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

Area of Science:

• Complex Systems Analysis
• Time Series Analysis
• Cognitive Psychology

Background:

• Detrended fluctuation analysis (DFA) is a standard method for assessing scaling indices in time series data.
• Previous applications of DFA have focused on reaction time series represented against trial number (operational time).

Purpose of the Study:

• To introduce a novel approach using DFA by treating reaction times as event durations (temporal time).
• To evaluate the efficacy of this event-time DFA in differentiating stress conditions and predicting performance.

Main Methods:

• The detrended fluctuation analysis (DFA) algorithm was applied to reaction time series represented in event (temporal) time.
• Data comprised reaction times from a Go-NoGo shooting task under low and high time-stress conditions across multiple sessions.

Main Results:

• The event-time DFA approach yielded quantitatively superior results in distinguishing between low and high time-stress conditions.
• This method also demonstrated improved prediction of task performance outcomes compared to traditional operational-time DFA.

Conclusions:

• Transforming reaction time data from operational to event time significantly enhances DFA's ability to discriminate stress levels.
• This revised DFA methodology provides a more effective tool for predicting task performance in complex dynamic systems.