Abstract
(1) Background: In this research, we aimed to investigate a computational model of repetitive reaction time (RT) and virtual reaction time (VRT) testing. (2) Methods: The study involved 180 subjects (50 men, 130 women, mean age 31.61 ± 13.56 years). The data were statistically analyzed through the coefficient of variation (CV) and the Poincaré plot indicators. (3) Results: We obtained an excellent level of reliability for both sessions of testing and we put into evidence a relationship of association of the RT and VRT with the subjects’ age, which was more pregnant for RT (p < 0.05). For both RT and VRT data series, we determined a consistent closer association between CV and the Poincaré plot descriptors SD1, SD2 (SD—standard deviation), and the area of the fitting ellipse (AFE) (p < 0.01). We reported an underestimation of the time interval of 2 s during the VRT session of testing, with an average value of CV of VRT, the equivalent of the Weber fraction, of 15.21 ± 8.82%. (4) Conclusions: The present study provides novel evidence that linear and nonlinear analysis of RT and VRT variability during serial testing bring complementary insights to the understanding of complex neurocognitive processes implied in the task execution.
Highlights
As a complex neurocognitive function, time estimation is fundamental for human beings because it conditions the adaptative behavior in everyday life settings [1]
We reported an underestimation of the time interval of 2 s during the virtual reaction time (VRT) session of testing, with an average value of coefficient of variation (CV) of VRT, the equivalent of the Weber fraction, of 15.21 ± 8.82%. (4) Conclusions: The present study provides novel evidence that linear and nonlinear analysis of reaction time (RT) and VRT variability during serial testing bring complementary insights to the understanding of complex neurocognitive processes implied in the task execution
The statistical analysis was completed by applying the Poincaré plot method for the series of data, by determining the indicators that are needed: standard deviation (SD) 1, SD2, SD1/SD2, and area of the fitting ellipse (AFE), according to the following formulas [21,22,28,29]: observed in serial RT tasks [31], which can be managed by removing outliers [32]
Summary
As a complex neurocognitive function, time estimation is fundamental for human beings because it conditions the adaptative behavior in everyday life settings [1]. The process of time estimation has been intensively studied in animals and humans, but the results of this research are often controversial regarding the precision of different tasks and are interpreted in terms of multiple neuropsychological models, depending on the subject’s age, gender, and interval duration [2,3], type of sensorial stimuli applied [4] or contextual factors during testing [5]. Most studies put into evidence the existence of an internal clock, according to the Scalar Timing Theory, which states that the behavior of an animal is a function of the time since a stimulus began [6–8]. On the subject of the short time estimation, within a range of seconds, this process involves complex cognitive functions, which depend on multiple brain regions, a special role assigned to the short-term and working memory [9]. Some authors consider the WF as inconstant in multiple interval testing or during special conditions of testing
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