trajectories around the x-and y-axes resulted in comparable scan patterns in a multiple object tracking task. We developed two new strategies for the statistical comparison of similarity between two groups of scan patterns, and then tested those strategies on artificial data. Our results suggest that although the scan patterns in flipped trials differ significantly from those in the original trials, this difference is small (as little as a 13 % increase of overall distance). Therefore, researchers could use geometric transformations to test more complex hypotheses regarding scan pattern coherence while retaining the same duration for experiments.Keywords Eye movements . Scanpath . Group similarity . Correlation distanceIn natural tasks, humans direct their eyes toward objects of interest to make the best use of the high acuity vision in the fovea. Many factors influence eye movements. However, it is possible to get consistent scan patterns and, therefore, to separate signal from noise if the stimuli are presented repeatedly. However, repetition introduces the risk that the participants will recognize the repeated scenes and alter their gaze behavior. In many tasks, repeated presentation of the same scene is undesirable, because some subjects will recognize the scene and examine previously unexplored areas, encode additional details, and/or find targets more efficiently on the basis of their previous experience. Dorr, Martinetz, Gegenfurtner, and Barth (2010) compared the variability of eye movements during free viewing of dynamic natural scenes and found that whereas between-subjects coherence was maximal at the first presentation of the movie, it decreased during later presentations throughout the day. Dorr et al. suggested that this decrease in the between-subjects coherence was caused by a rising influence of individual viewing strategies. Gaze patterns are even affected when the stimulus is not explicitly recognized. In studies on contextual cueing, Chun and Jiang (1998) showed that people implicitly learn the positions of targets over time, which then results in shorter response times.One way to decrease the chance that participants will recognize the repetition is to increase the number of filler trials. The disadvantage of this a...

Eye Behavior During Multiple Object Tracking and Multiple Identity Tracking

“…To prevent the contamination of observers noticing the repetition and consciously learning the trials, Děchtěrenko, Lukavský, and Holmqvist [19] geometrically flipped the trajectories along the x - or y -axes in the repeated trials. In addition, the trajectories shared common segments for 6 s while differed for the rest (2 s) of the motion.…”

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“…We calculated gaze similarity for each pair of motion trajectories using Pearson correlations (Děchtěrenko et al, 2017;Lukavský & Děchtěrenko, 2016). Before the analysis, we first trimmed the recorded eye-movements to common 6-second trajectory segments and binned the data in a 3D spatiotemporal matrix with a bin size of 0.25° × 0.25° × 10 ms.…”

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“…We used a data-driven approach with no a priori assumption about the underlying gaze model (target centroid, crowding reduction, etc.). We extended the method based on comparisons of spatiotemporal saliency maps (Děchtěrenko, Lukavský, & Holmqvist, 2016;Lukavský, 2013). In particular, we presented identical trajectories forward and backward (see Fig.…”