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Randomized Controlled Trial
. 2009 Aug;16(4):742-51.
doi: 10.3758/PBR.16.4.742.

Sleep Deprivation Affects Multiple Distinct Cognitive Processes

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Free PMC article
Randomized Controlled Trial

Sleep Deprivation Affects Multiple Distinct Cognitive Processes

Roger Ratcliff et al. Psychon Bull Rev. .
Free PMC article

Abstract

Sleep deprivation adversely affects the ability to perform cognitive tasks, but theories range from predicting an overall decline in cognitive functioning (because of reduced stability in attentional networks) to claiming specific deficits in executive functions. In the present study, we measured the effects of sleep deprivation on a two-choice numerosity discrimination task. A diffusion model was used to decompose accuracy and response time distributions in order to produce estimates of distinct components of cognitive processing. The model assumes that, over time, noisy evidence from the task stimulus is accumulated to one of two decision criteria and that parameters governing this process can be extracted and interpreted in terms of distinct cognitive processes. The results showed that sleep deprivation affects multiple components of cognitive processing, ranging from stimulus processing to peripheral nondecision processes. Thus, sleep deprivation appears to have wide-ranging effects: Reduced attentional arousal and impaired central processing combine to produce an overall decline in cognitive functioning.

Figures

Figure 1
Figure 1
Accuracy and mean response time (RT), as a function of numerosity category for the three sessions and two participant groups. Note that mean error RTs for the two extreme numerosity categories are not plotted; there were some participants with zero error responses in those categories.
Figure 2
Figure 2
Illustration of the diffusion model with starting point z, boundary separation a, and drift rate v. Three sample paths are shown, illustrating variability within the decision process, and correct and error response time (RT) distributions are illustrated.
Figure 3
Figure 3
Response time (RT) quantiles and response proportions. Panel A shows an RT distribution histogram (the circles joined by lines), with the quantile RTs identified on the abscissa and as rectangles with .2 area between the .1, .3, .5 (median), .7, and .9 quantiles and .095 area between extremes, the .005 and .1 quantiles and the .9 and .995 quantiles (we use .005 and .995 for illustration as being a little less variable than the maximum and minimum). Panel A shows that the six rectangles between and outside the five quantiles approximate the density function rather well. Panel B shows quantile probability functions for “large” and “small” responses for the three sessions in the experimental group (Session 1 baseline, Session 2 sleep-deprived, and Session 3 recovery) and in the control group. The quantile RTs in each vertical line of Xs from the bottom to the top are the .1, .3, .5 (median), .7, and .9 quantiles, respectively. The Xs represent the data, and the os joined with lines represent the predicted quantile RTs from the diffusion model. The eight columns of Xs in each graph are for eight different stimulus categories—namely, 31–35, 36–40, 41–45, 46–50, 51–55, 56–60, 61–65, and 66–70 asterisks for “large” responses; for “small” responses, the columns of Xs are for the same eight stimulus categories, but in the opposite order. The horizontal position of the columns of Xs indicates the response proportion for that category. Note that extreme error quantiles could not be computed for some of the numerosity categories because there were too few errors for some participants, so only the median RT value is plotted.
Figure 4
Figure 4
Plots of the diffusion model parameters averaged over participants for the three sessions in the experimental (sleep-deprived) group (1) and the control group (2). Sleep deprivation in Session 2 produced significant effects of boundary separation, the proportion of contaminants, the three drift rates, starting point variability across trials, and nondecision variability across trials. The effects on the nondecision component and variability in drift rate across trials were not significant.
Figure 5
Figure 5
Response time (RT) distributions for the participant with the most estimated contaminants. Five numerosity categories are grouped (see the text). The left panel shows the predicted RT distribution with the contaminant assumption of uniform random guesses, and the right panel shows the predicted RT distribution without the contaminant assumption, but with the same parameter values as in the left panel. The assumption of uniform random guesses produces an RT distribution tail that adequately describes the data.

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