Effects of Automatic Detection on Dynamic Decision Making

Abstract

The usefulness of the dual-process theory of automaticity to dynamic decision-making tasks is unclear. Dynamic decision making is characterized by multiple, diverse, and interrelated decisions that are often constrained by time limitations and workload. We investigated the relevance of this theory in a compound task consisting of multiple, dynamic components. In the first experiment, we reproduced the original findings that shaped the theory in a dynamic visual search task. In the second experiment, we added a decision-making component. The results replicated the original findings and extended them to decision-making components. Working under consistent-mapping conditions in the visual search component led to more accurate decision making despite variability in the decision-making conditions. Likewise, working under varied-mapping conditions in the visual search component led to poorer decision making, particularly under high workload. The implications of these results to real-world dynamic tasks are discussed.