Search for: All records

Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K and real-time index of pupillary activity relate to collaborative behavior. It was observed that the time a group takes to complete a puzzle is related to the ambient visual scanning behavior quantified and groups that spent more time had more scanning behavior. User experience questionnaire results suggest that their dashboard provides a comparatively good user experience. 

A novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-à-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper’s contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task. 

Abstract The enhancement hypothesis suggests that deaf individuals are more vigilant to visual emotional cues than hearing individuals. The present eye-tracking study examined ambient–focal visual attention when encoding affect from dynamically changing emotional facial expressions. Deaf (n = 17) and hearing (n = 17) individuals watched emotional facial expressions that in 10-s animations morphed from a neutral expression to one of happiness, sadness, or anger. The task was to recognize emotion as quickly as possible. Deaf participants tended to be faster than hearing participants in affect recognition, but the groups did not differ in accuracy. In general, happy faces were more accurately and more quickly recognized than faces expressing anger or sadness. Both groups demonstrated longer average fixation duration when recognizing happiness in comparison to anger and sadness. Deaf individuals directed their first fixations less often to the mouth region than the hearing group. During the last stages of emotion recognition, deaf participants exhibited more focal viewing of happy faces than negative faces. This pattern was not observed among hearing individuals. The analysis of visual gaze dynamics, switching between ambient and focal attention, was useful in studying the depth of cognitive processing of emotional information among deaf and hearing individuals. 

In this paper I review gaze-based interaction, distinguishing eye movement analysis from synthesis in virtual reality and games for serious applications. My focus is on four forms of gaze-based interaction: diagnostic, active, passive, and expressive. In discussing each, I briefly review seminal results and recent advancements, highlighting outstanding research problems. 

A variety of psychological disorders like antisocial personality disorder have been linked to impairments in facial emotion recognition. Exploring eye movements during categorization of emotional faces is a promising approach with the potential to reveal possible differences in cognitive processes underlying these deficits. Based on this premise we investigated whether antisocial violent offenders exhibit different scan patterns compared to a matched healthy control group while categorizing emotional faces. Group differences were analyzed in terms of attention to the eyes, extent of exploration behavior and structure of switching patterns between Areas of Interest. While we were not able to show clear group differences, the present study is one of the first that demonstrates the feasibility and utility of incorporating recently developed eye movement metrics such as gaze transition entropy into clinical psychology.