Proceedings of the Annual Meeting of the Cognitive Science Society

A cornerstone of behavioral modeling and decision-making research includes characterizing the strategies people employ to make decisions. We often study strategies by collecting sequences of choices which hold considerable information about human cognition and behavior. However, it can be difficult to identify patterns and extract strategies, (e.g., `win-stay-lose-shift'), from noisy, raw sequence data. Consequently, many standard analytical approaches aggregate choice data (e.g., over time, across multiple participants, etc.). Unfortunately, this process obscures temporal patterns that may provide insight about strategies employed, strategy switches, or even adaptation of strategies over time. As illustrated by McCormick, Blaha, and Gonzalez (2020b), we can generate novel insights about decision making strategies by characterizing patterns over choice sequences with recurrence quantification analysis.