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Using Discrete Recurrence Quantification Analysis to Probe the Dynamics of Decision Making

Authors
Dr. Leslie Blaha
Air Force Office of Scientific Research
Abstract

In this talk, I will explore applications of the visual analytics method Recurrence Quantification Analysis (RQA) to choice sequences and other discrete behavior time series. Choice sequences are often examined as aggregate behavior statistics, like choice proportions, or proxy summary statistics, like points earned. But in the process of aggregation, much information about behavioral dynamics is lost. Yet, our descriptions of choice strategies, like “win-stay-lose-shift”, are statements about the behavioral dynamics; they suggest specific patterns that should be observed in the sequences. Auto-RQA helps us characterize individual sequences in ways that highlight important aspects of behavioral dynamics, such as short-range switching between options and longer time-scale adaptations or shifts in preferences, when present. Cross-RQA provides tools allowing us to compare observed behaviors to specific strategies. I will discuss implications of using RQA for model selection and to inform intelligent machines for adaptive decision aiding and human-autonomy teaming.

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Cite this as:

Blaha, L. (2020, November). Using Discrete Recurrence Quantification Analysis to Probe the Dynamics of Decision Making. Paper presented at MathPsych at Virtual Psychonomics 2020. Via mathpsych.org/presentation/317.