scholarly journals Development of Perceptual Inhibition in Adolescents—a Critical Period?

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 457
Author(s):  
Isabel María Introzzi ◽  
María Marta Richard’s ◽  
Yesica Aydmune ◽  
Eliana Vanesa Zamora ◽  
Florencia Stelzer ◽  
...  
Keyword(s):  
Response Time ◽  
Executive Functions ◽  
Critical Period ◽  
Search Task ◽  
Probability Distributions ◽  
Linear Trajectory ◽  
Gaussian Functions ◽  
Mean Response Time ◽  
The Mean ◽  
One Year

Recent studies suggest that the developmental curves in adolescence, related to the development of executive functions, could be fitted to a non-linear trajectory of development with progressions and retrogressions. Therefore, the present study proposes to analyze the pattern of development in Perceptual Inhibition (PI), considering all stages of adolescence (early, middle, and late) in intervals of one year. To this aim, we worked with a sample of 275 participants between 10 and 25 years, who performed a joint visual and search task (to measure PI). We have fitted ex-Gaussian functions to the probability distributions of the mean response time across the sample and performed a covariance analysis (ANCOVA). The results showed that the 10- to 13-year-old groups performed similarly in the task and differ from the 14- to 19-year-old participants. We found significant differences between the older group and all the rest of the groups. We discuss the important changes that can be observed in relation to the nonlinear trajectory of development that would show the PI during adolescence.

scholarly journals Development of Perceptual Inhibition in Adolescents, a Critical Period?

2021 ◽  
Author(s):  
Isabel Maria Introzzi ◽  
María Marta Richard’s ◽  
Yesica Aydmune ◽  
Eliana Vanesa Zamora ◽  
Florencia Stelzer ◽  
...  
Keyword(s):  
Response Time ◽  
Executive Functions ◽  
Critical Period ◽  
Search Task ◽  
Probability Distributions ◽  
Covariance Analysis ◽  
Linear Trajectory ◽  
Mean Response Time ◽  
The Mean ◽  
One Year

Recent studies suggest that the developmental curves in adolescence, related to the development of executive functions, could be fitted to a non-linear trajectory of development with progressions and retrogressions. Therefore, the present study proposes to analyze the pattern of development in Perceptual Inhibition (PI), considering all stages of adolescence (early, middle, and late) in intervals of one year. To this aim, we worked with a sample of 275 participants between 10 and 25 years, who performed a joint visual and search task (to measure PI). We have fitted exGaussian functions to the probability distributions of the mean response time across the sample and performed a covariance analysis (ANCOVA). The results showed that the 10- to 13-year-old groups performed similarly in the task and differ from the 14- to 19-year-old participants. We found significant differences between the older group and all the rest of the groups. We discuss the important changes that can be observed in relation to the nonlinear trajectory of development that would show the PI during adolescence.

Study of Industrial Model for Five-Input, Five-Stage Queueing Network

2016 ◽  
pp. 300-339
Author(s):  
Jitendra Kumar ◽  
Vikas Shinde
Keyword(s):  
Response Time ◽  
Textile Industry ◽  
Optimal Path ◽  
Queueing Network ◽  
Numerical Illustration ◽  
Mean Response Time ◽  
Final Destination ◽  
Optimal Capacity ◽  
Mean Waiting Time ◽  
The Mean

In this paper, we have developed an industrial model for textile industry with five-input, five-stage queueing network, wherein system receives orders from clients that are waiting to be served. The aim of this paper is to compute the optimal path that will provide the least response time for delivery of items to the final destination, through the five stages under queueing network. The mean number of items that can be delivered is minimum response time constitute the optimal capacity of the network. The last node in each stage of the network can be executed in the least possible response time. Various performance indices were carried out such as mean number of item in the system, mean number of item in queue, mean response time, mean waiting time. We have established the equivalent queueing network to analyze the various performance measures with numerical illustration and graph.

Response time of cardiac mitochondrial oxygen consumption to heart rate steps

1991 ◽  
Vol 260 (2) ◽  
pp. H613-H625 ◽  
Author(s):  
J. H. Van Beek ◽  
N. Westerhof
Keyword(s):  
Heart Rate ◽  
Response Time ◽  
Time Course ◽  
Mean Transit Time ◽  
Impulse Response Function ◽  
Heart Tissue ◽  
Experimental Information ◽  
O2 Consumption ◽  
Mean Response Time ◽  
The Mean

We investigated the time course of cardiac mitochondrial O2 consumption following steps in heart rate in 16 isolated rabbit hearts perfused with Tyrode solution. The time course was characterized by the mean response time, i.e., the first statistical moment (mean time) of the impulse response function. Like the mean transit time for an indicator, it provides an important characteristic of the response time course. The venous O2 content transients during steps in heart rate were measured and corrected for O2 diffusion and vascular transport using a mathematical model with experimental information derived from O2 washout following steps in arterial O2 concentration or perfusion flow. We deduce from these washout experiments that the effective O2 solubility in heart tissue is 86 +/- 13% (mean +/- SE) of solubility in water. The measured venous mean response time following a step in heart rate at 37 degrees C was 17.6 +/- 1.1 s. The mean response time of cardiac mitochondrial O2 consumption to changes in heart rate after correction for O2 transport was 7.7 +/- 0.7 s.

scholarly journals Individual Differences in Cerebral Blood Flow in Area 17 Predict the Time to Evaluate Visualized Letters

1996 ◽  
Vol 8 (1) ◽  
pp. 78-82 ◽  
Author(s):  
Stephen M. Kosslyn ◽  
William L. Thompson ◽  
Irene J. Kim ◽  
Scott L. Rauch ◽  
Nathaniel M. Alpert
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Individual Differences ◽  
Response Time ◽  
Parietal Lobe ◽  
Regions Of Interest ◽  
Area 17 ◽  
Mean Response Time ◽  
Shape Characteristic ◽  
The Mean

Sixteen subjects closed their eyes and visualized uppercase letters of the alphabet at two sizes, as small as possible or as large as possible while remaining “visible.” Subjects evaluated a shape characteristic of each letter (e.g., whether it has any curved lines), and responded as quickly as possible. Cerebral blood flow was normalized to the same value for each subject, and relative blood flow was computed for a set of regions of interest. The mean response time for each subject in the task was regressed onto the blood flow values. Blood flow in area 17 was negatively correlated with response time (r = -0.65), as was blood flow in area 19 (r = -0.66), whereas blood flow in the inferior parietal lobe was positively correlated with response time (r = 0.54). The first two effects persisted even when variance due to the other correlations was removed. These findings suggest that individual differences in the activation of specific brain loci are directly related to performance of tasks that rely on processing in those loci.

Mitochondrial dehydrogenase activity affects adaptation of cardiac oxygen consumption to demand

1993 ◽  
Vol 264 (2) ◽  
pp. H448-H453 ◽  
Author(s):  
J. B. Hak ◽  
J. H. Van Beek ◽  
M. H. Eijgelshoven ◽  
N. Westerhof
Keyword(s):  
Oxygen Consumption ◽  
Response Time ◽  
Dehydrogenase Activity ◽  
Calcium Uptake ◽  
High Energy ◽  
Mitochondrial Calcium ◽  
High Energy Phosphates ◽  
Mean Response Time ◽  
The Mean ◽  
Mitochondrial Calcium Uptake

The effect of regulation of mitochondrial dehydrogenase activities on the mean response time of mitochondrial oxygen consumption, which characterizes the delay between changes in ATP hydrolysis and changes in oxygen consumption, was investigated in isolated rabbit hearts and perfused with Tyrode solution at 28 degrees C. Perfusion with ruthenium red (RR) blocks mitochondrial calcium uptake and thus decreases mitochondrial dehydrogenase activities. Perfusion with pyruvate increases pyruvate dehydrogenase activity. The mean response time was 11.8 +/- 0.7 s (means +/- SE) during control, 12.2 +/- 1.2 s during perfusion with 0.9 microgram/ml RR, and 20.7 +/- 3.4 s during perfusion with 2.1 micrograms/ml RR. Blockade with 0.9 microgram/ml RR, which is presumably partial, did not slow the response, suggesting that mitochondrial calcium uptake may not be rate limiting. Strong blockade of mitochondrial calcium uptake increases the mean response time, presumably due to decreased calcium activation of the mitochondrial dehydrogenases. Perfusion with pyruvate significantly decreased the mean response time to 10.0 +/- 1.4 s compared with 11.9 +/- 0.7 s during perfusion with glucose. This decrease with pyruvate is not compatible with a shift to regulation by high-energy phosphates but may reflect increased mitochondrial oxidative capacity caused by increased NADH levels.

Designing for the Extremes: Modeling Drivers’ Response Time to Take Back Control From Automation Using Bayesian Quantile Regression

2019 ◽  
pp. 001872081989342 ◽  
Author(s):  
Azadeh DinparastDjadid ◽  
John D. Lee ◽  
Joshua Domeyer ◽  
Chris Schwarz ◽  
Timothy L. Brown ◽  
...  
Keyword(s):  
Response Time ◽  
Quantile Regression ◽  
Driving Simulator ◽  
Time Budget ◽  
Response Time Distribution ◽  
Event Type ◽  
Actual System ◽  
Bayesian Quantile Regression ◽  
Mean Response Time ◽  
The Mean

Objective Understanding the factors that affect drivers’ response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency. Background Drivers’ responses to takeover requests can be assessed using the time it takes the driver to take over control. However, all the takeover timing studies that we could find focused on the mean response time. Method A study using an advanced driving simulator evaluated the effect of takeover request timing, event type at the onset of a takeover, and visual demand on drivers’ response time. A mixed effects model was fit to the data using Bayesian quantile regression. Results Takeover request timing, event type that precipitated the takeover, and the visual demand all affect driver response time. These factors affected the 85th percentile differently than the median. This was most evident in the revealed stopped vehicle event and conditions with a longer time budget and scenes with lower visual demand. Conclusion Because the factors affect the quantiles of the distribution differently, a focus on the mean response can misrepresent actual system performance. The 85th percentile is an important performance metric because it reveals factors that contribute to delayed responses and potentially dangerous outcomes, and it also indicates how well the system accommodates differences between drivers.

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