An experiment investigates (1) how the physical structure of a computer screen layout affects visual search and (2) how people select a found target object with a mouse. Two structures are examinedlabeled visual hierarchies (groups of objects with one label per group) and unlabeled visual hierarchies (groups without labels). Search and selection times were separated by imposing a point-completion deadline that discouraged participants from moving the mouse until they found the target. The observed search times indicate that labeled visual hierarchies can be searched much more efficiently than unlabeled visual hierarchies, and suggest that people use a fundamentally different strategy for each of the two structures. The results have implications for screen layout design and cognitive modeling of visual search. The observed mouse pointing times suggest that people use a slower and more accurate speed-accuracy operating characteristic to select a target with a mouse when visual distractor...

This article investigates the cognitive strategies that people use to search computer displays. Several different visual layouts are examined: unlabeled layouts that contain multiple groups of items but no group headings, labeled layouts in which items are grouped and each group has a useful heading, and a target-only layout that contains just one item. A number of plausible strategies were proposed for each layout. Each strategy was programmed into the EPIC cognitive architecture, producing models that simulate the human visual-perceptual, oculomotor, and cognitive processing required for the task. The models generate search time predictions. For unlabeled layouts, the mean layout search times are predicted by a purely random search strategy, and the more detailed positional search times are predicted by a noisy systematic strategy. The labeled layout search times are predicted by a hierarchical strategy in which first the group labels are systematically searched, and then the contents of the target group. The target-only layout search times are predicted by a strategy in which the eyes move directly to the

Queueing Network-Model Human Processor (QN-MHP) is a computational architecture that integrates two complementary approaches to cognitive modeling: the queueing network approach and the symbolic approach (exemplified by the MHP/GOMS family of models, ACT-R, EPIC, and SOAR). Queueing networks are particularly suited for modeling parallel activities and complex structures. Symbolic models have particular strength in generating a person’s actions in specific task situations. By integrating the two approaches, QN-MHP offers an architecture for mathematical modeling and generating in real-time concurrent activities in a truly concurrent manner. QN-MHP expands the three discrete serial stages of MHP into three continuoustransmission subnetworks of servers, each performing distinct psychological functions specified with a GOMSstyle language. Multitask performance emerges as the behavior of multiple streams of information flowing through a network, with no need to devise complex, task specific procedures to either interleave production rules into a serial program (ACT-R) or for an executive process to interactively control task processes (EPIC). Using QN-MHP, a driver performance model was created and interfaced with a driving simulator to perform a vehicle steering and a map reading task concurrently and in real time. The performance data of the model are

lar fixation, and how this should be incorporated into a predictive model, (2) how existing models can be refined to better account for new eye movement data, and (3) how to categorize and classify eye tracking data to identify the cognitive strategies that likely produced it. The promise for cognitive modeling in Human-Computer Interaction Cognitive models are computer programs that behave in some way like humans. In the context of this discussion, and in most cognitive modeling, the models emulate the perceptual, cognitive, and motor processes that a person would use to accomplish a specified piece of work--a task--and take the same amount of time that a human would take. Cognitive modeling is useful to the design and analysis of user interfaces because the modeling reveals patterns of behavior at a level of detail not otherwise available to analysts and designers (Gray, John & Atwood, 1993). The ultimate promise for cognitive modeling in design and analysis of user interfaces is t

resent and analyze this eye movement data with respect to the specific theories of the models. Issues I have encountered and would like to address My major research interests relate to how highly visual interfaces can better support human patterns of creativity. For example, how can data visualization tools better support complex decision and problem-solving tasks? Research problems I would like to solve include: 1. How can eye tracking best serve the field of HCI? The answer will most certainly involve automated capture, representation, and analysis of visual behavior. Numerous researchers are searching for ways that eye tracking can serve HCI, but outstanding opportunities have yet to emerge (Goldberg, 2000; Goldberg & Kotval, 1998). The web foraging work at XeroxPARC seems particularly promising, largely part because it is built on a theory of web usage that can be evaluated and is useful outside of the context of the actual usability analysis (Card et al., 2001). 2. How can eye