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 HCI researchers and practitioners a better understanding of how people visually interact with computers, and provides a theoretical foundation for predictive analysis tools that can predict aspects of that interaction.

Human-computer systems intended for time-critical multitasking need to be designed with an understanding of how humans can coordinate and interleave perceptual, memory, and motor processes. This paper presents human performance data for a highly-practiced time-critical dual task. In the first of the two interleaved tasks, participants tracked a target with a joystick. In the second, participants keyed-in responses to objects moving across a radar display. Task manipulations include the peripheral visibility of the secondary display (visible or not) and the presence or absence of auditory cues to assist with the radar task. Eye movement analyses reveal extensive coordination and overlapping of human information processes and the extent to which task manipulations helped or hindered dual task performance. For example, auditory cues helped only a little when the secondary display was peripherally visible, but they helped a lot when it was not peripherally visible.

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.

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. Categories and subject descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces-- Evaluation/methodology, eye tracking,