This research investigates the impact of time pressure and individual differences on learning in a Real-Time Dynamic Decision Making (RTDDM) task. Our empirical results indicate that high time pressure generates high cognitive loads inhibiting learning. The results also show that high time pressure have a differential impact on the learning of individuals with high or low Working Memory (WM) capacity. W e present a cognitive model based on ACT-R intended to explain learning in tiiis task. Our cognitive model simulates learning by recognizing regularities in the decision task, and building "chunks" that guide decision making (instance-based learning). We describe how the model will be used to explain the impact of time pressure and WM capacity by varying the number of chunks acquired by the system given alternative time pressure conditions and individual differences.

This paper investigates working memory failure in menu driven Phone-Based Interaction (PBT). W e have used a computational model of Phone-Based Interaction (PBI USER ) to generate predictions about the impact of three factors on W M failure: PBI features (i.e., m e n u structure), individual differences (i.e., W M capacity) and task characteristics (i.e., task format and number of tasks). Our computational model is based on the theory of W M proposed by Just and Carpenter (1992). This theory stipulates that the storage and the processing of information generate demands for W M resources. Our empirical results provide strong evidence for the importance of storage demands, and moderate evidence for the importance of processing demands as predictors of W M failure in PBI. In addition, our results provide evidence for the importance of individual differences in W M capacity as a predictor of W M failure in PBI. Finally, our results indicate that, contrary to general guidelines for the design of PBI, deep menu hierarchies (no more than three options per menu ) do not reduce WM error ratesin PBI.

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.