Using Discrete Recurrence Quantification Analysis to Probe the Dynamics of Decision Making
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.
There is nothing here yet. Be the first to create a thread.