Situated Decision-Making and Recognition-Based Learning: Applying Symbolic Theories to Interactive Tasks

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. We have used the classical expert-novice paradigm to study information search in the decision making task and cognitive modeling to predict the behavior of ATM 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. We 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 information search behavior instead of inducing superior decision heuristics. In the second study we describe a computer model, based on the Soar cognitive architecture, that learns part of the task of using an ATM 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