Sleep deprivation may be undermining teen health

Lack of sufficient sleep--a rampant problem among teens--appears to put adolescents at risk for cognitive and emotional difficulties, poor school performance, accidents and psychopathology, research suggests.

By SIRI CARPENTER

Monitor Staff

October 2001, Vol 32, No. 9

Print version: page 42

On any given school day, teen-agers across the nation stumble out of bed and prepare for the day. For most, the

alarm clock buzzes by 6:30 a.m., a scant seven hours after they went to bed. Many students board the school bus

before 7 a.m. and are in class by 7:30.

In adults, such meager sleep allowances are known to affect day-to-day functioning in myriad ways. In adolescents,

who are biologically driven to sleep longer and later than adults do, the effects of insufficient sleep are likely to be

even more dramatic--so much so that some sleep experts contend that the nation's early high-school start times,

increasingly common, are tantamount to abuse.

"Almost all teen-agers, as they reach puberty, become walking zombies because they are getting far too little sleep,"

comments Cornell University psychologist James B. Maas, PhD, one of the nation's leading sleep experts.

There can be little question that sleep deprivation has negative effects on adolescents. According to the National

Highway Traffic Safety Administration, for example, drowsiness and fatigue cause more than 100,000 traffic

accidents each year--and young drivers are at the wheel in more than half of these crashes.

Insufficient sleep has also been shown to cause difficulties in school, including disciplinary problems, sleepiness in

class and poor concentration.

"What good does it do to try to educate teen-agers so early in the morning?" asks Maas. "You can be giving the most

stimulating, interesting lectures to sleep-deprived kids early in the morning or right after lunch, when they're at their

sleepiest, and the overwhelming drive to sleep replaces any chance of alertness, cognition, memory or

understanding."

Recent research has also revealed an association between sleep deprivation and poorer grades. In a 1998 survey of

more than 3,000 high-school students, for example, psychologists Amy R. Wolfson, PhD, of the College of the Holy

Cross, and Mary A. Carskadon, PhD, of Brown University Medical School, found that students who reported that they

were getting C's, D's and F's in school obtained about 25 minutes less sleep and went to bed about 40 minutes later

than students who reported they were getting A's and B's.

In August, researchers at the University of Minnesota reported the results of a study of more than 7,000 high-school

students whose school district had switched in 1997 from a 7:15 a.m. start time to an 8:40 a.m. start time. Compared

with students whose schools maintained earlier start times, students with later starts reported getting more sleep on

school nights, being less sleepy during the day, getting slightly higher grades and experiencing fewer depressive

feelings and behaviors.

Also troubling are findings that adolescent sleep difficulties are often associated with psychopathologies such as

depression and attention deficit hyperactivity disorder (ADHD).

This research, combined with studies showing widespread sleep deprivation among teens, has propelled efforts to

educate children and adults about the importance of a good night's sleep and to persuade schools to push back high-

school starting times.

"There is substantial evidence that the lack of sleep can cause accidents, imperil students' grades and lead to or

exacerbate emotional problems," says U.S. Rep. Zoe Lofgren (D-Calif.), who has introduced a bill that would provide

federal grants to help school districts defray the cost of pushing back school starting times. Adjusting school

schedules, Lofgren says, "could do more to improve education and reduce teen accidents and crime than many more

expensive initiatives."

The research has also spurred further investigations into why teens need extra sleep, the effects of sleep deprivation

on cognition, emotion regulation and psychopathology, and the long-term consequences of chronic sleep deprivation.

Dogma reversed

For decades, experts believed that people require less sleep as they move from infancy through adulthood.

It's easy to see why this belief persisted: Adolescents sleep less than they did as children, declining from an average

of 10 hours a night during middle childhood to fewer than 7.5 hours by age 16. According to Wolfson and

Carskadon's 1998 study, 26 percent of high school students routinely sleep less than 6.5 hours on school nights, and

only 15 percent sleep 8.5 hours or more. The same study indicated that to make up for lost sleep, most teens snooze

an extra couple of hours on weekend mornings--a habit that can lead to poorer-quality sleep.

But to researchers' surprise, in the past two decades studies have shown that teen-agers require considerably more

sleep to perform optimally than do younger children or adults. Starting around the beginning of puberty and

continuing into their early 20s, Carskadon and colleagues have shown, adolescents need about 9.2 hours of sleep

each night, compared with the 7.5 to 8 hours that adults need.

In addition to needing more sleep, adolescents experience a "phase shift" during puberty, falling asleep later at night

than do younger children. Researchers long assumed that this shift was driven by psychosocial factors such as social

activities, academic pressures, evening jobs and television and Internet use. In the past several years, however,

sleep experts have learned that biology also plays a starring role in adolescents' changing sleep patterns, says

Carskadon.

Indeed, Carskadon's research is greatly responsible for that new understanding. In a pair of groundbreaking studies

published in 1993 and 1997, she and colleagues found that more physically mature girls preferred activities later in

the day than did less mature girls, and that in more physically mature teens, melatonin production tapered off later

than it did in less mature teens. Those findings, Carskadon says, suggest that the brain's circadian timing system--

controlled mainly by melatonin--switches on later at night as pubertal development progresses.

Changes in adolescents' circadian timing system, combined with external pressures such as the need to awaken

early in the morning for school, produce a potentially destructive pattern of early-morning sleepiness in teen-agers,

Carskadon argues. In a laboratory study of 40 high-school students published in the journal Sleep (Vol. 21, No. 8) in

1998, she, Wolfson and colleagues examined the effect of changing school starting times from 8:25 a.m. to 7:20 a.m.

Their results were disturbing: Almost half of the students who began school at 7:20 were "pathologically sleepy" at

8:30, falling directly into REM sleep in an average of only 3.4 minutes--a pattern similar to what is seen in patients

with narcolepsy.

Those findings, says Carskadon, persuaded her that "these early school start times are just abusive. These kids may

be up and at school at 8:30, but I'm convinced their brains are back on the pillow at home."

Elusive questions

The evidence of adolescents' increased need for sleep and that many--if not most--teen-agers are chronically sleep

deprived has raised further questions. Particularly elusive, says Carskadon, has been the question of why

adolescents' circadian clocks shift to a later phase around the beginning of puberty.

One possibility, she believes, is that the brain's sensitivity to light changes during adolescence. At the annual meeting

of the Associated Professional Sleep Societies in June, she and colleagues presented research showing that in the

evening, exposure to even very dim lighting delayed melatonin secretion for participants who were in middle or late

puberty, but not for prepubertal participants.

Carskadon is also interested in how teen-age alcohol use might affect the brain's sleep system. Following up on

studies in adults that have established a link between drinking problems and changes in sleep patterns, for example,

she and her colleagues plan to examine whether during early development, young people with a family history of

problem drinking might have abnormalities in the brain mechanisms that govern sleep.

Just as important as the question of why sleep patterns change during adolescence is the issue of how sleep

deprivation influences adolescents' emotion regulation and behavior. Many researchers have noted that sleep-

deprived teen-agers appear to be especially vulnerable to psychopathologies such as depression and ADHD, and to

have difficulty controlling their emotions and impulses.

Although it's difficult to untangle cause and effect, it's likely that sleep deprivation and problems controlling impulses

and emotions exacerbate one another, leading to a "negative spiral" of fatigue and sleepiness, labile emotions, poor

decision-making and risky behavior, says Ronald E. Dahl, MD, a professor of psychiatry and pediatrics at the

University of Pittsburgh.

Despite the evidence that insufficient sleep affects young people's thinking, emotional balance and behavior, the

long-term effects of chronic sleep deprivation on learning, emotion, social relationships and health remain uncertain.

"There's a real need for longitudinal studies to follow through later childhood and adulthood," says psychologist Avi

Sadeh, PhD, a sleep researcher at Tel Aviv University. Although research has amply demonstrated that sleep

problems affect young people's cognitive skills, behavior and temperament in the short term, he says, "It's not at all

clear to what extent these effects are long-lasting."

Researchers push for school changes, public outreach

With such a wealth of evidence about the prevalence of adolescent sleep deprivation and the risks it poses, many

sleep researchers have become involved in efforts to persuade school districts to push back high-school starting

times so that teens can get their needed rest.

Some schools argue that adjusting school schedules is too expensive and complicated. But others have responded

positively to sleep experts' pleas. The Connecticut legislature is considering a bill that would prohibit public schools

from starting before 8:30 a.m., and Massachusetts lawmakers are also weighing the issue. And Lofgren's "Zzzzz's to

A's" bill, first introduced in the U.S. House of Representatives in 1998, would provide federal grants of up to $25,000

to school districts to help cover the administrative costs of adjusting school start times.

These efforts are a move in the right direction, says Wolfson. But, she says, changing school start times isn't the

entire answer. "I think we have to be educating children, parents and teachers about the importance of sleep, just as

we educate them about exercise, nutrition and drug and alcohol use."

Toward that end, several public-education efforts are now under way:

With a grant from the Simmons mattress company, Cornell's Maas recently produced a film on teen-age

sleep deprivation, its consequences and the "golden rules" for healthy sleep. The film is scheduled for

distribution through parent-teacher associations and school principals this fall. In August, Maas also

published a children's book, "Remmy and the Brain Train," which discusses why the brain requires a good

night's sleep.

Next year, the National Center for Sleep Disorders Research at the National Institutes of Health plans to

release a supplemental sleep curriculum for 10th-grade biology classes, addressing the biology of sleep, the

consequences of insufficient sleep and the major sleep disorders. In a related effort, the center is

coordinating a sleep-education campaign aimed at 7- to 11-year-olds.

Wolfson and colleague Christine A. Marco, PhD, a psychologist at Worcester State College, are pilot-testing

an eight-week sleep curriculum for middle-school students. As part of the curriculum, students keep sleep

diaries, play creative games and participate in role-playing about sleep, and set goals--for example, for the

amount of sleep they want to get or for regulating their caffeine intake. Preliminary results indicate that the

curriculum helps students improve their sleep habits.

"Changing school start times is one critical measure we can take to protect young people's sleep," says Wolfson.

"And then, if we can only understand what's going on with sleep in these sixth-, seventh- and eighth-graders, we can

intervene to change their sleep behavior before it gets out of hand."

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Sleep deprivation can look like ADHDJason WilliamsPsychology Today; Aug 2003; 36, 4; ProQuest Psychology Journalspg. 23

Impact of daytime sleepiness underratedSharpley, AnnThe Lancet; Jul 13, 1996; 348, 9020; ProQuest Psychology Journalspg. 71

© 2009 The Psychonomic Society, Inc. 742

Sleep deprivation is a manipulation that allows arousal levels to be manipulated to a higher degree than one sees in the experimental interventions typically used in cogni-tive research. The effects of acute sleep deprivation on cognition are reversible, so sleep deprivation represents a different approach for investigating the underlying pro-cesses of cognition. Conversely, carefully selected tests of cognitive processing can provide new information about the effects of sleep deprivation on the brain.

Experimental and modeling studies of the effects of sleep deprivation on cognitive performance have thus far focused almost exclusively on global outcome measures (Durmer & Dinges, 2005; Van Dongen, 2004). Such re-search has revealed that overall performance declines as a function of time spent awake, modulated by circadian rhythm (Van Dongen & Dinges, 2005). However, not much is known about the effects of sleep deprivation on detailed performance outcomes, such as response time (RT) distri-butions, or on changes in specific cognitive components, such as decision processes. Attempts are being made to bridge this gap with the use of computational modeling that is based on cognitive architectures (Gunzelmann, Gluck, Price, Van Dongen, & Dinges, 2007), but these efforts need to be informed by precise information regard-ing which component processes of cognition are affected by sleep deprivation and how.

The present study addressed this issue. Participants were tested on a two-choice numerosity discrimination task at baseline after 57 h of sleep deprivation and again

following 2 days with recovery sleep. Participants in a control group were tested at the same times, but without sleep deprivation. In each trial of the two-choice task, be-tween 31 and 70 asterisks were placed in random positions in a 10 3 10 array, and the participants were instructed to judge whether the number was greater than 50 (large) or less than 50 (small ). This task was selected because it has few memory demands or perceptual limitations (such as brief presentation or low contrast), allowing us to focus on central cognitive processes and decision processes.

We applied the diffusion model (Ratcliff, 1978, 1988; Ratcliff, Cherian, & Segraves, 2003; Ratcliff & Mc Koon, 2008; Ratcliff & Smith, 2004; Ratcliff, Van Zandt, & McKoon, 1999; Smith, 2000; Smith, Ratcliff, & Wolf-gang, 2004; Voss, Rothermund, & Voss, 2004) to the data from each individual participant. From this, we were able to obtain estimates of the model parameter values for the baseline, deprivation, and recovery sessions in the experi-mental group, as well as for those in the control group. These parameter estimates allowed us to draw conclusions about the effects of sleep deprivation on components of cognitive processing.

Method

ParticipantsA total of 25 participants (13 women; age range, 22–38 years)

completed the study. The participants were screened with physical examinations, urine and blood chemistry tests, and questionnaires to ensure they were physically and psychologically healthy and free

Sleep deprivation affects multiple distinct cognitive processes

RogeR RatcliffOhio State University, Columbus, Ohio

and

Hans P. a. Van dongenWashington State University, Spokane, Washington

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 distribu-tions 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 pe-ripheral 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.

Psychonomic Bulletin & Review2009, 16 (4), 742-751doi:10.3758/PBR.16.4.742

R. Ratcliff, ratcliff.22@osu.edu

Sleep Deprivation effectS on cognitive proceSSeS 743

eliminated. The left-hand panels show the proportion of “large” responses as a function of eight numerosity cat-egories representing eight groups of numbers of asterisks (31–35, 36–40, 41–45, 46–50, 51–55, 56–60, 61–65, 66–70), and the right-hand panels show mean RT for “large” and “small” responses, as a function of the number of asterisks. Response proportions were very similar for the experimental and control groups in the baseline and recovery sessions; but in the sleep-deprivation session, the experimental participants showed a loss of accuracy, as compared with the control participants’ accuracy lev-els. In the baseline session, RTs from the experimental and control groups were similar. In the sleep-deprivation session, the experimental group exhibited mean RTs over 200 msec longer than those exhibited by the control group. In the recovery session, the experimental group had slightly slower responses than those by the control group, but performance was almost back to baseline levels. These results are consistent with the documented overall effects of sleep deprivation on cognitive performance (Durmer & Dinges, 2005; Van Dongen & Dinges, 2005).

diffusion ModelThe diffusion model is designed to explain the cogni-

tive processes involved in making simple two-choice deci-sions. The model separates the quality of evidence enter-ing a decision from the decision criteria, as well as from nondecision processes. Decisions are assumed to be made by a noisy process that accumulates information over time, from a starting point z toward one of two response criteria, or boundaries: a and 0 (Figure 2). When a boundary is reached, a response is initiated. The rate of accumulation of information is called the drift rate (v), and it is deter-mined by the quality of the information extracted from the stimulus in perceptual tasks and the quality of match between the test item and memory in lexical decision and memory tasks. The mean of the distribution of times taken up by the nondecision component (the combination of en-coding, response execution, etc.) is labeled Ter. Within-trial variability (Gaussian-distributed noise) in the accu-mulation of information from the starting point toward the boundaries results in processes with the same mean drift rate terminating at different times (producing RT distribu-tions) and, sometimes, terminating at the wrong boundary (producing errors).

The values of the components of processing are as-sumed to vary from trial to trial, under the assumption that participants cannot accurately set the same param-eter values from one trial to another (e.g., Laming, 1968; Ratcliff, 1978). Across-trial variability in drift rate is normally distributed with SD h, across-trial variability in starting point is uniformly distributed with range sz, and across-trial variability in the nondecision component is uniformly distributed with range st. This across-trial vari-ability allows the model to fit the relative speeds of correct and error responses (Ratcliff et al., 1999). In signal detec-tion theory, which deals only with accuracy, all sources of across-trial variability would be collapsed onto one parameter—namely, variability in evidence across trials. In contrast, in diffusion model fitting, the separate sources

of traces of drugs. They were good sleepers (getting between 6 and 10 hours per night), reported themselves as being neither extreme morning nor extreme evening types, and had no sleep or circadian disorder (including normal baseline polysomnogram). They had not traveled between time zones in the prior 3 months and had not worked shifts in the prior month. The participants were required to maintain their habitual sleep schedule in the week before the study, as monitored by sleep/wake logs, time-stamped voice recordings of bedtimes and rising times, and wrist actigraphy (wrist-worn activ-ity monitoring). They had normal or corrected-to-normal vision. The study was approved by the Institutional Review Board of Wash-ington State University, and all of the participants gave written in-formed consent.

designThe participants were in the lab for 6 consecutive nights (7 days).

They were randomized to either a sleep-deprivation condition (12 participants) or a control condition (13 participants). On Days 1 and 2, all of the participants had baseline sleep (10 h in bed each night). They practiced the numerosity discrimination task for 10 min twice on Day 1. On Day 3, participants in the experimental condition began 62 h of continuous wakefulness. At 17:00 on that day, while 9 h awake, they took their baseline test. Two days later, at 17:00, while 57 h awake (48 h after Test 1), they took their second test. After the 62 h of wakefulness, participants were allowed 2 recovery nights (10 h in bed each night). At 17:00 on the last day (48 h after Test 2), they took their recovery test. The control participants were tested at the same times but had sleep (10 h in bed) each night throughout the study. Participants continually stayed inside the isolated, environ-mentally controlled laboratory during the study and were behavior-ally monitored at all times by trained research assistants.

ProcedureCognitive performance was tested on a two-choice numerosity

discrimination task. On each trial, a number of asterisks between 31 and 70 was generated and placed in random positions in a 10 3 10 array of blank characters in the upper left-hand corner of an LCD monitor (from a viewing distance of 57 cm, the width of the asterisk array was 4º, and the height was 9º). Varying the number of asterisks causes performance accuracy to vary from near-ceiling (100% ac-curate) to near-floor (50% accurate) levels, providing a full range of values for this dependent variable for the purpose of modeling. They were clearly visible, light characters presented against a dark background. The participants were asked to press the “/” key if the number of displayed asterisks was judged to be large and the “z” key if the number was judged to be small. Each test array remained on the screen until a response was made. “Small” responses to ar-rays of 31 to 50 asterisks and “large” responses to arrays of 51 to 70 asterisks were counted as correct. Feedback on the accuracy of a response was provided by displaying the word “error” or “correct,” as appropriate, on the screen for 500 msec after each response was made. There were 30 blocks of 40 trials (all the numbers of asterisks, from 31 to 70) per test session. The participants were instructed to respond quickly and accurately, but not so quickly that they would hit the wrong key. The participants were also informed that they could take a break between blocks of trials. Examples of large and small numbers of asterisks were shown before the session to provide participants with a reference.

ReSultS

Summaries of the accuracy and RT data for the experi-mental and control groups for the three sessions are shown in Figure 1. Responses from the first block of each ses-sion, short (,280 msec) and long (.5,000 msec) outlier RTs in all blocks (less than 1.7% of the data in the sleep-deprivation session and less than 0.2% of the data for the other sessions), and the first response in each block were

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Sleep Deprivation effectS on cognitive proceSSeS 745

were kept constant across numerosity categories in each session, because it is routinely assumed that participants cannot change their decision criteria or adjust the dura-tion of other components of processing as a function of the quality of the stimulus being presented. All of the pa-rameters of the model were allowed to vary between ses-sions, because practice could alter any of the parameters. The model can be seen as decomposing accuracy and RT data for correct and error responses into distinct cognitive processes.

Fits of the diffusion ModelTo display the data and the diffusion model fits, we com-

puted the average over participants for quantile RTs (see Figure 3A) and for the proportion of “large” and “small” responses as a function of the numerosity categories. We also averaged the parameter estimates over participants and computed the corresponding model predictions; we then generated the quantile probability functions. To display the quality of fit of the model to the data, Figure 3B shows the observed and predicted quantile RTs (vertical) and the proportion of “large” and “small” responses (horizon-tal), as a function of the number of asterisks (numerosity categories) presented. These graphs contain information about all of the data and model predictions of the experi-ment: the probabilities of correct and error responses and the shapes of the RT distributions for both correct and error responses. As Figure 3B shows, the probability of a “large” response varied from near 1 for stimuli with large numbers of asterisks to near 0 for stimuli with small numbers of asterisks (cf. Figure 1). Median RT increased for the more difficult stimuli (numbers of asterisks close to 50), with most of the slowing coming from the skewing of the RT distributions, because the .9 quantile RTs increased much more than did the .1 quantile RTs.

Parameter estimatesThe average model parameter estimates are shown in

Table 1 and plotted in Figure 4, as a function of session and experimental versus control groups. For the sleep-deprivation session, relative to the baseline and recovery sessions and to the control group, boundary separation in-creased, drift rates decreased, variability in starting point and nondecision components increased, and the propor-tion of contaminants increased.

The parameter values represent the behavior of com-ponents of processing in the experiment, and we used their values to interpret the effects of sleep deprivation on performance in the two-choice task. Two-way mixed ef-fects ANOVAs of each of the parameters for the three ses-sions 3 experimental and control groups were performed; the results are shown in the Table 2 note. Table 2 shows planned statistical comparisons between the experimental group and the control group, as well as between the sleep-deprivation session and the baseline and recovery sessions. For the baseline and recovery sessions, there were almost no significant differences between the experimental and control groups. For all comparisons between the sleep-deprivation session and the corresponding control session and for all comparisons between the sleep-deprivation

of across-trial variability are identified. Thus, if simulated data are fit by the model, then, for example, variability in drift rate is not incorrectly recovered as variability in starting point (Ratcliff & Tuerlinckx, 2002). The success of parameter identifiability comes partly from the require-ment that the model is fit to both the correct and the error RT distributions, which provide very tight constraints on the model (see Ratcliff, 2002).

In almost all RT studies (e.g., Ratcliff, 1979, 1993), some proportion of responses are spurious contaminants, which have previously been explicitly modeled in ap-plications of the diffusion model (Ratcliff & Tuerlinckx, 2002) as random delays in processing. Thus, predicted RTs are mixtures of pure diffusion model processes and diffusion model processes with a delay added (usually 0%–2%), which means that contaminant processes are just as accurate as processes without contaminants. In this application, we used the simplest alternative assump-tion we could by representing contaminants as random guesses (Vandekerck hove & Tuerlinckx, 2007) that were uniformly randomly distributed over the range from the shortest to the longest RT for each numerosity category. Thus, the predicted RT distribution is a probability mix-ture of diffusion model processes and random guesses. Random guesses can be distinguished from the assump-tion of an added random delay because random guesses reduce accuracy, as was shown in the sleep-deprivation data. Unlike the assumption of random delays added to diffusion model RTs, the assumption of random guesses means that these processes are outside the diffusion model processing assumptions. Although it may seem that this assumption provides a lot of model freedom, no additional model parameters are added beyond the proportion of contaminants, and the ability of the mixture to account for RT distributions (discussed below) supports the assump-tion. Note that recovery of diffusion model parameters is reasonably robust to the assumed form of the contami-nant distribution; for example, if exponentially distributed contaminants are simulated and the recovery program as-sumes uniformly distributed contaminants, the model pa-rameters are recovered well (Ratcliff, 2008).

The diffusion model was fit to the accuracy and RT distributions using a standard chi-square method (Ratcliff & Tuerlinckx, 2002). The values of all of the parameters, including the variability parameters, are estimated simul-taneously from the data by fitting the model to all of the conditions of an experiment. It was assumed that “large” responses to large stimuli were symmetric with “small” responses to small stimuli; that is, drift rates were equal, but with opposite signs. Thus, there were four distinct drift-rate parameters (v1–v4) for the eight numerosity cat-egories. However, participants can have a bias in the zero point of drift, so a drift criterion, vc (analogous to the cri-terion in signal detection theory), was added to each drift rate (Ratcliff & McKoon, 2008). Because the values for the drift rate parameter for the two middle, most difficult numerosity categories (i.e., v4, for 46–50 and 51–55 as-terisks) were small and variable, they were not used in our statistical analyses; but they did help constrain the model fits. All of the parameters of the model (except drift rate)

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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 quan-tiles 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.

Sleep Deprivation effectS on cognitive proceSSeS 747

table 1 Parameter Values Averaged over Participants

Session a Ter h sz v1 v2 v3 v4 po st vc z χ2

Experimental Group Baseline .151 .428 .171 .066 .546 .416 .271 .093 .003 .138 .007 .075 101 Sleep-deprived .189 .401 .216 .130 .403 .318 .198 .083 .101 .165 .011 .089 163 Recovery .151 .420 .209 .083 .615 .491 .308 .110 .001 .113 .005 .076 101Control Group Baseline .152 .428 .179 .069 .558 .452 .300 .097 .007 .139 .004 .073 88 Control .136 .426 .165 .061 .549 .453 .294 .099 .012 .127 .005 .064 79 Recovery .132 .412 .156 .061 .579 .456 .311 .100 .014 .119 2.002 .064 81

Note—a, upper boundary of accumulation of information over time; Ter, mean of the distribution of times taken up by the non decision component, in seconds. h, sz, and st represent across-trial variability in drift rate, in starting point, and in the nondecision component, respectively. v1, v2, v3, and v4 represent the four distinct drift-rate parameters for the eight numerosity categories (see the text for details). po, proportion of contaminants; vc, drift criterion; z, starting point of accumulation of information over time. χ2 represents the goodness-of-fit index with critical value 102 for 76 degrees of freedom. For a total of k numerosity categories and a model with m parameters, the degrees of freedom are k(12 2 1) 2 m, where 12 comes from the number of bins between and outside the response time quantiles for correct and error responses for a single category (minus 1, because the total probability mass must be 1). There were k 5 8 numerosity categories in the experiment and m 5 12 parameters in the model, so df 5 88 2 12 5 76.

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748 ratcliff anD van Dongen

stimulus, but were random guesses (contaminants). How-ever, there were large individual differences.

Individual differencesMost of the participants in the experimental group

showed a degradation in the components of cognitive pro-cessing from the baseline session to the sleep-deprivation session and a rebound in the recovery session. Boundary separation was higher in the sleep-deprivation session for 9 of 12 participants, and the proportion of contaminants was larger for 11 of 12 participants. Most of the participants in the sleep-deprivation group showed a decrease in drift rate from the baseline session to the sleep- deprivation ses-sion and an increase to the recovery session. These results show that the main conclusions apply to most individuals in the study and are not the result of there having been just a few participants showing a large effect and others show-ing no effect. That said, systematic individual differences in the response to sleep deprivation were observed, as has been documented before (Van Dongen, Baynard, Maislin, & Dinges, 2004).

Contaminant ResponsesThe effect of sleep deprivation on the proportion of

contaminants indicates that sleep-deprived participants produced more responses that were not based on the stim-ulus, but were random guesses. However, there were large individual differences: In the sleep-deprivation session, 5 participants had under 4% contaminants, 5 had between 8% and 11% contaminants, and 2 participants had over 30% contaminants. This high estimate of the proportion of contaminants for those 2 participants was corroborated by accuracy values in the highest accuracy categories of around 70% for the sleep-deprivation session and over 95% in the baseline and recovery sessions. In the baseline and recovery sessions, none of the participants in the ex-perimental group had over 3% contaminants. These obser-vations are, again, consistent with previously documented individual differences in cognitive responses to sleep dep-rivation (Van Dongen et al., 2004).

session and the baseline and recovery sessions in the ex-perimental group, there were significant differences for the following: boundary separation (one comparison was marginally significant), variability in starting point across trials, drift rates for the easier numerosity categories, pro-portion of contaminants, and variability in the duration of nondecision processes (one comparison was marginally significant). Although these comparisons involved mul-tiple t tests, the results are consistent and clear. To sum-marize, the estimates for a, sz, v1, v2, v3, po, and st from the sleep-deprivation session are different from the estimates for the baseline and recovery sessions, as well as from all of the sessions in the control group.

The differences in the parameter estimates led to the following interpretation of the effects of sleep depriva-tion on the two-choice numerosity discrimination task. When deprived of sleep, most participants adopted mod-erately more conservative decision criteria (larger bound-ary separation), thus requiring more evidence for making a response. However, this was not a large difference, as compared to the effect that was seen with speed–accuracy manipulations (Ratcliff, Thapar, & McKoon, 2001). Vari-ability in starting point and in the nondecision compo-nents of cognition became larger with sleep deprivation. The former suggested that participants experienced dif-ficulty resetting the criteria to the normal range of val-ues observed in the nondeprivation sessions. The latter indicated that participants were more unstable in stimulus detection and/or more variable in executing the decision process.

The mean of drift rates v1, v2, and v3 for the most ac-curate numerosity categories decreased from .411 for the baseline session to .307 for the sleep-deprivation session, and then increased again to .438 for the recovery session. The change induced by sleep deprivation was substantial, suggesting that central information processing was af-fected by sleep deprivation—specifically, the cognitive processes that extracted an estimate of the relative nu-merosity of the asterisk array. Sleep-deprived participants also produced more responses that were not based on the

table 2 p Values for Planned Comparisons of Parameters

Comparison of Sessions a Ter h sz v1 v2 v3 v4 po st vc z

Session 1 Experimental vs. control .961 .997 .766 .868 .812 .411 .336 .730 .248 .970 .373 .862Session 2 Experimental vs. control .008* .279 .076 .000* .013* .011* .006* .255 .028* .023* .467 .021*

Session 3 Experimental vs. control .122 .701 .016* .161 .527 .490 .948 .503 .065 .765 .728 .118Experimental Group Only Sleep-deprived vs. baseline .055 .232 .118 .000* .003* .020* .012* .465 .017* .065 .429 .164 Sleep-deprived vs. recovery .042* .392 .755 .003* .000* .002* .003* .064 .015* .004* .570 .179

Note—a, upper boundary of accumulation of information over time; Ter, mean of the distribution of times taken up by the nondecision component, in seconds. h, sz, and st represent across-trial variability in drift rate, in starting point, and in the nondecision component, re-spectively. v1, v2, v3, and v4 represent the four distinct drift-rate parameters for the eight numerosity categories (see the text for details). po, proportion of contaminants; vc, drift criterion; z, starting point of accumulation of information over time. Session 1, baseline; Session 2, sleep-deprived (or control); Session 3, recovery. Group 3 session interactions are as follows: For a, F(2,46) 5 7.40; for Ter, F(2,46) 5 4.20; for h, F(2,46) 5 2.34; for sz, F(2,46) 5 7.04; for v1, F(2,46) 5 8.77; for v2, F(2,46) 5 10.39; for v3, F(2,46) 5 6.07; for po, F(2,46) 5 8.08; for st, F(2,46) 5 4.2. The critical value was F.95(2,46) 5 3.2. *p , .05.

Sleep Deprivation effectS on cognitive proceSSeS 749

Thus, such a deficit in this simple two-choice task argues for a potent effect of sleep deprivation on ability to effec-tively extract information from stimuli.

In the sleep-deprivation condition, as compared with the baseline condition, participants also adopted moder-ately more conservative decision criteria, which suggests some compensation for poorer processing in extracting in-formation from the stimulus. Also, variability in the start-ing point across trials was larger, and variability in the nondecision component of processing across trials was modestly but significantly larger. These increases suggest a reduction in the ability to reset the decision process to the same starting point and suggest an increase in variabil-ity in the encoding and response-output processes.

There was a notable increase in the proportion of con-taminants, indicating that sleep-deprived participants made more random responses, albeit with large individual differences. There was no trade-off across participants be-tween the proportion of contaminants and other model parameters; all of the other components of processing had similar trends across participants, as a function of sleep deprivation.

There was no increase in the duration of the nondeci-sion component of processing, which represents the dura-tion of stimulus-encoding and response-output processes as a function of sleep deprivation. Also, there was no in-crease in the variability in drift rate across trials, which suggests that sleep deprivation simply lowers the quality of the output from stimulus processing on all trials rather than lowering the quality considerably on some trials but hardly at all on other trials.

There are competing hypotheses about the underlying mechanisms of the effects of sleep deprivation on perfor-mance in cognitive tasks. One hypothesis suggests that sleep loss specifically affects cognitive processes medi-

To show that the assumption of randomly distributed contaminants is reasonable, we used the data from the participant with the largest proportion of contaminants to evaluate the fit of the model to the RT distribution. This participant had similar proportions of “large” responses across five of the numerosity categories. The average pro-portion of “large” responses was .755, and the mean RT was 867 msec (the five categories had proportions rang-ing from .714 to .789 and mean RTs ranging from 794 to 875 msec); the estimated proportion of contaminants was .38. The left panel of Figure 5 shows the data histogram with the predicted distribution generated from the model parameters with the contaminant assumption; the right panel shows the predicted distribution generated from the same model parameters but with zero contaminants. The right panel shows that the tail of the distribution is mispredicted; but the left panel, with the assumption of uniform random guesses, shows the long, elevated tail that adequately describes the data.

dISCuSSIoN

In this study, the effects of sleep deprivation on distinct cognitive processes in a two-choice decision paradigm were investigated. Data were fit with the diffusion model, which has previously been applied successfully in a num-ber of other domains (see Ratcliff & McKoon, 2008). The diffusion model accounts for both speed and accuracy of processing in a unified approach. The model analysis of our data showed that processes that produced evidence from the stimulus entering the decision process were im-paired substantially by sleep deprivation: Drift rates de-creased by about 30% relative to baseline values. This is a large effect, especially considering that the two-choice task requires few, if any, perceptual or memory demands.

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Figure 5. Response time (Rt) distributions for the participant with the most estimated contami-nants. 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 pa-rameter values as in the left panel. the assumption of uniform random guesses produces an Rt distribution tail that adequately describes the data.

750 ratcliff anD van Dongen

NIMH Grant R37-MH44640 to the first author. We thank the research team of the Sleep and Performance Research Center at Washington State University–Spokane and Russ Childers of the Department of Psychol-ogy at Ohio State University for their help with the implementation of this study. Correspondence concerning this article should be addressed to R. Ratcliff, Department of Psychology, 1835 Neil Ave., Ohio State University, Columbus, OH 43210 (e-mail: ratcliff.22@osu.edu).

ReFeReNCeS

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Doran, S. M., Van Dongen, H. P. A., & Dinges, D. F. (2001). Sus-tained attention performance during sleep deprivation: Evidence of state instability. Archives of Italian Biology, 139, 253-267.

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ated by the prefrontal cortex (PFC; Harrison, Horne, & Rothwell, 2000), thereby degrading higher-order cognitive functions, such as decision making (Harrison & Horne, 2000). Our results are consistent with this hypothesis be-cause drift rates (central cognitive processes involved in producing evidence from the stimulus) were reduced by sleep deprivation. However, the effects of sleep deprivation were not exclusively on such processes, so the hypothesis is only partially consistent with the experimental findings.

Another hypothesis, known as the state instability hy-pothesis (Doran, Van Dongen, & Dinges, 2001), postu-lates that sleep deprivation induces an escalating “state instability” that is particularly evident in experimental tasks requiring sustained attention. This hypothesis posits that sleep deprivation induces moment-to-moment fluctu-ations in sustained attention due to the influence of sleep-initiating mechanisms. Our results are consistent with the state instability hypothesis, in that sleep deprivation increased the number of contaminants (guesses), skewed the RT distributions (Dinges & Kribbs, 1991) partly by in-creasing separation of decision criteria, and increased the across-trial variability in the starting point of the decision process, as well as in the nondecision component. State instability may also affect attentional networks (Posner, 2008) and, thereby, the functioning of the PFC (Durmer & Dinges, 2005), which could explain the degradation of stimulus processing. Although this argument needs to be demonstrated experimentally, the state instability hypoth-esis has the potential to provide the most comprehensive explanation of the present findings, although the hypoth-esized moment-to-moment fluctuations in sustained at-tention would seem to extend to other aspects of cognition as well. The challenge is to unambiguously link these hy-potheses to components of processing in explicit models so they can be subjected to experimental tests.

The nature and scope of the cognitive impairments re-sulting from sleep deprivation have been debated for more than a century (Doran et al., 2001; Horne, 1993; Kleit-man, 1923; Lim & Dinges, 2008; Patrick & Gilbert, 1896; Williams, Lubin, & Goodnow, 1959). However, progress has been slowed by a tendency to focus on global per-formance outcomes, with little research being done to examine the effects of sleep deprivation on distinct cogni-tive processes. Using the diffusion model, we were able to study the effects of sleep deprivation on distinct cognitive processes. Within the framework of current theories about the effects of sleep deprivation on cognition (see Dinges & Kribbs, 1991; Harrison & Horne, 2000), the present results suggest that sleep deprivation degrades both atten-tional arousal (through state instability) and central pro-cessing. Indeed, our findings show that sleep deprivation has wide-ranging effects on brain functioning, affecting multiple, distinct components of cognition.

AuthoR Note

The present research was supported by USAMRMC Award W81XWH-05-1-0099 and DURIP Grant FA9550-06-1-0281. Prepara-tion of this article was also supported by NIA Grant R01-AG17083 and

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(Manuscript received October 6, 2008; revision accepted for publication April 1, 2009.)

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