In the course of running an eye-tracking experiment, one computer system or subsystem typically presents the stimuli to the participant and records manual responses, and another collects the eye movement data, with little interaction between the two during the course of the experiment. This article demonstrates how the two systems can interact with each other to facilitate a richer set of experimental designs and applications and to produce more accurate eye tracking data. In an eye-tracking study, a participant is periodically instructed to look at specific screen locations, or explicit required fixation locations (RFLs), in order to calibrate the eye tracker to the participant. The design of an experimental procedure will also often produce a number of implicit RFLs-screen locations that the participant must look at within a certain window of time or at a certain moment in order to successfully and correctly accomplish a task, but without explicit instructions to fixate those locations. In these windows of time or at these moments, the disparity between the fixations recorded by the eye tracker and the screen locations corresponding to implicit RFLs can be examined, and the results of the comparison can be used for a variety of purposes. This article shows how the disparity can be used to monitor the deterioration in the accuracy of the eye tracker calibration and to automatically invoke a recalibration procedure when necessary.This article also demonstrates how the disparity will vary across screen regions and participants and how each participant's unique error signature can be used to reduce the systematic error in the eye movement data collected for that participant.
This research investigates the cognitive strategies and eye movements that people use to search for a known item in a hierarchical computer display. Computational cognitive models were built to simulate the visual-perceptual and oculomotor processing required to search hierarchical and nonhierarchical displays. Eye movement data were collected and compared on over a dozen measures with the a priori predictions of the models. Though it is well accepted that hierarchical layouts are easier to search than nonhierarchical layouts, the underlying cognitive basis for this design heuristic has not yet been established. This work combines cognitive modeling and eye tracking to explain this and numerous other visual design guidelines. This research also demonstrates the power of cognitive modeling for predicting, explaining, and interpreting eye movement data, and how to use eye tracking data to confirm and disconfirm modeling details.
Visual search is an important part of human-computer interaction. It is critical that we build theory about how people visually search displays in order to better support the users' visual capabilities and limitations in everyday tasks. One way of building such theory is through computational cognitive modeling. The ultimate promise for cognitive modeling in HCI it to provide the science base needed for predictive interface analysis tools. This paper discusses computational cognitive modeling of the perceptual, strategic, and oculomotor processes people used in a visual search task. This work refines and rounds out previously reported cognitive modeling and eye tracking analysis. A revised "minimal model" of visual search is presented that explains a variety of eye movement data better than the original model. The revised model uses a parsimonious strategy that is not tied to a particular visual structure or feature beyond the location of objects. Three characteristics of the minimal strategy are discussed in detail.
Human visual search plays an important role in many human-computer interaction (HCI) tasks. Better models of visual search are needed not just to predict overall performance outcomes, such as whether people will be able to find the information needed to complete an HCI task, but to understand the many human processes that interact in visual search, which will in turn inform the detailed design of better user interfaces. This article describes a detailed instantiation, in the form of a computational cognitive model, of a comprehensive theory of human visual processing known as ''active vision'' (Findlay & Gilchrist, 2003). The computational model is built using the Executive Process-Interactive Control cognitive architecture. Eye-tracking data from three experiments inform the development and validation of the model. The modeling asks-and at least partially answers-the four questions of active vision: (a) What can be perceived in a fixation? (b) When do the eyes move? (c) Where do the eyes move? (d) What information is integrated between eye movements? Answers include: (a) Items nearer the point of gaze are more likely to be perceived, and the visual features of objects are sometimes misidentified. (b) The eyes move after the fixated visual stimulus has been processed (i.e., has entered working memory). (c) The eyes tend to go to nearby objects. (d) Only the coarse spatial information of what has been fixated is likely maintained between fixations. The model developed to answer these questions has both scientific and practical value in that the model gives Tim Halverson is a cognitive scientist with an interest in human-computer interaction, cognitive modeling, eye movements, and fatigue; he is a Research Computer Scientist in the Applied Neuroscience Branch of the Air Force Research Laboratory. Anthony Hornof is a computer scientist with an interest in human-computer interaction, cognitive modeling, visual search, and eye tracking; he is an Associate Professor in the
This research investigates the cognitive strategies and eye movements that people use to search for a known item in a hierarchical computer display. Computational cognitive models were built to simulate the visual-perceptual and oculomotor processing required to search hierarchical and nonhierarchical displays. Eye movement data were collected and compared on over a dozen measures with the a priori predictions of the models. Though it is well accepted that hierarchical layouts are easier to search than nonhierarchical layouts, the underlying cognitive basis for this design heuristic has not yet been established. This work combines cognitive modeling and eye tracking to explain this and numerous other visual design guidelines. This research also demonstrates the power of cognitive modeling for predicting, explaining, and interpreting eye movement data, and how to use eye tracking data to confirm and disconfirm modeling details.
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