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It is well documented that interacting with a mobile phone is associated with poorer performance on concurrently performed tasks because limited attentional resources must be shared between tasks. However, mobile phones generate auditory or tactile notifications to alert users of incoming calls and messages. Although these notifications are generally short in duration, they can prompt task-irrelevant thoughts, or mind wandering, which has been shown to damage task performance. We found that cellular phone notifications alone significantly disrupted performance on an attention-demanding task, even when participants did not directly interact with a mobile device during the task. The magnitude of observed distraction effects was comparable in magnitude to those seen when users actively used a mobile phone, either for voice calls or text messaging. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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The Attentional Cost of Receiving a Cell Phone Notification
Cary Stothart, Ainsley Mitchum, and Courtney Yehnert
Florida State University
Author Note
This paper was published in the Journal of Experimental Psychology: Human Perception and
Performance in August 2015. You can find the publisher-formatted version at:
http://dx.doi.org/10.1037/xhp0000100.
C. Stothart helped develop the idea for the study, prepared the materials, conducted the statistical
analyses, and edited the manuscript. A. Mitchum helped develop the idea for the study, provided
critical feedback, and drafted the manuscript. C. Yehnert collected the data, provided critical
feedback, and edited the manuscript.
Correspondence should be addressed to Cary Stothart, Department of Psychology, Florida State
University, 1107 W. Call Street, Tallahassee, FL 32306-4301. E-mail: stothart@psy.fsu.edu
Abstract
It is well documented that interacting with a mobile phone is associated with poorer performance
on concurrently performed tasks because limited attentional resources must be shared between
tasks. However, mobile phones generate auditory or tactile notifications to alert users of incoming
calls and messages. Although these notifications are generally short in duration, they can prompt
task-irrelevant thoughts, or mind wandering, which has been shown to damage task performance.
We present evidence that cellular phone notifications alone significantly disrupt performance on
an attention-demanding task, even when participants do not directly interact with a mobile device
during the task. The magnitude of observed distraction effects was comparable in magnitude to
those seen when users actively use a mobile phone, either for voice calls or text messaging.
Keywords: cell phones, text messaging, distraction, mind wandering, attention, prospective
memory
Word count: 2500
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Introduction
Mobile phones have become ubiquitous in modern culture; an estimated 91 percent of American
adults report owning a mobile phone, and an increasing proportion of these are “smartphones,”
which can also be used to access email and social media, download music, and watch videos
(Duggan, 2013). A common concern is that these multi-use electronic devices are a significant
distractor and that many users, particularly adolescents and young adults, do not make judicious
decisions about when it is safe and appropriate to use a mobile device (National Highway Traffic
Safety Administration [NHTSA], 2012; NHTSA, 2013). In addition to the well-known effects of
cellular phone-related distraction on driving performance, which are found whether or not drivers
use a hands-free device (Caird et al., 2014; Horrey & Wickens, 2006; NHTSA, 2013; Strayer &
Johnston, 2001; Strayer, Drews, & Johnston, 2003), problematic use of mobile phones has also
been documented in many other settings (Katz-Sidlow, Ludwig, Miller, & Sidlow, 2012; Smith,
Darling, & Searles, 2010; Tindell & Bohlander, 2012).
Public information campaigns intended to deter problematic mobile phone use often emphasize
waiting to respond to messages and calls. However, it may be that waiting, too, carries a significant
attention cost. This yet unexamined source of phone-related distraction is the focus of the current
study. There is good reason to suspect that waiting to respond to a call or text message may itself
disrupt attention performance. First, work in prospective memory has found that simply
remembering to perform some action in the future is sufficient to disrupt performance on an
unrelated concurrent task (e.g. Smith, 2003). In some sense, noticing that one has received a call
or text represents a new prospective memory demand; if one receives a message or call, most prefer
to respond promptly. Second, a large body of work finds that task-irrelevant thoughts, even in
cases where the individual appears to be attending to the task at hand, disrupt performance on a
wide range of tasks (Smallwood & Schooler, 2006; Schooler et al., 2011), including driving
(Cowley, 2013; He et al., 2011; Lemercier et al., 2014). It is reasonable to expect that a notification
from a mobile device, when noticed by the user, can give rise to task-irrelevant thoughts
concerning the message’s source or content. While message notifications themselves may be very
brief, message-related thoughts prompted by these notifications likely persist for much longer.
To address the question of whether receiving, but not responding to, cellular notifications carries
an attention cost, we conducted a study where we compared performance on an attention-
demanding task, Sustained Attention to Response Task (SART; Robertson, Manly, Andrade, &
Baddeley, 1997), between participants who received cellular notifications and those who did not.
The SART, which requires quick responses to target items while also withholding responses to
infrequent non-target items, has been shown to be a sensitive measure of sustained attention, and
performance on the task correlates with self-reported instances of mind wandering (e.g Cheyne,
Solman, Carriere, & Smilek, 2009).
Method
Participants
Participants were 212 undergraduate students enrolled in psychology courses at Florida State
University. Participants were randomly assigned to one of three groups: Call, text message, or no
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notification control. Data from 46 participants were excluded from analysis, the most common
reasons for which being that the experimenter noticed the participant looking at or handling their
phone (n = 20), the participant’s phone was turned off during the study (n = 13), and missing data
(n = 9)
1
. Analyses include data from the remaining 166 participants: 57 in the text messaging group
(39 females, 11 males, 7 refused, mean age = 19.37 yrs, SD = 1.65), 50 in the call group (35
females, 12 males, 3 refused, mean age = 19.48 yrs, SD = 2.19 yrs), and 59 in the no notification
group (30 females, 19 males, 10 refused, mean age = 20.00 yrs, SD = 2.46 yrs).
Stimuli and Apparatus
Figure 1. The Sustained Attention to Response Task (SART). Participants are instructed to press
a key when a number is flashed except if that number is 3. Participants could make a keyboard
response at any time during the trial.
Sustained attention to response task. In the SART (see Figure 1), numbers were presented at a
rate of about one item per second, and participants were instructed to press a key every time a
number (digits 1 through 9) was presented (target) unless the number was 3 (non-target). The
SART consisted of three blocks: An 18-trial practice block, which included accuracy feedback,
1
We chose to exclude any participant who showed overt signs of not attending to the SART task. This is because we
were interested in the performance of participants who were attending to the task but may have been distracted by
thoughts about the message or call content. Other work has already documented performance costs associated with
interacting with mobile devices, so we did not wish to duplicate that effort in the current study. Our results do not
change if all participants are included in the analyses. A table with a full list of all reasons participants were excluded
are included in the supplemental materials, as well as analysis results if all participants are included.
4
and two additional blocks of 360 trials each, which included no accuracy feedback. Each unique
digit appeared 40 times per block, and presentation order was randomized for each participant.
The font size at which digits were presented was varied between trials, randomly selected from a
set of five possible font heights, with each size used 72 times per block: 1.20, 1.80, 2.35, 2.50, or
3.00 cm
2
.
Mobile phone notifications. Participants in the two notification conditions received either calls
or text messages during the second block of the SART. In order to synchronize the timing of the
SART trials with the timing of the text messages, the SART included an integrated script that sent
text messages or made calls to participants’ phones. Both the notification program and the SART
were coded in Python 2.7 and utilized functions from the PsychoPy package (Peirce, 2007). Text
messages and phone calls were sent using the Twilio application programming interface (Twilio,
2014a; Twilio, 2014b).
A unique and important aspect of our experimental task, which differs from previous studies of
cellular phone induced distraction, is that notifications were sent to participants’ own phones, but
participants were not made aware of this, or that the experiment’s purpose related to cellular
phones or distraction, until after the task. We chose this approach because we believe that a large
part of cellular notifications’ potential for distraction comes from the fact that messages contain
personally-relevant content
3
, which would not be true of notifications participants know are part
of an experiment. To avoid arousing suspicion about the experiment’s true purpose, participants
received no instructions regarding their phones at the beginning of the experimental session, nor
were they instructed to refrain from checking their phones during the SART. If a participant did
look at or manipulate their phone during the SART, the experimenter recorded the number of times
this occurred during the session.
Procedure
Participants first completed an electronic check-in sheet, which included fields for their phone
number, email, and other demographic and health information. Participants in the text message
and call conditions received either text messages or phone calls during the second block of the
SART, and participants in the no notification group did not receive notifications, though they may
have received personal messages during that time. To ensure that experimenters were blind to
participant condition, the experiment program managed condition assignment.
The experimenter remained in the room during the entire session but was seated behind the
participant, so that the experimenter had a clear view of the screen but was not in the participant’s
field of view. SART instructions, which were displayed on screen and were identical for all three
groups, instructed participants to give equal importance to both accuracy and speed. Immediately
following the instructions, participants completed a brief practice block, then began the first block
2
This was also done in the original version of the SART task used by Robertson et al., 1997. The purpose of the
manipulation was to increase the likelihood that performance on the task required processing the numerical value of
items and did not reflect a search strategy where participants looked only for a specific feature of non-target items
(3s).
3
However, it is possible that the opposite is true.
5
of the SART. Participants in both groups were given a 1-minute break before beginning the second
block and were asked to remain in the room during the break.
During the second block of the SART, participants in the two notification conditions received
either text messages or calls placed to the phone number they provided at the beginning of the
session. The first notification was sent after trial 1, a second was sent after the 91th trial, a third
was sent after the 181th trial, and a fourth was sent after the 271th trial, making it so that each
notification was separated by 90 trials.
Following the SART, but before being debriefed, all participants completed a survey asking about
their beliefs about the purpose and hypothesis of the study and then a survey regarding their text
messaging behavior. At the end of the session, all participants were fully debriefed on the purpose
of the study and asked what alert setting their phone had been on during the task.
Results
Analyses were run using the lme4 package in R version 2.14.1 (R Core Team, 2012; Bates et al.,
2013). Two metrics of attention performance were examined, commission errors, unintended
responses to non-target items, and anticipations, which are responses with such fast response times
that it is likely the participant responded before they were aware of what number was being
displayed. Past work has found each of these measures of attention performance to be associated
with task-unrelated thoughts (e.g. Cheyne et al., 2009).
Table 1
The results from the mixed effects logistic regression model for commission errors.
Variable
B (logit)
Wald
Z
p
Intercept
-0.388
-5.221
< 0.001
Block 1
0.148
7.758
< 0.001
Notifications vs. no notifications
0.014
0.264
0.791
Calls vs. text messaging
0.173
1.871
0.061
Block 1: notifications vs. no notifications
0.047
3.566
< 0.001
Block 1: calls vs. text messaging
0.033
1.367
0.171
Note. The variance for the random intercepts was 0.845 (logit).
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Figure 2. Probability of making a commission error by condition and block. Error bars represent
one standard error.
A mixed-effects logistic regression using full maximum likelihood estimation was conducted to
assess the effect of phone notifications on the probability of making a commission error. The full
model included fixed effects of block number, notification condition, and their interaction and a
random intercept for each participant (see Table 1). As in other studies using long, sustained
attention tasks like the SART, the probability of errors increased between blocks, with participants
making significantly more errors on the second block of trials than on the first, X2(1) = 55.97, p <
0.001 (see Figure 2). However, and more critically, the extent to which the probability of making
an error increased between blocks differed between conditions, X2(2) = 14.15, p < 0.001, such that
there was a greater increase in the probability of making an error for the text message and call
conditions compared to the no notification condition
4
. The degree to which the probability of
making an error increased between blocks did not differ significantly between the text message
and call conditions, though the magnitude of the increase was larger for the call group (dz = 0.72)
than for the text message group (dz = 0.54). When comparing each notification group to the no
4
The effect of block, X2(1) = 65.35, p < 0.001, as well as the block by condition interaction, X2(2) = 9.59, p = 0.008,
were virtually unchanged when analyses were also run on the full sample of 212 participants, including the 46 excluded
participants. The degree to which commission error rate increased between blocks were also similar in this analysis:
Call (dz = 0.63), text message (dz = 0.50), and no notification (dz = 0.17).
7
notification group directly, both the call group (b = -0.17, SE = 0.05, Wald Z = -3.69, p < 0.001)
and text message group (b = 0.11, SE = 0.05, Wald Z = 2.37, p = 0.02) showed a greater block
increase.
For the call condition, the state of the participant’s phone was recorded for each trial (e.g. ringing,
not ringing)
5
. Participants’ phones were ringing during 31% of trials during block 2. If the act of
noticing a notification itself is distracting, one would expect a higher rate of commission errors
during trials where participants’ phones were ringing. This was not the case; the probability of
errors was effectively identical between trials where the participants’ phone was ringing (49%)
and those where it was not (51%), b = 0.07, SE = 0.11, Wald Z = 0.66, p = 0.51.
Another indicator of distraction on the SART is the frequency of trials with very fast response
times, which suggest that the participant is responding automatically, in pace with the SART’s
predictable item presentation rate. We defined fast response times as those that fell below the 5th
percentile for all response times (< 188.9 ms), which included both correct responses and
commission errors. As was done for commission errors, a mixed-effects logistic regression was
used to test whether there was a greater change in the likelihood of very fast response times
between blocks for the two notification conditions. Consistent with results for commission errors,
the likelihood of very fast response times increased between blocks and the magnitude of this
increase differed between groups, X2(2) = 25.41, p < 0.001. However, for this measure the increase
was larger for the call group compared to the text message group, suggesting that call notifications
were more distracting than text notifications (see Table 2; Figure 3). When comparing each
notification group to the no notification group directly, the call group (b = -0.18, SE = 0.04, Wald
Z = -4.49, p < 0.001) but not the text message group (b = 0.01, SE = 0.04, Wald Z = 0.14, p = 0.89)
showed a greater block increase.
Table 2
The results from the mixed effects logistic regression model for fast response times.
Variable
B (logit)
Wald
Z
95% CI for B
p
Intercept
-4.099
-29.35
-4.378, -3.827
< 0.001
Block 1
0.513
31.69
0.482, 0.545
< 0.001
Notifications vs. no notifications
0.012
0.170
-0.174, 0.207
0.865
Calls vs. text messaging
0.193
1.120
0.147, 0.535
0.265
Block 1: notifications vs. no notifications
0.030
2.670
0.008, 0.052
0.008
Block 1: calls vs. text messaging
0.085
4.210
0.045, 0.124
< 0.001
Note. The variance for the random intercepts was 2.977 (logit).
5
This is not possible for text messages, as the state of the receiver’s phone cannot be monitored with text messages.
Also, the delay between when a text message is sent and when it will be received, although typically very brief, is not
consistent and cannot be predicted.
8
Figure 3. Probability of making a fast response (a response less than 188.91 ms) by condition
and block. Error bars represent one standard error.
Discussion
The current study found evidence that cellular notifications, even when one does not view or
respond to messages or answer calls, can significantly damage performance on an attention-
demanding task
6
. In fact, the degree to which commission errors increased between blocks for both
the call (dz = 0.72) and text message (dz = 0.54) conditions was similar in magnitude to the decrease
in performance between distracted and non-distracted conditions in studies of distracted driving
(Caird et al., 2014; Horrey & Wickens, 2006). Though not directly assessed in our study, we
believe that what underlies this effect is the tendency for cellular notifications to prompt task-
irrelevant thoughts, or mind wandering, which persist beyond the duration of the notifications
themselves. Because our analysis included only participants who did not directly interact with their
6
We must, however, use some caution when interpreting the effects observed in the text message group. In terms of
making a fast response, the block difference was equivalent between the text message group and no notification group.
For commission errors, there was a greater block increase for the text message group compared to the no notification
group, however, the baseline for the text message group was lower than the no notification group’s baseline and the
second block difference between the two groups was equivalent. Taken together, these results make it difficult to
determine if the commission error difference between the text message group and no notification group is due to the
text messages being distracting or something else (e.g., regression-to-the-mean).
9
mobile phone, either by looking at or manipulating the phone, and the degree to which error rate
increased between blocks was significantly greater for the two notification groups compared to the
no notification group, off-task thoughts are the most likely cause of the observed between-group
performance differences
7
.
Our study had several methodological advantages over other research on mobile phone-related
distraction. First, both participants and experimenters were blind to condition and study hypothesis
(see Boot, Blakely, & Simons, 2011; Rosenthal & Rosnow, 2009). However, in most distracted
driving research both participants and experimenters are aware of distraction condition, and the
study hypothesis is easy to infer, especially given that distracted driving is often discussed in
popular media. While we do not dispute the validity of findings that performance on tasks is poorer
when people are distracted, we recognize the possibility that participant and experimenter
expectancies could exaggerate these effects. Our design rules this out as a potential explanation
for our results. Second, our study used participants’ own phones. We believe that our results likely
depend on the fact that it was plausible that the calls and text messages received during the
experiment contained personally relevant content. If participants had been aware that many of the
notifications they received were part of the experiment, this would reduce, if not eliminate, the
notifications’ potential to prompt task-irrelevant thoughts.
There is a great deal of evidence that interacting with mobile devices, whether sending messages
or engaging in conversations, can impair driving performance. Our results suggest that mobile
phones can disrupt attention performance even if one does not interact with the device. We feel
that this is an important consideration, particularly given the ubiquity of mobile phones in many
people’s day-to-day life. As mobile phones become integrated into more and more tasks, it may
become increasingly difficult for people to set their phones aside and concentrate fully on the task
at hand, whatever it may be. Further, it may be that the persistence of problematic mobile phone
use is driven, at least in part, by the distracting effect of notifications. If people are genuinely
distracted by notification-induced thoughts, some problematic mobile phone use could be
prompted by the desire to escape that feeling of divided attention. Taken together, these findings
highlight the need to adopt a broader view of cellular phone-related distraction.
7
It’s possible that this relationship is mediated by anxiety and/or a temporary or long-term reallocation of attention.
10
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The purpose of this sequential explanatory mixed-methods research study was to investigate the role of smartphones in college students' mind-wandering during learning. In the quantitative phase of the study, data were collected from 402 college students in order to examine the extent to which college students' smartphone addiction predicts their level of mind-wandering. The result of a simple linear regression analysis indicated that college students' smartphone addiction explained 26% of variance in their level of mind-wandering. In the qualitative phase of the study, semi-structured interviews were conducted with 14 college students in order to explain this relationship by exploring how smartphones influence college students' mind-wandering during learning. The analysis of these interviews revealed that messages, incoming calls, social media, and smartphone functions played a prominent role in college students’ smartphone-related mind-wandering during lectures or whilst studying. The implications of these findings were discussed and some suggestions for future studies put forth.
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p style="text-align: justify;">Recent research has found that the presence of cell phones impairs attention during learning. The present experiment sought to better understand this phenomenon by measuring the effects of cell phone presence, cell phone notifications, and cell phone ownership (participant's or others) on attention. Attention was measured using a Stroop task in a within-subjects design, wherein participants (n = 105) were exposed to five experimental conditions. Cell phone notifications caused distractions, regardless of phone ownership and task difficulty, increasing the amount of time required to complete the task. However, unlike the noted literature above, the researchers did not find that the mere presence of a cell phone contributed to distraction. These results help us better understand which factors actually contribute to distraction and inattention.</p
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This article reviews the hypothesis that mind wandering can be integrated into executive models of attention. Evidence suggests that mind wandering shares many similarities with traditional notions of executive control. When mind wandering occurs, the executive components of attention appear to shift away from the primary task, leading to failures in task performance and superficial representations of the external environment. One challenge for incorporating mind wandering into standard executive models is that it often occurs in the absence of explicit intention—a hallmark of controlled processing. However, mind wandering, like other goal-related processes, can be engaged without explicit awareness; thus, mind wandering can be seen as a goal-driven process, albeit one that is not directed toward the primary task.
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Text messaging while driving is considered dangerous and known to produce injuries and fatalities. However, the effects of text messaging on driving performance have not been synthesized or summarily estimated. All available experimental studies that measured the effects of text messaging on driving were identified through database searches using variants of “driving” and “texting” without restriction on year of publication through March 2014. Of the 1476 abstracts reviewed, 82 met general inclusion criteria. Of these, 28 studies were found to sufficiently compare reading or typing text messages while driving with a control or baseline condition. Independent variables (text-messaging tasks) were coded as typing, reading, or a combination of both. Dependent variables included eye movements, stimulus detection, reaction time, collisions, lane positioning, speed and headway. Statistics were extracted from studies to compute effect sizes (rc). A total sample of 977 participants from 28 experimental studies yielded 234 effect size estimates of the relationships among independent and dependent variables. Typing and reading text messages while driving adversely affected eye movements, stimulus detection, reaction time, collisions, lane positioning, speed and headway. Typing text messages alone produced similar decrements as typing and reading, whereas reading alone had smaller decrements over fewer dependent variables. Typing and reading text messages affects drivers’ capability to adequately direct attention to the roadway, respond to important traffic events, control a vehicle within a lane and maintain speed and headway. This meta-analysis provides convergent evidence that texting compromises the safety of the driver, passengers and other road users. Combined efforts, including legislation, enforcement, blocking technologies, parent modeling, social media, social norms and education, will be required to prevent continued deaths and injuries from texting and driving.
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This research examined the relationship between performance decrements and types of off task thinking, including off task thoughts, mind wanderings and wanderings without meta-awareness. Participants (n=118) were probed 5 times during a simulated driving route, wherein they self-classified their own thought types as on task, off task, mind wanderings and meta-unaware mind wanderings. Performance decrements (i.e., seconds of speeding and number of lane deviations) were objectively measured in the 14 seconds preceding the probe. When participants were most frequently mind wandering without metaawareness, the highest mean number of lane deviations and the second highest mean seconds of speeding were recorded. When participants were most frequently on task, the lowest mean seconds of speeding and second lowest number of mean lane deviations resulted. These findings suggest a relationship between off task thought types and performance decrements. Implications and future research directions are discussed herein.
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As the use of mobile devices, such as cell phones, has proliferated in academic settings in recent years, new challenges are faced by institutions of higher education and their faculties. The authors surveyed 269 college students from 21 academic majors at a small northeastern university to gain a better understanding of the frequency and manner in which cell phones are used in college classrooms. Focusing on the use of text messaging in the classroom, students reported on their own and others’ use of cell phones. It was found that 95% of students bring their phones to class every day, 92% use their phones to text message during class time, and 10% admit they have texted during an exam on at least one occasion. The majority of the students surveyed believe that instructors are largely unaware of the extent to which texting and other cell phone activities engage students in the classroom. These activities include browsing the Internet, sending pictures, or accessing social networking sites. The authors discuss these and other findings and their implications for issues of classroom management and academic dishonesty.
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While there are numerous benefits of smartphone use for physicians, little is known about the negative effects of using these devices in the context of patient care. To assess resident and faculty smartphone use during inpatient attending rounds and its potential as a source of distraction during transfer of clinical information. Cross-sectional survey. University-affiliated public teaching hospital. All housestaff and inpatient faculty in the departments of Medicine and Pediatrics. Participants were asked about smartphone ownership, usage patterns during attending rounds, and whether team members had ever missed important data during rounds due to distraction from smartphones. Attendings were asked whether policies should be established for smartphone use during rounds. The overall response rate was 73%. Device ownership was prevalent (89% residents, 98% faculty), as was use of smartphones during inpatient rounds (57% residents, 28% attendings). According to self-reports, smartphones were used during rounds for patient care (85% residents, 48% faculty), reading/responding to personal texts/e-mails (37% residents, 12% faculty), and other non-patient care uses (15% residents, 0% faculty). Nineteen percent of residents and 12% of attendings believed they had missed important information because of distraction from smartphones. Residents and faculty agreed that smartphones "can be a serious distraction during attending rounds," and nearly 80% of faculty believed that smartphone policies should be established. Smartphone use during attending rounds is prevalent and can distract users during important information transfer. Attendings strongly favored the institution of formal policies governing appropriate smartphone use during inpatient rounds. Journal of Hospital Medicine 2012;. © 2012 Society of Hospital Medicine.
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Insufficient attention to tasks can result in slips of action as automatic, unintended action sequences are triggered inappropriately. Such slips arise in part from deficits in sustained attention, which are particularly likely to happen following frontal lobe and white matter damage in traumatic brain injury (TBI). We present a reliable laboratory paradigm that elicits such slips of action and demonstrates high correlations between the severity of brain damage and relative-reported everyday attention failures in a group of 34 TBI patients. We also demonstrate significant correlations between self-and informant-reported everyday attentional failures and performance on this paradigm in a group of 75 normal controls. The paradigm (the Sustained Attention to Response Task—SART) involves the withholding of key presses to rare (one in nine) targets. Performance on the SART correlates significantly with performance on tests of sustained attention, but not other types of attention, supporting the view that this is indeed a measure of sustained attention. We also show that errors (false presses) on the SART can be predicted by a significant shortening of reaction times in the immediately preceding responses, supporting the view that these errors are a result of `drift' of controlled processing into automatic responding consequent on impaired sustained attention to task. We also report a highly significant correlation of −0.58 between SART performance and Glasgow Coma Scale Scores in the TBI group.