Modeling Experimentally Induced Strategy Shifts

Abstract

In dynamic decision-making environments, observers must continuously adjust their decision-making strategies. Previous research has focused on internal fluctuations in decision mechanisms, without regard to how these changes are induced by environmental changes. We developed a simple paradigm in which we manipulated task difficulty, thereby inducing changes in decision processes. We applied this paradigm to recognition memory, manipulating task difficulty by changing the similarity of lures to targets. More difficult decision environments caused participants to make more careful decisions, but these changes did not appear immediately. We propose a simple theoretical account for these data, using a dynamic version of signal detection theory fitted to individual subjects. Our model represents a significant departure from existing models because it incorporates subject-controlled parameters that may adjust over time in response to environmental changes.