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

Abstract

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.

Complexity synchronization in living matter: a mini review

“…Consequently, the variability of fractal statistical fluctuations examined are the same at each level of magnification. Being fractal implies that an appparently continuous time trace such as an ECG or an EEG is actually controlled by a set of discrete statistical events generically called CETS as we have shown elsewhere ( Mahmoodi et al, 2023a ; Mahmoodi et al, 2023b ; West et al, 2023 ) and is briefly reviewed herein.…”

“…The complexity (scaling) of EEG time series data (64-channel electroencephalogram) were shown to be multifractal as were the respiratory and cardiovascular time series in addition to which the multifractal dimension (MFD) scaling of the three kinds of simultaneously measured datasets were shown to be in synchronization with one another ( Mahmoodi et al, 2023a ; Mahmoodi et al, 2023b ; West et al, 2023 ). The MFDs of these time series have also been identified using pairwise correlations between time series to identify an appropriate mechanism ( Bartsch and Ivanov, 2014 ).…”

“…In a recent series of papers Mahmoodi et al ( Mahmoodi et al, 2023a ; Mahmoodi et al, 2023b ) and West et al ( West et al, 2023 ) have used the scaling behavior of dynamically generated time series to hypothesize the existence of a new form of synchronization, called complexity synchronization (CS), having to do with the optimally efficient exchange of information between and among the organ-networks (ONs) within the human body viewed as a network-of-ONs (NoONs). This complexity synchronization (CS) involves the matching of the multifractal dimensions (MFDs) for time series and was identified by processing a 64-channel electroencephalogram (EEG) of the human brain with each channel treated as a distinct network, while the brain continuously interacts with the respiratory and cardiovascular networks.…”

Complexity Synchronization of Organ Networks

“…Herein, we show through the data processing of empirical time series that physiologic ONs generate crucial event time series, that is, the events have statistically independent time intervals and are therefore of the renewal type. In this paper, we focus on the empirical complexity of electroencephalographic (EEG) data being multifractal, as are the respiratory and cardiovascular time series, and establish that the three multifractal scalings are synchronous [3,5]. This remarkable synchrony among the three ONs' time series is the empirical evidence for the existence of complexity synchronization, as well as its fundamental importance in coordinating the functions of various ONs for the healthy operation of the human body.…”

“…The data processing approach revealing this new kind of synchronization is based on time series consisting of random discrete events whose statistics are of the renewal type and which enable the detection and quantification of synchrony among ONs operating on different time scales and not necessarily in stationary regimes [3]. The discrete events in such time series have been named crucial events [4] because they determine the efficiency of the information exchange between these complex ONs, and for a large class of complex networks, not necessarily only ONs, crucial events determine network failures-from heart attacks to stock market crashes.…”