Exploring Dynamic Decision Making Strategies with Recurrence Quantification Analysis
- Erin McCormick, Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Leslie Blaha, 711th Human Performance Wing, Air Force Research Laboratory, Pittsburgh, Pennsylvania, United States
- Cleotilde Gonzalez, Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
AbstractAggregate statistics, such as percentage of choices, drive many insights about sequential behavior in decision making research. However, aggregation leaves usable information and potential insights unexamined. Here, we introduce the use of recurrence plots (RP) and recurrence quantification analysis (RQA) to explore individual choice sequences and determine generalized patterns of decision making strategies in a dynamic decision task. We illustrate the insights that RPs and RQAs reveal in a data set collected in a past study involving a dynamic, binary choice task (McCormick et al., in preparation). Patterns of recurrence reveal multiple, distinguishable, individual choice patterns among participants who were equally successful in adapting to the dynamic environment. We discuss how RQA of choice behavior can augment our understanding of decision strategies when paired with traditional aggregate assessments.