We describe a GOMS model of a ship-board Radar Operator's behavior while monitoring air and sea traffic. GOM S is a technique that has been successfully used in Human-Computer Interaction to generate engineering models of human performance. Based on the GOM S model developed, we identified those portions of the task where an intelligent agent would be most able to assist operators in the performance of their duties, and the nature of the knowledge that will be required for the task. W e present the results of a simulated execution of the model in a sample scenario, which predicted the operator's responses with a high degree of accuracy.

This paper presents a follow-up to the ATM-Soar models presented at 1993 Meeting of the Cognitive Science Society and the CHI 1994 Research Symposium. The original work described the use of the Soar cognitive architecture to simulate user learning with different ATM interfaces. In particular, it focused on the relative effects of interface instructions (e.g., "Insert card into slot") and perceptual attentional cues (e.g., a flashing area around the card slot) on learning and performance. The study described here involves getting human data on the same tasks to test the predictions of the computational models. The ATM task is simulated on a PC in order to contrast three types of interface conditions: just instructions, instructions plus flashing, and just flashing. Subjects must insert a bank card, check the account balance, and withdraw money. They are asked to repeat the task four times so that the effects of training on performance and teaming can be observed. The data suggests that subjects learn to perform the task faster with attentional attractors, as the Soar model predicted. More interestingly, the Soar model also predicted that people would do better without instructions when there are attentional attractors. This prediction was supported as well.

Perceptual cues clearly play a fundamental role in early categorization. Perceptual properties, however, are typically understood to be static shape cues. Some studies have suggested that dynamic perceptual cues, such as motion, may also be important in categorization. This study was an attempt to explore the role that motion plays in children's categorization of biological kinds as well as in more abstract concepts, such as geometric figures. Confronted with a choice between movement and shape, 4-year-old children were found to base their inductions about category membership primarily on motion cues, regardless of whether the objects were animals or geometric figures. This pattern of responses is also present in 7-year-olds for animals but not for geometric figures. Older children may begin to appreciate that motion is unique to animals and are therefore less likely to use motion cues to categorize geometric figures. The results support the view that children are initially guided by motion in categorization. Only as they grow older do they begin to constrain their inferences with respect to different motion cues. The present findings suggest that motion plays an overriding role that is central in the process of concept acquisition and in the mechanisms by which concepts are later structured.

This paper describes two research projects that study typical Situated Action tasks using traditional cognitive science methodologies. The two tasks are decision making in a complex production environment and interaction with an Automated Teller Machine (ATM). Both tasks require that the decision maker and the user search for knowledge in the environment in order to execute their tasks. The goal of these projects is to investigate the interaction between internal knowledge and dependence on external cues in these kinds of tasks. W e have used the classical expert-novice paradigm to study information search in the decision making task and cognitive modeling to predict the behavior of A T M users. The results of the first project strongly indicate that decision makers are forced to rely on environmental cues (knowledge in the environment) to make decisions, independently of their level of expertise. W e also found that performance and information search are radically different between experts and novices. Our explanation is that prior experience in dynamic decision tasks improves performance by changing informadon search behavior instead of inducing superior decision heuristics. In the second study w e describe a computer model, based on the Soar cognitive architecture, that learns part of the task of using an A T M machine. The task is performed using only the external cues available from the interface itself, and knowledge assumed of typical human users (e.g., how to read, how to push buttons). These projects suggest that tasks studied by Situated Action research pose interesting challenges for traditional symbolic theories. Extending symbolic theories to such tasks is an important step toward bridging these theoretical frameworks.

The idea that GOMS can be used to model HCI tasks within the organizational environment in which they occur is discussed and reviewed. An example in terms of satellite operations is provided.