ArticleBrain and behavior2023
Temporal complexity measure of reaction time series: Operational versus event time.
Article in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Principles Entailed by Complexity, Crucial Events, and Multifractal Dimensionality.Entropy (Basel, Switzerland) · 2025Article
- Complexity synchronization analysis of neurophysiological data: Theory and methods.Frontiers in network physiology · 2025Article
- How short video addiction affects risk decision-making behavior in college students based on fNIRS technology.Frontiers in human neuroscience · 2025Article
- Complexity synchronization in emergent intelligence.Scientific reports · 2024Article
- Complexity synchronization in living matter: a mini review.Frontiers in network physiology · 2024Review
- Multifractal foundations of biomarker discovery for heart disease and stroke.Scientific reports · 2023Article
- Complexity Synchronization of Organ Networks.Entropy (Basel, Switzerland) · 2023Article
- Temporal complexity measure of reaction time series: Operational versus event time.Brain and behavior · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDetrended 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.
methodsHerein 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.
resultsThis 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.
conclusionWe show that by changing from operational time to event time, the DFA allows discrimination of time-stress conditions and predicts performance outcomes.
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