Temporal complexity measure of reaction time series: Operational versus event time

• Published in: Brain and behavior 2023-07, Vol.13 (7), p.e3069-n/a
• Main Authors: Mahmoodi, Korosh, Kerick, Scott E., Grigolini, Paolo, Franaszczuk, Piotr J., West, Bruce J.
• Format: Article
• Language: English
• Subjects: detrended fluctuation analysis; Estimates; Fourier transforms; Humans; Memory; Original; Reaction Time; reaction time series; temporal complexity; Time Factors; Time series; time‐stress
• ISSN: 2162-3279; 2162-3279
• DOI: 10.1002/brb3.3069

Introduction Detrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number. Methods Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go–NoGo shooting task that was performed by 30 participants under low and high time‐stress conditions in each of six repeated sessions over a 3‐week period. Results This new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time‐stress conditions and (2) predicting task performance outcomes. Conclusion We show that by changing from operational time to event time, the DFA allows discrimination of time‐stress conditions and predicts performance outcomes. We introduce a novel conceptual and analytical framework for estimating the complexity of short behavioral time series using Detrended Fluctuation Analysis (DFA). Analyzing the dataset based on a Go‐NoGo shooting task by this method, we found a clear classification of complexity indices between Low and High time‐stress conditions. We also found clear relations between complexity indices and errors of commission.