A methodology for studying cognitive groupings in a target-tracking task

Abstract

-Subjects performing visual target tracking tasks have been shown to utilize perceptual organization. This organization has both Gestalt and goal-oriented features. Previous studies have attempted to use memory recall techniques to examine potential cognitive groupings in air traffic control (ATC), which is, in part, a complex target tracking task. In the present research, a special form of cluster analysis was successful in revealing cognitive groupings having appreciable influence on task performance in a targettracking task designed to resemble ATC. The method of cluster analysis was derived from the "virtual associative network" model of memory organization, and applied to the record of eye fixations in the course of task performance. Results using inexperienced subjects showed fixation clustering consistent with Gestalt factors. Task objectives (such as conflict detection) did not seem to affect grouping as much. The subjects' recall was generally poor, except where direct manipulation of targets occurred. We conclude that a) cognitive grouping influences performance, b) recall techniques may not be able to elicit subjects' cognitive groupings, and c) such groupings can be determined via analysis of eye fixations. These findings have implications for studying workload assessment and information structuring in complex visual scanning tasks.

Detecting single-target changes in multiple object tracking: The case of peripheral vision

“…Most interestingly, with regard to the observed gaze behavior, slower stimuli did not lead to more saccades, so a saccade-avoiding strategy was not evident. On the one hand, this result could have been expected, since increased effects of crowding are usually observed at higher stimulus speeds (Franconeri, Lin, Pylyshyn, Fisher, & Enns, 2008), which would lead to participants needing to saccade more frequently to targets because of potential collisions (Elfanagely, Haladjian, Aks, Kourtev, & Pylyshyn, 2011;Landry, Sheridan, & Yufik, 2001). On the other hand, although saccading helps update the position of single targets (Landry et al, 2001), the costs of eye movements increase with higher stimulus speeds because objects will move farther during the saccade if the stimulus speed is high (Huff, Papenmeier, Jahn, & Hesse, 2010).…”

“…On the one hand, this result could have been expected, since increased effects of crowding are usually observed at higher stimulus speeds (Franconeri, Lin, Pylyshyn, Fisher, & Enns, 2008), which would lead to participants needing to saccade more frequently to targets because of potential collisions (Elfanagely, Haladjian, Aks, Kourtev, & Pylyshyn, 2011;Landry, Sheridan, & Yufik, 2001). On the other hand, although saccading helps update the position of single targets (Landry et al, 2001), the costs of eye movements increase with higher stimulus speeds because objects will move farther during the saccade if the stimulus speed is high (Huff, Papenmeier, Jahn, & Hesse, 2010). Additionally, saccadic suppression of information processing (from about 75 ms before saccade onset to 50 ms after saccade offset) makes the updating process even more difficult as object speeds increase (Diamond, Ross, & Morrone, 2000).…”

Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications

“…The display utilizes the outputs of the assistive subsystems to provide situational awareness while driving. To minimize cognitive loading, only the obstacle map and guide views are shown to a driver to highlight the most relevant obstacles [15]. An example of a lane change with the Vehicle Iconic Surround Observer is shown in Fig.…”

Contextual Activity Visualization from Long-Term Video Observations

“…While providing expansive environmental context, we take care to avoid distractions that can detract from the principle monitoring task. 7 Instead of overloading the display with many annotations, we distill information and visualize it using icons and avatars (see Figure 7a). This filtered view uses automatic highlighting to limit the cognitive load on users and help focus their attention on the locations most likely to be interesting.…”