OBSERVATIONThe Attentional Cost of Receiving a Cell Phone .docx

OBSERVATION
The Attentional Cost of Receiving a Cell Phone Notification
Cary Stothart, Ainsley Mitchum, and Courtney Yehnert
Florida State University
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.
Keywords: cell phones, text messaging, distraction, mind
wandering, attention, prospective memory
Supplemental materials:
http://dx.doi.org/10.1037/xhp0000100.supp
Mobile phones have become ubiquitous in modern culture; an
estimated 91% of American adults report owning a mobile
phone, and

an increasing proportion of these are “smartphones,” which can
also
be used to access e-mail and social media, download music, and
watch videos (Duggan, 2013). A common concern is that these
multiuse electronic devices are a significant distractor and that
many
users, particularly adolescents and young adults, do not make
judi-
cious decisions about when it is safe and appropriate to use a
mobile
device (National Highway Traffic Safety Administration, 2012,
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, Johnston, Willness,
Asbridge,
& Steel, 2014; Horrey & Wickens, 2006; National Highway
Traffic
Safety Administration, 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; T. Smith, Darling, & Searles, 2011;
Tindell
& Bohlander, 2012).
Public information campaigns intended to deter problematic mo-
bile 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
distrac-
tion 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.,
R. E. 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 has found that task-irrelevant thoughts,
even in
cases when 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, Becic, Lee, & McCarley, 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
concern-
ing the message’s source or content. While message
notifications
themselves may be very brief, message-related thoughts
prompted by
these notifications likely persist much longer.
To address whether receiving, but not responding to, cellular
notifications carries an attention cost, we conducted a study to
compare performance of participants who received cellular
notifi-
cations and those who did not using an attention-demanding

task,
the Sustained Attention to Response Task (SART; Robertson,
Manly, Andrade, Baddeley, & Yiend, 1997). The SART, which
requires quick responses to target items while also withholding
responses to infrequent nontarget 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 psy-
chology courses at Florida State University. Participants were
This article was published Online First June 29, 2015.
Cary Stothart, Ainsley Mitchum, and Courtney Yehnert,
Department of
Psychology, Florida State University.
We would like to thank Blake Cowing and Diana Hanf for their
assis-
tance in reviewing the literature and providing useful
comments.
Correspondence concerning this article should be addressed to
Cary
Stothart, Department of Psychology, Florida State University,
1107 West
Call Street, Tallahassee, FL 32306-4301. E-mail:
[email protected]
T
hi

s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch

ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs

.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al

us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at

ed
br
oa
dl
y.
Journal of Experimental Psychology:
Human Perception and Performance
© 2015 American Psychological Association
2015, Vol. 41, No. 4, 893– 897
0096-1523/15/$12.00 http://dx.doi.org/10.1037/xhp0000100
893
http://dx.doi.org/10.1037/xhp0000100.supp
mailto:[email protected]
http://dx.doi.org/10.1037/xhp0000100
randomly assigned to one of three groups: call, text message, or
no
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 included
data
from the remaining 166 participants: 57 in the text messaging
group (39 females, 11 males, 7 refused; Mage � 19.37 years,

SD �
1.65), 50 in the call group (35 females, 12 males, 3 refused;
Mage � 19.48 years, SD � 2.19), and 59 in the no notification
group (30 females, 19 males, 10 refused; Mage � 20.00 years,
SD � 2.46).
Stimuli and Apparatus
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�9) was presented (target) unless the number
was 3 (nontarget). The SART consisted of three blocks: An 18-
trial
practice block, which included accuracy feedback, and two
addi-
tional 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 notifica-
tion conditions received either calls or text messages during the
second block of the SART. 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 partici-
pants’ phones. Both the notification program and the SART
were

coded in Python, Version 2.7, and used functions from the Psy-
choPy package (Peirce, 2007). Text messages and phone calls
were sent using the Twilio application programming interface
(Twilio, 2014a, 2014b).
A unique and important aspect of our experimental task, which
differs from previous studies of cellular phone�induced distrac-
tion, 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, which
would not be true of notifications participants knew were part
of an
experiment3. 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 oc-
curred during the session.
Procedure
Participants first completed an electronic check-in sheet, which
included fields for their phone number, email, and other demo-
graphic 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
follow-
ing the instructions, participants completed a brief practice
block,
then began the first block of the SART. Participants in both
groups
were given a 1-min 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

1 We excluded any participant who showed overt signs of not
attending
to the SART task 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
documented
performance costs associated with interacting with mobile
devices, so we
did not want to duplicate that effort in the current study. Our
results did not
change when all participants were included in the analyses. A
table with a
full list of reasons participants were excluded is included in the
supple-
mental materials, as well as analysis results when all
participants were
included.
2 This was also done in the original version of the SART 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 nontarget items (3s).
3 However, it is possible that the opposite is true.
Figure 1. The Sustained Attention to Response Task.
Participants were
instructed to press a key when a number is flashed, except if the

number
was 3. Participants could make a keyboard response at any time
during the
trial.
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er

ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed

pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th

e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be

di
ss
em
in
at
ed
br
oa
dl
y.
894 STOTHART, MITCHUM, AND YEHNERT
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 Development Core Team, 2012; Bates, Maechler, Bolker, &
Walker, 2013). Two metrics of attention performance were
exam-
ined: commission errors, that is, unintended responses to
nontarget
items, and anticipations, which are responses with such fast
reac-
tion times (RTs) that it is likely participants 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).
A mixed-effects logistic regression using full maximum likeli-
hood 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, �2(1) � 55.97, p
�
.001 (see Figure 2). However, and more critically, the extent to
which the probability of making an error increased between
blocks
differed between conditions, �2(2) � 14.15, p � .001, such that
there was a greater increase in the probability of making an
error
for the text message and call conditions than 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 with the no notification group directly, both the call
group
(b � �0.17, SE � 0.05, Wald z � �3.69, p � .001) and text
message group (b � 0.11, SE � 0.05, Wald z � 2.37, p � .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 partici-
pant’s phone was ringing (49%) and those where it was not
(51%)
(b � 0.07, SE � 0.11, Wald z � 0.66, p � .51).
Another indicator of distraction on the SART is the frequency
of
trials with very fast RTs, which suggest that the participant is
responding automatically, in pace with the SART’s predictable
item presentation rate. We defined fast RTs as those that fell
below
the 5th percentile for all RTs (�188.9 ms), which included both

correct responses and commission errors. As was done for com-
mission errors, a mixed-effects logistic regression was used to
test
whether there was a greater change in the likelihood of very fast
RTs between blocks for the two notification conditions.
Consistent
4 The effect of block, �2(1) � 65.35, p � .001, as well as the
Block �
Condition interaction, �2(2) � 9.59, p � .008, were virtually
unchanged
when analyses were run on the full sample of 212 participants,
including
the 46 excluded participants. The degree to which commission
error rate
increased between blocks was also similar in this analysis: call
(dz � 0.63),
text message (dz � 0.50), and no notification (dz � 0.17).
5 This was not possible for text messages, because the state of
the
receiver’s phone could not 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.
Table 1
Results of the Mixed Effects Logistic Regression Model for
Commission Errors
Variable B (logit) Wald z 95% CI for B p
Intercept �0.388 �5.221 [�0.535, �0.241] �.001
Block 1 0.148 7.758 [0.110, 0.185] �.001

Notifications vs. no notifications 0.014 0.264 [�0.088, 0.116]
.791
Calls vs. text messaging 0.173 1.871 [�0.009, 0.356] .061
Block 1: Notifications vs. no notifications 0.047 3.566 [0.021,
0.073] �.001
Block 1: calls vs. text messaging 0.033 1.367 [�0.014, 0.800]
.171
Note. The variance for the random intercepts was 0.845 (logit).
CI � confidence interval.
Figure 2. The probability of making a commission error by
condition and
block. Error bars represent 1 SE. See the online article for the
color version
of this figure.
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh

te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or

on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te

nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us

er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
895CELL PHONE NOTIFICATIONS AND ATTENTION
with results for commission errors, the likelihood of very fast
RTs
increased between blocks and the magnitude of this increase
differed between groups, �2(2) � 25.41, p � .001. However,

for
this measure, the increase was larger for the call group than the
text
message group, suggesting that call notifications were more dis-
tracting than text notifications (see Table 2; Figure 3). When
comparing each notification group with the no notification
group
directly, the call group (b � �0.18, SE � 0.04, Wald z �
�4.49,
p � .001) but not the text message group (b � 0.01, SE � 0.04,
Wald z � 0.14, p � .89) showed a greater block increase.
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 nondistracted
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 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 than the no notification group, off-task
thoughts are the most likely cause of the observed between-
groups
performance differences.7
Our study had several methodological advantages over other
research on mobile phone�related distraction. First, both
partici-
pants and experimenters were blind to condition and study
hypoth-
esis (see Boot, Blakely, & Simons, 2011; Rosenthal & Rosnow,
2009). However, in most distracted driving research, both
partic-
ipants and experimenters are aware of distraction condition, and
the study hypothesis is easy to infer, especially given that dis-
tracted driving is often discussed in popular media. Although
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 believe 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 inte-
grated into more and more tasks, it may become increasingly
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 than the no notification
group, how-
ever, 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 whether 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).
7 It is possible that this relationship is mediated by anxiety
and/or a
temporary or long-term reallocation of attention.

Table 2
Results of the Mixed Effects Logistic Regression Model for Fast
Reaction Times
Variable B (logit) Wald z 95% CI for B p
Intercept �4.099 �29.35 [�4.378, �3.827] �.001
Block 1 0.513 31.69 [0.482, 0.545] �.001
Notifications vs. no notifications 0.012 0.170 [�0.174, 0.207]
.865
Calls vs. text messaging 0.193 1.120 [0.147, 0.535] .265
Block 1: Notifications vs. no notifications 0.030 2.670 [0.008,
0.052] .008
Block 1: Calls vs. text messaging 0.085 4.210 [0.045, 0.124]
�.001
Note. The variance for the random intercepts was 2.977 (logit).
CI � confidence interval.
Figure 3. The probability of making a fast response (a response
� 188.91
ms) by condition and block. Error bars represent 1 SE. See the
online
article for the color version of this figure.
T
hi
s
do
cu
m
en
t

is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss

oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti

cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in

di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.

896 STOTHART, MITCHUM, AND YEHNERT
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
genu-
inely distracted by notification-induced thoughts, some
problem-
atic 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�re-
lated distraction.
References
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2013). lme4:
Linear
mixed-effects models using Eigen and S4 (Version 1.0 – 4)
[Computer
software]. Retrieved from http://CRAN.R-
project.org/package�lme4
Boot, W. R., Blakely, D. P., & Simons, D. J. (2011). Do action
video
games improve perception and cognition? Frontiers in
Psychology, 2,
226. http://dx.doi.org/10.3389/fpsyg.2011.00226
Caird, J. K., Johnston, K. A., Willness, C. R., Asbridge, M., &

Steel, P.
(2014). A meta-analysis of the effects of texting on driving.
Accident
Analysis and Prevention, 71, 311–318.
Cheyne, J. A., Solman, G. J. F., Carriere, J. S. A., & Smilek, D.
(2009).
Anatomy of an error: A bidirectional state model of task
engagement/
disengagement and attention-related errors. Cognition, 111, 98
–113.
http://dx.doi.org/10.1016/j.cognition.2008.12.009
Cowley, J. A. (2013). Types of off-task thinking and
performance decre-
ments during simulated automobile driving. Proceedings of the
Human
Factors and Ergonomics Society Annual Meeting, 57, 1214 –
1218.
http://dx.doi.org/10.1177/1541931213571270
Duggan, M. (2013, September 19). Cell phone activities 2013.
Retrieved
from http://pewinternet.org/Reports/2013/Cell-Activities.aspx
He, J., Becic, E., Lee, Y. C., & McCarley, J. S. (2011). Mind
wandering
behind the wheel: Performance and oculomotor correlates.
Human Fac-
tors, 53, 13–21. http://dx.doi.org/10.1177/0018720810391530
Horrey, W. J., & Wickens, C. D. (2006). Examining the impact
of cell
phone conversations on driving using meta-analytic techniques.
Human
Factors, 48, 196 –205.

http://dx.doi.org/10.1518/001872006776412135
Katz-Sidlow, R. J., Ludwig, A., Miller, S., & Sidlow, R. (2012).
Smart-
phone use during inpatient attending rounds: Prevalence,
patterns and
potential for distraction. Journal of Hospital Medicine, 7, 595–
599.
http://dx.doi.org/10.1002/jhm.1950
Lemercier, C., Pêcher, C., Berthié, G., Valéry, B., Vidal, V.,
Paubel, P., . . .
Maury, B. (2014). Inattention behind the wheel: How factual
internal
thoughts impact attentional control while driving. Safety
Science, 62,
279 –285. http://dx.doi.org/10.1016/j.ssci.2013.08.011
National Highway Traffic Safety Administration. (2012, April).
Young
drivers report the highest level of phone involvement in crash or
near-
crash incidences. Traffic safety facts: Research note (Report
No. DOT
HS 811 611). Washington, DC: National Highway Traffic Safety
Ad-
ministration. Retrieved from
http://www.nhtsa.gov/staticfiles/nti/pdf/
811611.pdf
National Highway Traffic Safety Administration. (2013, April).
Distracted
driving 2011. Traffic safety facts: Research note (Report No.
DOT HS
811 737). Washington, DC: National Highway Traffic Safety
Adminis-

tration. Retrieved from http://www-
nrd.nhtsa.dot.gov/Pubs/811737.pdf
Peirce, J. W. (2007). PsychoPy—Psychophysics software in
Python. Jour-
nal of Neuroscience Methods, 162, 8 –13.
http://dx.doi.org/10.1016/j
.jneumeth.2006.11.017
R Development Core Team. (2012). R: A language and
environment for
statistical computing (Version 2.15.1) [Computer software].
Retrieved
from http://www.R-project.org/
Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., &
Yiend, J.
(1997). ‘Oops!’: Performance correlates of everyday attentional
failures
in traumatic brain injured and normal subjects.
Neuropsychologia, 35,
747–758. http://dx.doi.org/10.1016/S0028-3932(97)00015-8
Rosenthal, R., & Rosnow, R. L. (2009). Artifacts in behavioral
research:
Robert Rosenthal and Ralph L. Rosnow’s classic books. New
York, NY:
Oxford University Press. http://dx.doi.org/10.1093/acprof:oso/
9780195385540.001.0001
Schooler, J. W., Smallwood, J., Christoff, K., Handy, T. C.,
Reichle, E. D.,
& Sayette, M. A. (2011). Meta-awareness, perceptual
decoupling and the
wandering mind. Trends in Cognitive Sciences, 15, 319 –326.

Smallwood, J., & Schooler, J. W. (2006). The restless mind.
Psychological
Bulletin, 132, 946 –958. http://dx.doi.org/10.1037/0033-
2909.132.6.946
Smith, R. E. (2003). The cost of remembering to remember in
event-based
prospective memory: Investigating the capacity demands of
delayed
intention performance. Journal of Experimental Psychology:
Learning,
Memory, and Cognition, 29, 347–361.
http://dx.doi.org/10.1037/0278-
7393.29.3.347
Smith, T., Darling, E., & Searles, B. (2011). 2010 Survey on
cell phone use
while performing cardiopulmonary bypass. Perfusion, 26, 375–
380.
http://dx.doi.org/10.1177/0267659111409969
Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell
phone-
induced failures of visual attention during simulated driving.
Journal of
Experimental Psychology: Applied, 9, 23–32.
http://dx.doi.org/10.1037/
1076-898X.9.1.23
Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction:
Dual-task
studies of simulated driving and conversing on a cellular
telephone.
Psychological Science, 12, 462– 466.
http://dx.doi.org/10.1111/1467-
9280.00386

Tindell, D. R., & Bohlander, R. W. (2012). The use and abuse
of cell
phones and text messaging in the classroom: A survey of
college
students. College Teaching, 60, 1–9. http://dx.doi.org/10.1080/
87567555.2011.604802
Twilio. (2014a). Twilio SMS API [Computer software].
Retrieved from
http://www.twilio.com/sms/api
Twilio. (2014b). Twilio Voice API [Computer software].
Retrieved from
http://www.twilio.com/voice/api
Received December 9, 2014
Revision received May 28, 2015
Accepted May 29, 2015 �
T
hi
s
do
cu
m
en
t
is
co

py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia

ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e

is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi

du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
897CELL PHONE NOTIFICATIONS AND ATTENTION
http://CRAN.R-project.org/package=lme4
http://dx.doi.org/10.3389/fpsyg.2011.00226

http://dx.doi.org/10.1016/j.cognition.2008.12.009
http://dx.doi.org/10.1177/1541931213571270
http://pewinternet.org/Reports/2013/Cell-Activities.aspx
http://dx.doi.org/10.1177/0018720810391530
http://dx.doi.org/10.1518/001872006776412135
http://dx.doi.org/10.1002/jhm.1950
http://dx.doi.org/10.1016/j.ssci.2013.08.011
http://www.nhtsa.gov/staticfiles/nti/pdf/811611.pdf
http://www.nhtsa.gov/staticfiles/nti/pdf/811611.pdf
http://www-nrd.nhtsa.dot.gov/Pubs/811737.pdf
http://dx.doi.org/10.1016/j.jneumeth.2006.11.017
http://dx.doi.org/10.1016/j.jneumeth.2006.11.017
http://www.R-project.org/
http://dx.doi.org/10.1016/S0028-3932%2897%2900015-8
http://dx.doi.org/10.1093/acprof:oso/9780195385540.001.0001
http://dx.doi.org/10.1093/acprof:oso/9780195385540.001.0001
http://dx.doi.org/10.1037/0033-2909.132.6.946
http://dx.doi.org/10.1037/0278-7393.29.3.347
http://dx.doi.org/10.1037/0278-7393.29.3.347
http://dx.doi.org/10.1177/0267659111409969
http://dx.doi.org/10.1037/1076-898X.9.1.23
http://dx.doi.org/10.1037/1076-898X.9.1.23
http://dx.doi.org/10.1111/1467-9280.00386
http://dx.doi.org/10.1111/1467-9280.00386
http://dx.doi.org/10.1080/87567555.2011.604802
http://dx.doi.org/10.1080/87567555.2011.604802
http://www.twilio.com/sms/api
http://www.twilio.com/voice/apiThe Attentional Cost of
Receiving a Cell Phone NotificationMethodParticipantsStimuli
and ApparatusSustained attention to response taskMobile phone
notificationsProcedureResultsDiscussionReferences
APA Citation Practice Activity
(Using OWL Purdue’s website, choose the correct reference for
each of the types of sources below.)

1. Book source with two authors listed:
a. Rath, Tom & Clifton, Donald O. (2009). How full is your
bucket? New York: Gallup Press.
b. Rath, T., & Clifton, D.O. (2009). How full is your bucket?
New York: Gallup Press.
c. Rath, T., & Clifton, D.O. (2009). How Full Is Your Bucket?
New York: Gallup Press.
d. Rath, Tom & Clifton, Donald O. How full is your bucket?
New York: Gallup Press (2009).
2. Article in a journal:
a. Smith, J. (2013). Who cares: A study of college students’
academic performance. Higher
Education Quarterly, 15, 235-240.
b. Smith, Joann (2013). Who cares: A study of college students’
c. Smith, J. (2013). Who Cares: A Study of College Students’
Academic Performance. Higher
d. Smith, J. (2013). Who cares: A study of college students’
3. Entry in an Encyclopedia:
a. Walheimer, A.L. (2001). Synergy. In The World
Encyclopedia. (Vol. 14). San Francisco, CA: World
Publications.
b. Walheimer, Alaric L. (2001). Synergy. In The World
Encyclopedia. (Vol. 14, pp. 403-406). San
Francisco, CA: World Publications.
c. Walheimer, A.L. (2001). Synergy. In The World
Encyclopedia. (Vol. 14, pp. 403-406). San
Francisco, CA: World Publications.

d. Walheimer, A.L. Synergy. In The World Encyclopedia. (Vol.
14, pp. 403-406). San Francisco, CA:
World Publications (2001).
4. Article from a website:
a. Bull, Roberto. (2015, October 4). Who will win the World
Series? Retrieved from
http://espnsports.com/bull/report/2015/104/
b. Bull, R. (2015). “Who Will Win the World Series?” Retrieved
from
c. Bull, R. Who will win the World Series? (2015, October
4).Retrieved from
d. Bull, R. (2015, October 4). Who will win the World Series?
Retrieved from
5. Correctly site the following source:
Authors: Rebecca A. Smith and Janet R. Cabel
Title: Communication Barriers with Aging Parents
Journal: Journal of Family Communication Volume 20
Website:
http://www.journaloffamilycommunication.org/special/edition/a
ging.html
Career Building/ Resume Portfolio/ Presentation
1. Cover sheet/Title Page (Title, your name, and class) – Due

November 16th
2. Resume: Using the format attached to the back of this
assignment list current or prior jobs, job skills, honors,
activities, volunteer work, internships, etc.)
3. Cover Letter (Can use either PLAN A OR PLAN B): Using
the format attached to the back of this assignment, address to a
real company or a mock company. You may also apply to an
intership or degree program. Include any
skills/specializations/strengths that you bring into the job
culture.
4. Write paper (2-3 pages, Time New Roman, 12 point font,
double-spaced, 1” margins) describing your career choice for
PLAN A and PLAN B detailing the following items:
· Why did you choose this field?
· What is your motivation factor (s) with your choice?
· Educational requirements
· Starting Salary/Salary Range/ Benefits
· What does the work-day/hourly schedule look like?
· What are the opportunities for advancement/job growth?
· Required Skills/or qualifications; do you need licensure?
· Is Continuing Ed offered? Will you have or need any
technology (systems) training?
· Any specializations/specialty fields required?
· Market availability
· Job Description
· Any Additional Information you want to include
5. APA Reference Page
6. Prepare a presentation on your “Plan B” to share with the rest
of the class. The presentation should be between 2-3 minutes.
Presentations will be during class the week of November 16th-

20th. Students will draw order of presentations on November
16th. Presentation should cover bulleted list up above. Visual
Aids are welcome but not required.
Candice’s Career Goals – Your Title Candice Tope-Phillips –
Your Name CHHS 175 MWF 9:10-10:55 – Your Class
Candice Tope-Phillips
[email protected]
292 Sequoia Way (Until August 22, 2013) 540 Herman Ave
Bowling Green, KY 42104 Bowling Green, KY 42101
(859) 624-9718(859) 624-9178

PROFESSIONAL OBJECTIVE: Seeking an entry-level job as
communication professor.
EDUCATION: Western Kentucky University, Bowling Green,
Kentucky
· Masters of Arts in Communication, August 2009
· Bachelor of Arts in Communication with an emphasis in
Corporate and Organizational Communication, December 2006
· Minor in Business Administration
SKILLS :
ORGANIZATION/PLANNING SKILLS
· Manage operating budget, unit productivity budget, and
faculty development budgets for the WKU Department of Public
Health and Social Work.
COMPUTER SKILLS
· Banner
· Microsoft Office 2007
WORK EXPERIENCE:
Western Kentucky University Dance Team Coach
Bowling Green, KY
2009-2012
Office Associate Department of Public Health
Bowling Green, KY
2007-2011
Bowling Green High School Head Coach
Bowling Green, KY
2003-2009
Office Assistant Department of Public Health,
Bowling Green KY
2001-2008
Assistant Store Manager at Hollister Co.,
Greenwood Mall

2007
HONORS:
President of Phi Mu Sorority
Hall of Distinguished Seniors, 2006
President of Lambda Pi Eta, Communication Major National
Honors Society First Runner up in WKU Homecoming Queen,
2005
540 Herman Avenue
Bowling Green, Kentucky 42104
January 25, 2011
Department of Social Work 1906 College Heights Blvd.
Bowling Green, KY 42101
Dear Search Committee:
Having worked for the Division of Public Health at Western
Kentucky University managing the administrative support since
August 2006, has allowed me to gain the skills necessary to
qualify me for the position as the Office Associate for the
Department of Social Work. I am confident that I would make a
successful addition to the department.
For the last four years, I have successfully managed all
administrative support for the Department of Public Health.
My experience in higher education administrative support has
allowed me to master the word processing skills needed to
successfully use Word, Excel, PowerPoint, and Adobe Acrobat.
In addition to utilizing these software applications, I routinely
use Banner to manage budgets and the scheduling of courses. I

am also familiar with policies and procedures for administrative
research.
My experiences as a student and employee of the university
have offered me the chance to encounter diverse personalities.
These opportunities have allowed me to gain positive
interpersonal qualities such as active listening, conflict
resolution, and leadership skills that are critical to the position.
As you will see on my resume, I possess exemplary leadership
skills. In addition to my interpersonal skill, you will find that
my professional presence and oral and written communication
skills will be an asset to this position.
After reviewing my resume, I am confident that you will find
that my experience, knowledge, and personal qualities will
qualify me for an interview. I look forward to hearing from you
and continuing my contributions and positive impact that I will
bring to the position and to Western Kentucky University.
Please call me at your earliest convenience at 859-582-3628 to
schedule an interview.
Professionally,
Candice B. Tope-Phillips
Candice B. Tope- Phillips (’06,’09)
Plan A: Communication Professor
Communication is a human process which allows interaction
with others, transfer information, exchange emotions, or
develop relationships. It is a basic necessity of society for
human beings to identify with one another. Communication has
been studied in academia using many different frameworks. I
would love to teach young adults these many different
frameworks in a university setting.
A university communication professor trains students in
different areas to become effective communicators. Along with
teaching, professors constantly research and learn new
techniques in effective communication methods, studying
human behaviors, as well as understanding future problems and

issues that will affect how people in the world communicate
(Smith, 2012).
To become a communication professor one must complete their
doctorate degree in communication or related field.CONTINUE
WITH BULLET LIST ON ASSIGNMENT
Plan B: Marine Biologist
According to “A History of the Study of Marine Biology”
(2012) marine biology is the study of life in the ocean and other
saltwater environments. I have been in love with the ocean and
the life it holds.
After interviewing, Dr. James B Woods (2011) on my
honeymoon, I learned that marine biologist don’t have a daily
routine. It really depends on what the weather, climate, mating
season, and other factors. Sometime marine biologist could
work 60 hours in one week during mating season. Tagging and
tracking the information of whales, dolphins, squids, or
whatever species you specialize in.CONTINUE WITH BULLET
LIST ON ASSIGNMENT
References
Bennett, M.G. (1998). Basic concepts of intercultural
communication: Selected readings.
Yarmouth, ME: Intercultural Press.
Brady, C. & Woodward, O. (2005). Launching a leadership
revolution. New York: Business Plus
Brown, W.A. & Yoshioka, C.F. (2003). Mission attachment and
satisfaction as factors in employee retention. Nonprofit
Management & Leadership, 14(1), 5-15.
Daley, J.M., Netting, F.E., Angula, J. (1996). Languages,
ideologies, and culture in nonprofit boards. Nonprofit
Management and Leadership, 6(3), 227-239.
French, J.R.P., & Raven, B., (1959). The bases of social power.

D Cartwright (Ed) Studies in Social Power. Ann Arbor MI:
University of Michigan Press
Gannon, M.J. (2004). Understanding global cultures. Thousand
Oaks, CA: Sage. Griffin, E., (2006). A first look at
communication theory. New York: McGraw-Hill Gudykunst,
W., & Lee, C., (1998). Cross-Cultural communication theories.
Handbook of
international and intercultural communication. Thousand Oaks,
CA: Sage Hackman, M.Z., & Johnson, C.E. (2004). Leadership:
A communication perspective. Long
Grove, IL: Waveland
King, A., (1987). Power and Communication. Prospects
Heights, IL: Waveland Press. Rabtoin, M., (2008).
Deconstructing the successful global leader. Training and
Development,
62(7), 54-59
Schmidt, W.V., Conway, R.N., Easton, S.S., & Wardrop, W.J.
(2007). Communicating globally: intercultural communication
and international business. Los Angeles CA: Sage
BRIEF REPORT
Trait Susceptibility to Worry Modulates the Effects of
Cognitive
Load on Cognitive Control: An ERP Study
Max Owens, Nazanin Derakshan, and Anne Richards
Birkbeck University of London
According to the predictions of attentional control theory (ACT)
of anxiety (Eysenck, Derakshan, Santos,
& Calvo, 2007), worry is a central feature of anxiety that

interferes with the ability to inhibit distracting
information necessary for successful task performance.
However, it is unclear how such cognitive control
deficits are modulated by task demands and by the emotionality
of the distractors. A sample of 31
participants (25 female) completed a novel flanker task with
emotional and neutral distractors under low-
and high-cognitive-load conditions. The negative-going N2
event-related potential was measured to
index participants’ level of top-down resource allocation in the
inhibition of distractors under high- and
low-load conditions. Results showed N2 amplitudes were larger
under high- compared with low-load
conditions. In addition, under high but not low load, trait worry
was associated with greater N2
amplitudes. Our findings support ACT predictions that trait
worry adversely affects goal-directed
behavior, and is associated with greater recruitment of cognitive
resources to inhibit the impact of
distracting information under conditions in which cognitive
resources are taxed.
Keywords: worry, flanker, cognitive load, cognitive control,
ERPs
A tendency to engage in worry in response to uncertainty may
be related to maladaptive neurocognitive function and behavior
across anxiety disorders (cf. Grupe & Nitschke, 2013). As de-
scribed by the attentional control theory (ACT) of anxiety (Ey-
senck, Derakshan, Santos, & Calvo, 2007), worry contributes to
cognitive dysfunction by increasing cognitive interference and
occupying attentional resources of the limited capacity working
memory system that are needed for successful task performance.
In
this way, worry is theorized to impair processing efficiency, in
which efficiency reflects the amount of effort needed to

maintain
effective task performance.
Processing efficiency impairments in anxiety are often reflected
as
increased neural activation in broad cognitive control regions of
the
prefrontal cortex and preserved behavioral performance for
tasks
measuring inhibitory function (see Basten, Stelzel, & Fiebach,
2011,
2012; Righi, Mecacci, & Viggiano, 2009; Sehlmeyer et al.,
2010; see
Berggren & Derakshan, 2013). For instance, in a modified
nonemo-
tional go/no-go task, Righi et al. (2009) found that anxious
individuals
had similar behavioral performance as controls, but displayed
in-
creased N2 frontal activity, typically associated with conflict
process-
ing and effortful control (Kanske & Kotz, 2012).
In line with predictions from ACT, high- compared with low-
trait-anxious individuals often show increased interference from
irrelevant threat information on emotional Stroop and dot-probe
paradigms (Richards & Blanchette, 2004; see Bar-Haim, Lamy,
Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007, for
a
review), visual search (e.g., Derakshan & Koster, 2010), and
antisaccade tasks (e.g., Derakshan, Ansari, Hansard, Shoker, &
Eysenck, 2009; see Berggren & Derakshan, 2013, for a review).
More recently, however, trait susceptibility to worry was asso-
ciated with the inefficient filtering of irrelevant threat-related

in-
formation from visual working memory (Stout, Shackman, John-
son, & Larson, 2014), which may contribute to poor processing
efficiency in anxiety. Sussman, Heller, Miller, and Mohanty
(2013) found that negative stimuli impaired task-relevant
process-
ing compared with neutral and positive distractors, and this was
magnified as worry, rather than trait anxiety (TA), increased.
However, recent work using a color-singleton visual search task
with neutral distractors found that TA, rather than worry per se,
underpinned the attentional control deficit (Moser, Becker, &
Moran, 2012).
Given that worry is closely related to impaired attentional con-
trol in anxiety, individual variation in trait worry and variation
of
concurrent task demands may moderate neural reactivity. How-
ever, evidence for the effect of worry on neural activation is
limited, and previous attempts to manipulate the effect of task
This article was published Online First March 16, 2015.
Max Owens, Nazanin Derakshan, and Anne Richards,
Department of
Psychological Sciences, Birkbeck University of London.
Max Owens is now at Department of Psychological Sciences,
Bingham-
ton University, State University of New York. Nazanin
Derakshan is now
at the Department of Experimental Psychology, University of
Oxford.
This work was supported by a British Academy grant
(SG112866)
awarded to Nazanin Derakshan and Anne Richards.

Nazanin
Derakhshan or Anne Richards, Affective and Cognitive Control
Lab,
Department of Psychological Sciences, Birkbeck University of
London,
Malet Street, London, WC1E 7HX, UK. E-mail:
[email protected]
or [email protected]
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e

A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s

al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no

t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
Emotion © 2015 American Psychological Association
2015, Vol. 15, No. 5, 544 –549 1528-3542/15/$12.00
http://dx.doi.org/10.1037/emo0000052
544
demands on performance in the presence of distracting
emotional
stimuli have been inconsistent (Pessoa, Kastner, & Ungerleider,
2002). For instance, whereas some studies have reported

increased
interference in anxiety from threat under high cognitive load
(Judah, Grant, Lechner, & Mills, 2013), others have found no
specific relationship (Berggren, Richards, Taylor, & Derakshan,
2013).
In the current study, we used a novel flanker task with
emotional
and neutral faces as distractors. We examined whether anxiety,
and, in particular, trait susceptibility to worry, modulated the
inhibition of the distractors in low- and high-working-memory-
load conditions. Neural activation in response to distractors was
measured using the negative-going conflict N2 event-related po-
tential. The conflict N2 is believed to reflect functional
activation
of the anterior cingulate cortex related to conflict monitoring
processes and cognitive control of working memory (Cavanagh
&
Shackman, in press; Yeung, Botvinick, & Cohen, 2004), in
which
working memory is conceived as a limited capacity system that
coordinates cognitive processes to regulate attention and guide
goal-directed behavior (Miller & Cohen, 2001).
We predicted that individuals characterized by high levels of
trait worry would be vulnerable to the influence of distractors,
as
reflected by increased N2 amplitudes, particularly under condi-
tions of high working memory load compared with low working
memory load, suggesting a greater recruitment of cognitive re-
sources to perform the task.
Method
Participants

A final sample of 31 participants (25 females) with normal or
corrected-to-normal vision (mean age � 23.25 years, range �
18
to 43) were analyzed (data from one participant were lost
because
of power failure). Before each session, self-report measures of
anxiety (State-Trait Anxiety Inventory, [STAI]; Spielberger,
Gor-
such, Lushene, Vagg, & Jacobs, 1983) and trait worry (the
Worry
Domains Questionnaire [WDQ]; Tallis, Eysenck, & Mathews,
1992; the Penn State Worry Questionnaire [PSWQ]; Meyer,
Miller, Metzger, & Borkovec, 1990) were completed. The STAI
consists of 40 items (20 state and 20 trait), and scores range
from
20 to 80 for both the state and trait anxiety measures. The WDQ
comprises 25 items on worries across different domains (i.e.,
Relationships, Confidence, Future, Work and Financial), and
scores range between 0 and 140. In contrast, the PSWQ
measures
a general tendency toward worry independent of specific
content
of the worrying thoughts. The PSWQ consists of 16 items with
scores ranging from 16 to 80. The WDQ and PSWQ are highly
correlated; however, items on the WDQ are believed to measure
task-orientated problem-solving strategies (Davey, 1993). Each
worry measure shows good internal consistency and retest
reliabil-
ity (Molina & Borkovec, 1994; Stöber, 1998; van Rijsoort, Em-
melkamp, & Vervaeke, 1999).
Materials and Procedure
The experiment was programmed using DMDX software (For-
ster & Forster, 2003) on a Dell Opitplex GX520 with a 17-in.

LCD
(refresh rate � 60 Hz). We used a modified version of Erikson
Flanker task (Eriksen & Eriksen, 1974), in which the central
target
was either a neutral male or female face, flanked by two
distractor
faces of the opposite gender on either side (see Figure 1).
Distrac-
tor faces were angry, happy, or neutral. Participants decided if
the
central target face was male or female by pressing one of two
buttons. Forty-eight angry, happy, and neutral male and female
faces, divided equally between gender and emotional
expression,
were chosen from the Karolinska Directed Emotional Faces set
(Lundqvist, Flykt, & Öhman, 1998). Using MATLAB, faces
were
trimmed of all nonfacial features, converted to 8-bit grayscale,
and
matched for luminance and contrast.
Cognitive load was manipulated by presenting an auditory tone
of high, medium, or low pitch played simultaneously with the
faces (cf. Berggren et al., 2013). Within every trial, participants
heard a tone, with the only difference between conditions being
the
instructions given. For high load, participants were instructed to
remember the pitch of the tone, and then verbally report, after
presentation of the faces, whether the tone was “high,” “mid,”
or
“low.” In the low-load condition, participants were merely
prompted to say “tone.” During the task, participants’ verbal
responses were monitored by microphone to ensure the task was
performed adequately. There were five blocks of low-load and
five
blocks of high-load trials presented separately, each containing

96
Figure 1. An example of a high-load trial with negative
distractors. KDEF faces (AF07 and BM07; Lundqvist
et al., 1998) are reprinted with permission from the Karolinska
Institutet. During presentation of the faces,
participants responded with a button press to indicate the gender
of the target face (center) and were instructed
to remember the pitch of the tone. Participants were told faces
flanking either side of the target face were
irrelevant to the task and should be ignored.
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of

it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so

le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d

is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
545TRAIT SUSCEPTIBILITY TO WORRY
trials, with block order counterbalanced across participants.
Target
faces (neutral male or female) and distractors (angry, happy,
neutral) were randomized and presented equally within each
block.
Each trial began with a central fixation for a randomly deter-
mined interval between 500 ms and 1,000 ms. The row of faces
(target � distractors) and the tone were presented

simultaneously,
with the faces remaining on the screen for 1,000 ms or until a
response was made. A prompt screen for the tone response then
appeared and remained for 1,000 ms. Each experimental session
lasted about 150 min. Participants were debriefed and received
course credit for their contribution.
ERP recording. Participants sat in an electrically isolated,
soundproof room with dimmed lighting. Electroencephalogra-
phy (EEG) was recorded using 64 Ag/AgCl sintered ring elec-
trodes mounted on a fitted cap (EASYCAP) according to the
International 10/20 system. Horizontal eye movements were
recorded from two electrodes placed 1 cm to the left and right
of the external canthi. Eyeblinks were recorded from a single
electrode placed below the left eye. Electrode impedances were
below 10 k� during testing. EEG data were recorded referenced
to the left mastoid, and rereferenced offline to the mean of the
left and right mastoids (average mastoids). EEG recordings
were amplified and filtered with a BrainAmp standard model
amplifier (Gain: 1,000) with a band-pass at 0.01 to 80 Hz and
sampled at 250 Hz.
EEG processing. Data was processed offline using the MAT-
LAB extension EEGLAB (Delorme & Makeig, 2004) and with
the
ERPLAB plugin (Lopez-Calderon & Luck, 2010). The data were
low-pass filtered at 30 Hz. Independent component analysis
(ICA)
was first conducted to identify stereotypical ocular, muscle, and
noise components (Jung et al., 2001). Artifact detection and
rejec-
tion was then conducted on epoched uncorrected data files.
Trials
with ocular artifacts at stimulus presentation (blinks or
saccades)
were removed from both behavioral and ICA-corrected

continuous
data. No participant lost more than 30% of trials during artifact
rejection, so all were included in analyses (M � 871, SD � 81).
The number of usable trials across participants was uncorrelated
with anxiety and worry (all ps � .1).
ERP analysis ERP waveforms were time locked to target pre-
sentation with a 100-ms baseline. EEG activity for each
distractor
(angry, happy, neutral) by load (low, high) was averaged sepa-
rately for the N2 (215 ms to 275 ms) component. ERP data was
averaged across 12 frontal electrode sites (Fp1, Fp2, F3, F4, Fz,
FC1, FC2, FCz, F1, F2, FC3, and FC4).
Results
Reaction time and accuracy data (proportion of correct re-
sponses) were analyzed in two separate ANOVAs with Load
(low,
high) and Distractor Type (angry, happy, neutral) as within-
subject
factors. Mean N2 amplitudes were analyzed using a two-way
ANOVA with distractor type (angry, happy, neutral) and load
(low, high) as within-subject variables. The Greenhouse-Geisser
correction was applied when appropriate.
Behavioral Performance
Performance was more accurate in low-load (M � .90, SD �
.08) relative to high-load (M � .80, SD � .11) trials, F(1, 30) �
39.32, p � .001, �p2 � .57. No other effects were significant,
ps �
.2. Levels of anxiety (trait, state) and worry (WDQ, PSWQ)
were
not correlated with accuracy data, ps � .1. Responses were

faster
under low (M � 688, SD � 47) relative to high (M � 744, SD �
37) load, F(1, 30) � 78.47, p � .001, �p2 � .72. There were no
other significant effects (Fs � 2.24). Levels of anxiety (trait,
state)
and worry (WDQ, PSWQ) were not correlated with reaction-
time
data, ps � .1.
N2 Analysis
A main effect of load for was observed, F(1, 30) � 4.05, p �
.05, �p2 � .12, with greater N2 amplitudes under high load
(M � �3.97, SD � 2.15) compared with low load (M � �3.33,
SD � 2.58; see Figure 2a). There was no main effect of
distractor type or interaction between load and distractor type,
Fs � 1.
Trait worry (WDQ) correlated with the N2 amplitude under
high, r(31) � �.38, p � .04, but not low, r(31) � �.17, p �
.35, load, and these correlations were marginally significantly
different, t(28) � 1.63, p � .055, one-tailed. The cost of load
(high load – low load) on the N2 amplitude was calculated.
Load
cost was correlated with WDQ scores such that higher levels of
worry on the WDQ were associated with greater N2 amplitudes
for
load cost, r(31) � �.34, p � .06. Next, a linear regression was
performed with WDQ, generalized trait worry (PSWQ), and TA
entered simultaneously in the model, with N2 load cost as the
dependent variable. The WDQ significantly predicted N2 load
cost, � �.67, t(27) � �2.67, p � .01 (see Figure 2b).
Con-
versely, neither PSWQ, � .30, t(27) � 1.21, p � .2, nor TA,
�
.19, t(27) � 1.21, p � .4, affected N2 load cost. Results suggest

a
specific modulatory role for WDQ on N2 amplitudes across
load.
The relationship between load cost on the N2 and WDQ was
then
examined for each distractor type and found to be particularly
pronounced for angry distractors, r � �.34, p � .06, rather than
happy (r � �.27, p � .14) and neutral (r � �.29, p � .11)
distractors, suggesting that higher levels of trait worry were
asso-
ciated with marginally greater N2 amplitudes in the inhibition
of
angry distractors in high-load conditions.
Discussion
The main findings of the current study are twofold. First,
under high cognitive load, greater N2 amplitudes were observed
relative to low-load trials, suggesting that greater attentional
control resources were needed to inhibit the effect of the dis-
tracting flankers. Second, trait susceptibility to worry, as mea-
sured by the WDQ, modulated the distracting effect of the
flankers on the N2 amplitude. Specifically, greater levels of
trait worry were associated with enhanced N2 amplitudes under
high, but not low, cognitive load. The modulating effect of
worry (as assessed by the WDQ) on the N2 amplitude remained
after partialing out for the effects of TA and a more general
measure of worry (PSWQ), suggesting that the worry compo-
nent of anxiety as measured by the WDQ alone is driving this
association. Importantly, in the absence of a modulation of
worry on behavioral performance, findings of increased N2
amplitudes, under high cognitive load, suggest that worry can
lead to a compensatory mechanism that necessitates the greater
use of cognitive resources toward accomplishing the task goal.
T

hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy

ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he

rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on

al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in

at
ed
br
oa
dl
y.
546 OWENS, DERAKSHAN, AND RICHARDS
Our findings support attentional control theory’s (Eysenck et
al., 2007) prediction that trait susceptibility to worry is related
to adverse effects on performance and processing efficiency on
tasks imposing substantial demands on the processing and stor-
age capacity of working memory. The current results add to the
growing literature suggesting that worry interferes with the
recruitment of working memory functions by reducing process-
ing efficiency (Hirsch & Mathews, 2012). They also extend
recent evidence that shows trait vulnerability to worry is asso-
ciated with inefficient filtering of task-irrelevant threat distrac-
tors from working memory (Stout et al., 2014). The WDQ is
believed to measure aspects of worry linked to attempts at
task-orientated problem solving (e.g., Davey, 1993). Our find-
ings suggest that although such attempts may preserve ongoing
behavioral performance, trait worry, as measured by the WDQ,
may also be associated with increased cognitive interference
and effort. In this view, maladaptive attempts to regulate atten-
tion in worry, as in the related construct of depressive rumina-
tion, may help fuel a cycle of cognitive dysfunction and nega-
tive biases (cf. Nolen-Hoeksema, Wisco, & Lyubomirsky
2008). There was only a trend for a specific effect of worry on

the inhibition of threat-related distractors in the current study,
-8.00
-4.00
0.00
4.00
8.00
-100 0 100 200 300
M
ea
n
A
m
p
lit
u
d
e
μ
V
Grand averaged ERPs by load
Analysis Range low load high load
-4

-2
0
2
4
-30 -20 -10 0 10 20 30
N
2
lo
ad
c
o
st
WDQ
Residual correlation plot of WDQ
and N2 load cost
n = 27
par�al r² = 0.21
p =.01

low load
high load
-5 - 5μV
Figure 2. Top Left (2a): Grand average ERP waveforms time
locked to face sets for high-load and low-load
trials. Top Right: Topographical scalp map for the N2 for high-
load and low-load trials. Waveforms and maps
are averaged across frontal electrode sites: Fp1, Fp2, F3, F4,
Fz, FC1, FC2, FCz, F1, F2, FC3, FC4. Highlighted
regions and maps show the measurement window for the N2
(215-275 ms). Bottom (2b); Partial regression for
Worry Domains Questionnaire (WDQ) and N2 load cost (high
load minus low load) with regression line.
T
hi
s
do
cu
m
en
t

di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.

and clearly more work is needed. Future research should there-
fore elucidate the mechanisms by which threatening informa-
tion interferes with the recruitment of cognitive processes of
control under high task demands. In conclusion, the current
study adds to the growing evidence that worry impairs process-
ing efficiency through a compensatory mechanism that aims to
protect availability of cognitive resources toward goal-directed
behavior.
References
Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg,
M. J., &
van IJzendoorn, M. H. (2007). Threat-related attentional bias in
anxious
and nonanxious individuals: A meta-analytic study.
Psychological Bul-
letin, 133, 1–24. http://dx.doi.org/10.1037/0033-2909.133.1.1
Basten, U., Stelzel, C., & Fiebach, C. J. (2011). Trait anxiety
modulates the
neural efficiency of inhibitory control. Journal of Cognitive
Neurosci-
ence, 23, 3132–3145.
Basten, U., Stelzel, C., & Fiebach, C. J. (2012). Trait anxiety
and the neural
efficiency of manipulation in working memory. Cognitive,
Affective &
Behavioral Neuroscience, 12, 571–588.
s13415-012-0100-3

Berggren, N., & Derakshan, N. (2013). Attentional control
deficits in trait
anxiety: Why you see them and why you don’t. Biological
Psychology,
92, 440 – 446.
http://dx.doi.org/10.1016/j.biopsycho.2012.03.007
Berggren, N., Richards, A., Taylor, J., & Derakshan, N. (2013).
Affective
attention under cognitive load: Reduced emotional biases but
emergent
anxiety-related costs to inhibitory control. Frontiers in Human
Neuro-
science, 7, 188. http://dx.doi.org/10.3389/fnhum.2013.00188
Cavanagh, J. F., & Shackman, A. J. (in press). Frontal midline
theta reflects
anxiety and cognitive control: Meta-analytic evidence. Journal
of Physi-
ology–Paris.
Davey, G. C. (1993). A comparison of three worry
questionnaires. Behav-
iour Research and Therapy, 31, 51–56.
http://dx.doi.org/10.1016/0005-
7967(93)90042-S
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source
toolbox for
analysis of single-trial EEG dynamics including independent
component
analysis. Journal of Neuroscience Methods, 134, 9 –21.
Derakshan, N., Ansari, T. L., Hansard, M., Shoker, L., &
Eysenck, M. W.
(2009). Anxiety, inhibition, efficiency, and effectiveness. An

investiga-
tion using antisaccade task. Experimental Psychology, 56, 48 –
55. http://
dx.doi.org/10.1027/1618-3169.56.1.48
Derakshan, N., & Koster, E. H. W. (2010). Processing
efficiency in
anxiety: Evidence from eye-movements during visual search.
Behaviour
Research and Therapy, 48, 1180 –1185.
http://dx.doi.org/10.1016/j.brat
.2010.08.009
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters
upon
identification of a target letter in a non-search task. Perception
&
Psychophysics, 16, 143–149.
http://dx.doi.org/10.3758/BF03203267
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G.
(2007).
Anxiety and cognitive performance: Attentional control theory.
Emo-
tion, 7, 336 –353. http://dx.doi.org/10.1037/1528-3542.7.2.336
Forster, K. I., & Forster, J. C. (2003). DMDX: A windows
display program
with millisecond accuracy. Behavior Research Methods,
Instruments &
Computers, 35, 116 –124.
http://dx.doi.org/10.3758/BF03195503
Grupe, D. W., & Nitschke, J. B. (2013). Uncertainty and
anticipation in
anxiety: An integrated neurobiological and psychological

perspective.
Nature Reviews Neuroscience, 14, 488 –501.
nrn3524
Hirsch, C. R., & Mathews, A. (2012). A cognitive model of
pathological
worry. Behaviour Research and Therapy, 50, 636 – 646.
http://dx.doi
.org/10.1016/j.brat.2012.06.007
Judah, M. R., Grant, D. M., Lechner, W. V., & Mills, A. C.
(2013).
Working memory load moderates late attentional bias in social
anxiety.
Cognition and Emotion, 27, 502–511. http://dx.doi.org/10.1080/
02699931.2012.719490
Jung, T. P., Makeig, S., McKeown, M. J., Bell, A. J., Lee, T.
W., &
Sejnowski, T. J. (2001). Imaging brain dynamics using
independent
component analysis. Proceedings of the IEEE, 89, 1107–1122.
Kanske, P., & Kotz, S. A. (2012). Effortful control, depression,
and anxiety
correlate with the influence of emotion on executive attentional
control.
Biological Psychology, 91, 88 –95.
http://dx.doi.org/10.1016/j.biopsycho
.2012.04.007
Lopez-Calderon, J., & Luck, S. J. (2010). ERPLAB (version
1.0.0.33a)
[Computer software]. UC-Davis Center for Mind & Brain.

Retrieved
from http://erpinfo.org/erplab/erplab-download
Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska
Directed
Emotional Faces – KDEF, CD ROM from Department of
Clinical
Neuroscience, Psychology section: Karolinska Institutet, ISBN
91-630-
7164-9.
Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D.
(1990).
Development and validation of the Penn State Worry
Questionnaire.
Behaviour Research and Therapy, 28, 487– 495.
0005-7967(90)90135-6
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of
prefrontal
cortex function. Annual Review of Neuroscience, 24, 167–202.
http://dx
.doi.org/10.1146/annurev.neuro.24.1.167
Molina, S., & Borkovec, T. D. (1994). Worrying: Perspectives
on theory,
assessment and treatment. In G. C. L. Davey & F. Tallis (Eds.),
Wiley
series in clinical psychology (pp. 265–283). Oxford, United
Kingdom:
Wiley.
Moser, J. S., Becker, M. W., & Moran, T. P. (2012). Enhanced
attentional
capture in trait anxiety. Emotion, 12, 213–216.

a0026156
Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008).
Rethinking
rumination. Perspectives on psychological science, 3, 400 –
424.
Pessoa, L., Kastner, S., & Ungerleider, L. G. (2002).
Attentional control of
the processing of neural and emotional stimuli. Cognitive Brain
Re-
search, 15, 31– 45. http://dx.doi.org/10.1016/S0926-
6410(02)00214-8
Richards, A., & Blanchette, I. (2004). Independent manipulation
of emo-
tion in an emotional Stroop task using classical conditioning.
Emotion,
4, 275–281. http://dx.doi.org/10.1037/1528-3542.4.3.275
Righi, S., Mecacci, L., & Viggiano, M. P. (2009). Anxiety,
cognitive
self-evaluation and performance: ERP correlates. Journal of
Anxiety
Disorders, 23, 1132–1138.
http://dx.doi.org/10.1016/j.janxdis.2009.07
.018
Sehlmeyer, C., Konrad, C., Zwitserlood, P., Arolt, V.,
Falkenstein, M., &
Beste, C. (2010). ERP indices for response inhibition are related
to
anxiety-related personality traits. Neuropsychologia, 48, 2488 –
2495.
Retrieved from

http://dx.doi.org/10.1016/j.neuropsychologia.2010.04
.022
Spielberger, C. C., Gorsuch, R. L., Lushene, R., Vagg, P. R., &
Jacobs,
G. A. (1983). Manual for the state-trait anxiety inventory. Palo
Alto,
CA: Consulting Psychologists Press.
Stöber, J. (1998). Reliability and validity of two widely-used
worry ques-
tionnaires: Self-report and self-peer convergence. Personality
and Indi-
vidual Differences, 24, 887– 890.
http://dx.doi.org/10.1016/S0191-
8869(97)00232-8
Stout, D. M., Shackman, A. J., Johnson, J. S., & Larson, C. L.
(2014).
Worry is associated with impaired gating of threat from working
mem-
ory. Emotion. Advance online publication.
emo0000015
Sussman, T. J., Heller, W., Miller, G. A., & Mohanty, A.
(2013). Emo-
tional distractors can enhance attention. Psychological Science,
24,
2322–2328. http://dx.doi.org/10.1177/0956797613492774
Tallis, F., Eysenck, M., & Mathews, A. (1992). A questionnaire
for the
measurement of non-pathological worry. Personality and
Individual

T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P

sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is

he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs

on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em

in
at
ed
br
oa
dl
y.
548 OWENS, DERAKSHAN, AND RICHARDS
http://dx.doi.org/10.1037/0033-2909.133.1.1
http://dx.doi.org/10.3758/s13415-012-0100-3
http://dx.doi.org/10.3758/s13415-012-0100-3
http://dx.doi.org/10.3389/fnhum.2013.00188
http://dx.doi.org/10.1016/0005-7967%2893%2990042-S
http://dx.doi.org/10.1016/0005-7967%2893%2990042-S
http://dx.doi.org/10.1027/1618-3169.56.1.48
http://dx.doi.org/10.1027/1618-3169.56.1.48
http://dx.doi.org/10.1016/j.brat.2010.08.009
http://dx.doi.org/10.3758/BF03203267
http://dx.doi.org/10.1037/1528-3542.7.2.336
http://dx.doi.org/10.3758/BF03195503
http://dx.doi.org/10.1038/nrn3524
http://dx.doi.org/10.1038/nrn3524
http://dx.doi.org/10.1080/02699931.2012.719490
http://dx.doi.org/10.1080/02699931.2012.719490
http://erpinfo.org/erplab/erplab-download

http://dx.doi.org/10.1016/0005-7967%2890%2990135-6
http://dx.doi.org/10.1016/0005-7967%2890%2990135-6
http://dx.doi.org/10.1146/annurev.neuro.24.1.167
http://dx.doi.org/10.1146/annurev.neuro.24.1.167
http://dx.doi.org/10.1037/a0026156
http://dx.doi.org/10.1037/a0026156
http://dx.doi.org/10.1016/S0926-6410%2802%2900214-8
http://dx.doi.org/10.1037/1528-3542.4.3.275
http://dx.doi.org/10.1016/j.janxdis.2009.07.018
http://dx.doi.org/10.1016/j.janxdis.2009.07.018
http://dx.doi.org/10.1016/j.neuropsychologia.2010.04.022
http://dx.doi.org/10.1016/j.neuropsychologia.2010.04.022
http://dx.doi.org/10.1016/S0191-8869%2897%2900232-8
http://dx.doi.org/10.1016/S0191-8869%2897%2900232-8
http://dx.doi.org/10.1177/0956797613492774
Differences, 13, 161–168. http://dx.doi.org/10.1016/0191-
8869(92)
90038-Q
van Rijsoort, S., Emmelkamp, P., & Vervaeke, G. (1999). The
Penn State
Worry Questionnaire and the Worry Domains Questionnaire:
Structure,
reliability and validity. Clinical Psychology & Psychotherapy,
6, 297–
307. http://dx.doi.org/10.1002/(SICI)1099-
0879(199910)6:4�297::
AID-CPP206�3.0.CO;2-E
Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural
basis of
error detection: Conflict monitoring and the error-related

negativity.
Psychological Review, 111, 931–959.
http://dx.doi.org/10.1037/0033-
295X.111.4.931
Received July 3, 2014
Revision received November 17, 2014
Accepted December 4, 2014 �
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th

e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it

s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le

ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is

no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
http://dx.doi.org/10.1016/0191-8869%2892%2990038-Q
http://dx.doi.org/10.1016/0191-8869%2892%2990038-Q
http://dx.doi.org/10.1002/%28SICI%291099-
0879%28199910%296:4%3C297::AID-CPP206%3E3.0.CO;2-E
http://dx.doi.org/10.1002/%28SICI%291099-
0879%28199910%296:4%3C297::AID-CPP206%3E3.0.CO;2-E
http://dx.doi.org/10.1037/0033-295X.111.4.931
http://dx.doi.org/10.1037/0033-295X.111.4.931Trait
Susceptibility to Worry Modulates the Effects of Cognitive
Load on Cognitive Control: An E
...MethodParticipantsMaterials and ProcedureERP
recordingEEG processingResultsBehavioral PerformanceN2

AnalysisDiscussionReferences
Divided Attention Can Enhance Memory Encoding: The
Attentional Boost
Effect in Implicit Memory
Pietro Spataro
Sapienza University of Rome
Neil W. Mulligan
University of North Carolina at Chapel Hill
Clelia Rossi-Arnaud
Sapienza University of Rome
Distraction during encoding has long been known to disrupt
later memory performance. Contrary to this
long-standing result, we show that detecting an infrequent target
in a dual-task paradigm actually
improves memory encoding for a concurrently presented word,
above and beyond the performance
reached in the full-attention condition. This absolute facilitation
was obtained in 2 perceptual implicit
tasks (lexical decision and word fragment completion) but not
in a conceptual implicit task (semantic
classification). In the case of recognition memory, the
facilitation was relative, bringing accuracy in the
divided attention condition up to the level of accuracy in the
full attention condition. The findings follow
from the hypothesis that the attentional boost effect reflects
enhanced visual encoding of the study
stimulus consequent to the transient orienting response to the
dual-task target.

Keywords: implicit memory, divided attention, attention and
memory, repetition priming
The deleterious effects of distraction on memory encoding have
been amply documented from the earliest days of psychological
research (see Mulligan, 2008, for review). A common
experimen-
tal technique uses the dual-task paradigm, in which memory en-
coding is carried out under full-attention (FA) or divided-
attention
(DA) conditions (i.e., while simultaneously carrying out a
second
task designed to compete for attentional resources). The results
of
numberless studies make it abundantly clear that DA during en-
coding degrades later memory on tests such as recognition, free
recall, and cued recall (e.g., Craik et al., 1996; Mulligan, 1998,
2008).
Recently, Swallow and Jiang (2010) reported a surprising twist
on the usual adverse effects of DA. In these experiments, partic-
ipants studied a sequence of pictures, each with a small square
superimposed at the center. In the DA condition, participants
were
instructed to remember all of the images and to monitor the
color
of the square, pressing the space bar whenever they detected an
infrequent white square (targets) among frequent black squares
(distractors). In the FA condition, participants were told to
ignore
the squares and to focus only on encoding the pictures. When
memory for the pictures was later tested in a four-choice
recogni-
tion task, Swallow and Jiang (2010) found that in the DA condi-
tion, the images encoded together with the target squares (i.e.,

corresponding to the press response) were recognized
significantly
better than were those encoded with the distractor squares (the
attentional boost effect). In the FA condition, in which the
partic-
ipants made no response to the squares, no attentional boost
effect
was found. What is importantly for present purposes is that the
attentional boost effect was relative: For pictures accompanied
by
the more frequent black squares (the distractor trials), the DA
condition produced worse picture memory than did the FA
condi-
tion, a typical DA effect on memory encoding. For pictures ac-
companied by the target (white) squares, picture memory was
equal in the two attention conditions, which indicated the elimi-
nation of the DA effect. Thus, the attentional boost effect
reported
by Swallow and Jiang (2010) was a relative boost in memory
encoding, bringing the typically poor memory produced by DA
up
to the level of the FA condition.
In later studies, the authors ruled out a number of potential
accounts of the effect based on attentional cuing, reinforcement
learning, perceptual grouping, oddball processing and
distinctive-
ness. Rather, Swallow and Jiang (2011, 2012) concluded, the
attentional boost effect reflects enhanced visual encoding pro-
duced by the opening of an attentional gate consequent to the
transient orienting responses triggered by the detection of target
squares. According to the event segmentation theory (Zacks,
Speer, Swallow, Braver, & Reynolds, 2007), this gating mecha-
nism would be implemented in subcortical regions like the locus
coeruleus or the nucleus basalis and is involved in alerting the
observer to salient environmental changes. Once a modification

of
the stimulus’ properties occurs (e.g., the color of the target
squares
becomes red), the gating mechanism is activated, resulting in
increased attention to the perceptual properties of the
concurrently
presented images, as well as in the updating of their internal
representations. In other words, the attentional gate postulated
by
Swallow and Jiang (2010) would act as a filter to sensory input,
This article was published Online First January 28, 2013.
Pietro Spataro, Department of Psychology, Sapienza University
of
Rome, Rome, Italy; Neil W. Mulligan, Department of
Psychology, Uni-
versity of North Carolina at Chapel Hill; Clelia Rossi-Arnaud,
Department
of Psychology, Sapienza University of Rome.
Neil W.
Mulligan, Department of Psychology, University of North
Carolina, Cha-
pel Hill, NC 27599-3270. E-mail: [email protected]
T
hi
s
do
cu
m
en

t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A

ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar

ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e

in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.

Journal of Experimental Psychology:
Learning, Memory, and Cognition
© 2013 American Psychological Association
2013, Vol. 39, No. 4, 1223–1231
0278-7393/13/$12.00 DOI: 10.1037/a0030907
1223
http://dx.doi.org/10.1037/a0030907
facilitating the perceptual processing of the information
encoded
together with dual-task targets.
Consideration of the attentional boost effect in tandem with
research on implicit memory raises the possibility of an
absolute,
positive effect of DA on later memory. Such a result would be
quite surprising given that the presence of a second task at
encod-
ing virtually always reduces (or, in rare instances, merely fails
to
reduce) later memory. Enhanced memory encoding under DA
would be unprecedented and important to document.
Implicit memory refers to unconscious or unintentional retrieval
and is typically assessed on tests of repetition priming, such as
lexical decision or word-fragment completion, whereas explicit
memory refers to conscious or intentional retrieval reflected in
standard memory tests such as free recall and recognition. In
general, DA during encoding produces much weaker effects on

implicit memory than explicit memory (Mulligan & Brown,
2003;
Spataro, Cestari, & Rossi-Arnaud, 2011). Although implicit
mem-
ory is affected by DA under some conditions (e.g., depending
on
the type of implicit test, the type of dependent measure, or the
precise nature of the dual task; Mulligan & Peterson, 2008),
there
are numerous examples in which a dual task that impaired later
explicit memory left implicit memory unaffected (Mulligan,
Duke,
& Cooper, 2007; Parkin, Reid, & Russo, 1990). In general, re-
search on attention and implicit memory has found that dual
tasks
with infrequent response selection typically fail to affect
implicit
memory encoding despite disrupting explicit memory encoding
(Mulligan, 2003).
In the present study, we first reproduced the attentional boost
effect in a four-choice recognition test (explicit memory), using
words instead of pictures. Then we examined the effect in two
different perceptual implicit tasks (lexical decision and word-
fragment completion). We contrasted a DA condition, in which
participants read aloud a series of words and concurrently moni-
tored the color (red or green) of a small circle placed below
each
word, with a FA condition, in which participants read the words
but ignored the circles. In agreement with Swallow and Jiang
(2010), our approach was to consider the attentional boost
effect as
the combination of two related factors: a primary task
facilitation
as a consequence of increased attention and perceptual
processing

of the words due to target detection, plus a primary task
interfer-
ence as a consequence of the attentional demands associated
with
monitoring the circles to determine if a target is present. In the
case
of implicit memory, the mere monitoring required by the dual
task
is unlikely to reduce later implicit memory (Mulligan, 2003;
Mulligan et al., 2007). This implies that, for words encoded
with
green (distractor) circles, the FA and DA conditions should pro-
duce comparable amounts of priming. In contrast, for words en-
coded with red (target) circles, priming should be significantly
greater in the DA condition than in the FA condition, because
the
facilitating effect due to the detection of the infrequent red
circles
should overcome any small attentional interference produced by
the dual task. An alternative possibility stems from the
automatic-
ity hypothesis (Aloisi, McKone, & Heubeck, 2004; see Lozito &
Mulligan, 2010, for discussion), which states that implicit
memory
is the result of involuntary encoding processes. According to
this
account, any variation in attentional levels should not influence
implicit memory; thus, the attentional boost effect should not be
observed either in lexical decision or in the word-fragment com-
pletion task.
Experiment 1
Experiment 1 reproduced the attentional boost effect in a four-
choice recognition task, using words instead of pictures. In
addi-

tion, Swallow and Jiang (2010) repeated the presentation of the
to-be-remembered images 10 times, whereas in the present
exper-
iment, the study words were shown only once. Experiment 1
verifies that the attentional boost effect is observed with verbal
material and without an extended learning process.
Method
Participants. Thirty-six students from Sapienza University of
Rome participated (27 women, mean age � 24.6 years).
Materials and procedure. The critical items were 30 words,
seven to nine letters in length, from the LexVar database
(Barca,
Burani, & Arduino, 2002). They were divided into two lists of
15
words each (A and B), matched on several variables, including
length in letters (M � 7.80 vs. M � 7.73), written frequency (M
�
74.47 vs. M � 79.33), age of acquisition (M � 4.21 vs. M �
4.13),
familiarity (M � 6.01 vs. M � 6.13), imageability (M � 5.22
vs.
M � 5.42), and concreteness (M � 5.88 vs. M � 6.08). The
mean
values of age of acquisition, familiarity, imageability, and con-
creteness were obtained from the LexVar database, as measured
on
7-point Likert-type scales, whereas the estimates of written fre-
quency were taken from the CoLFIS Vocabulary (which
includes
over 3 million occurrences; Laudanna, Thornton, Brown,
Burani,
& Marconi, 1995). An additional set of 120 words of medium
frequency (between 50 and 100 occurrences) were selected and

used as filler items during the study phase.
In the DA condition, participants were told to study (and read
aloud) each word while simultaneously monitoring the color of
a
small circle immediately below the word. Participants were in-
structed to press the space bar whenever they saw an infrequent
red
circle among more frequent green circles. A total of 150 words
were presented during the encoding phase, at a rate of 500 ms
per
word. The study procedure was modeled on Swallow and Jiang
(2010). On each trial, one word (Times New Roman, 44 points)
and one circle (red or green; 1 cm in diameter) appeared
simulta-
neously at the center of the screen for 100 ms, with a vertical
distance of 1 cm between them, after which only the word re-
mained visible for an additional 400 ms. There was no
interruption
between successive trials. The study list was constructed of 16
blocks of seven items (15 critical blocks preceded by one
practice
block). Critical words encoded with red circles (and associated
with a press response) were always located in the fourth
position.
Critical words encoded with green circles were placed either in
the
third position or in the fifth position; this choice followed from
the fact that in the study by Swallow and Jiang (2010), memory
for
the stimuli presented with distractor squares did not vary across
encoding positions. For simplicity, the critical words presented
on
target trials (i.e., with red circles) are referred to as target
words
and the critical words presented on distractor trials (i.e., with

green
circles) are referred to as distractor words. However, it should
be
noted that words on all trials were to be identified. The
designation
of target and distractor words refers to whether the word co-
occurred with a target or a distractor on the dual (circle
detection)
task.
In sum, only two critical words were presented per block,
yielding a total of 30 critical words across all blocks (15
encoded
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d

by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e

of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed

so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an

d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
1224 SPATARO, MULLIGAN, AND ROSSI-ARNAUD
with red circles [target words] and 15 encoded with green
circles
[distractor words]). For half of the participants in each
condition,
eight distractor words were located in the third position, while
the
other seven were placed in the fifth position; for the remaining

participants, the pattern was reversed. All other words in a
block
were noncritical filler words and were accompanied by green
circles. One to five additional filler words, always presented
with
green circles, were randomly located between blocks to reduce
the
temporal regularity of the target trials. It should be noted that
the
block organization of the study list was not apparent to the par-
ticipant; all trials were presented as one continuous list. List A
and
List B words were counterbalanced across participants, so they
had
the same probability to be encoded with red and green circles.
The
FA condition was identical except that participants were told to
ignore the circles.
After a 5-min distractor task (arithmetic problems), a four-
choice recognition task was administered. There were a total of
30
quadruplets, one for each critical word. Each trial included a
fixation point for 500 ms, a test slide including four words that
remained visible until response, and a 1,500-ms pause. The four
words were arranged in different quadrants of the screen, with
the
studied word being located equally often in each quadrant. Two
foils were semantically unrelated to the studied word, while the
third foil was a semantic associate (e.g., for the studied word
hospital, the semantic associate was surgeon). For each studied
word, the corresponding foils were selected from the LexVar
database to be matched as closely as possible in terms of length
and written frequency (range: 20 –100 occurrences; M � 73.86).
Such a manipulation was used to mimic as closely as possible
the

original study by Swallow and Jiang (2010), where two of the
four
test images were similar scenes. Participants were required to
select the words studied during the encoding phase. For the pur-
pose of scoring, the choice of the semantic associates was
consid-
ered an error throughout.
Results and Discussion
During the encoding phase, participants correctly detected
98.2% of the red circles (mean response time � 419.9 ms). In
the
present experiment, as well as in the following ones, care was
taken to ensure that participants read aloud all the words, so
that
identification accuracy was always 100%. The proportions of
studied words correctly recognized (Figure 1) were submitted to
a
2 (word type: target words [encoded with red circles] vs.
distractor
words [encoded with green circles]) � 2 (attentional condition:
FA
vs. DA) mixed analysis of variance (ANOVA), in which word
type
was manipulated within subject and attentional condition was
manipulated between subjects. Results revealed (a) a significant
effect of word type, F(1, 34) � 9.41, mean square error (MSE)
�
0.024, p � .004, �2 � 0.22, indicating that target words were
recognized better than distractor words were (M � 0.64 vs. M �
0.53), and (b) a significant interaction, F(1, 34) � 6.55, MSE �
0.024, p � .015, �2 � 0.16. The effect of attentional condition
was
not significant, F(1, 34) � 1.12, p � .297. An analysis of simple
effects showed the typical attentional boost effect: In the DA

condition, accuracy was greater for target words than for
distractor
words, F(1, 34) � 15.82, p � .000, �2 � 0.32, whereas no
difference was found in the FA condition, F(1, 34) � 0.13, p �
.722). It is important to note that the attentional boost effect
was
relative, because it enhanced memory for target words in the
DA
condition to the same level of the FA condition, F(1, 34) �
1.24,
p � .272; in contrast, distractor words were recognized more
accurately in the FA condition than in the DA condition, F(1,
34) � 8.19,
p � .007, �2 � 0.19, replicating the usual negative effect of a
secondary task during encoding on explicit memory. The results
did not change when the performance for the target words was
conditionalized on prior success at the study-phase detection
task
(not surprisingly given the very high detection rate): indeed, the
critical interaction between word type and attentional condition
remained significant, F(1, 34) � 6.12, MSE � 0.023, p � .019,
�2 � 0.15. Furthermore, the findings are not explained by
differ-
ences in the likelihood of (incorrectly) selecting the semantic
associates, because a mixed ANOVA with the same factors as
above showed no significant effects or interaction on the
propor-
tion of semantic associates chosen, F(1, 34) � 2.23, p � .14.
In summary, the results of Experiment 1 replicated the relative
boost effect observed by Swallow and Jiang (2010), using
verbal
material (instead of pictures) and a single encoding presentation
(instead of multiple presentations). Moreover, our data extend
to

long-term memory the suggestion that the detection targets need
not overlap in space with the to-be-remembered items to
produce
a significant facilitation (Makovski, Swallow, & Jiang, 2011).
Experiment 2
In Experiment 2, we examined the attentional boost effect in the
lexical decision task (LDT), a perceptual implicit test
characterized
by a strong resilience to the negative consequences of DA (Mul-
ligan & Peterson, 2008; Newell, Cavenett, & Andrews, 2008;
Spataro, Mulligan, & Rossi-Arnaud, 2011).
Method
Experiment 2 used the methods of Experiment 1 with the
following modifications. A new pool of 36 participants took
part
(21 women, mean age � 24.2 years). A set of 45 critical words,
seven to nine letters in length (including the 30 used in
Experiment
1), were divided into three sublists of 15 words each. The prop-
erties of the additional 15 words were similar to those
illustrated
for Experiment 1 (M length in letters � 7.67; M written
frequency �
Figure 1. Experiment 1: Proportions of correct recognition, as a
function
of word type and attentional condition. DA � divided-attention;
FA �
full-attention. Bars represent standard errors.
T
hi

ed
br
oa
dl
y.
1225ATTENTIONAL BOOST EFFECT IN IMPLICIT
MEMORY
77.33; M age of acquisition � 4.23; M familiarity � 5.85; M
imageability � 5.30; M concreteness � 5.92; M neighborhood
size � 0.20). The three sublists were counterbalanced across
participants, so that all the critical words had the same
probability
to be presented in target trials (with red circles), in distractor
trials
(with green circles) or as unstudied words. Encoding was
inciden-
tal, because participants were required to read aloud all the
words
but were not told to remember them for a later memory task.
The test phase consisted of the LDT. Participants were
presented
with a total of 110 items, including the 45 critical words (30
studied and 15 unstudied) and 45 legal pronounceable
nonwords,
plus 20 practice items (10 words and 10 nonwords). Each trial
included three events: a fixation point for 500 ms, a string of
letters
until the participant’s response, and a pause of 1,500 ms. The

instructions were to decide if each item was a valid Italian word
by
pressing either S for word or N for nonword: Both speed and
accuracy were stressed. The order of presentation was
randomized
anew for each participant, and no mention was made about the
relationship with the study phase.
Results and Discussion
During the encoding phase, participants correctly detected
99.2% of the red circles (mean response time � 401.2 ms). Only
reaction times (RTs) for correct responses were analyzed and
RTs
more than 3 standard deviations from the participant’s mean
were
removed (Ziegler & Perry, 1998; less than 3% of the data).
Mean
RTs for words encoded with red circles, words encoded with
green
circles, and baseline (unstudied) words were 667.08, 701.81,
and
743.99 ms in the DA condition and 682.58, 679.40, and 720.25
ms
in the FA condition. Priming scores (the difference between RTs
for unstudied and studied words) are the dependent measure
(see
Figure 2). Preliminary t tests indicated that RTs for unstudied
words did not differ between the DA and FA conditions, t(34) �
– 0.59, p � .55, and that priming scores were significantly
greater
than zero for target words and for distractor words, t(17)s �
3.28, all
ps � 0.01. A mixed 2 (word type: target vs. distractor) � 2
(attentional condition: FA vs. DA) ANOVA, with word type as
the

within-subject variable and attentional condition as the
between-
subjects variable, found a significant effect of word type, F(1,
34) �
6.89, MSE � 644.85, p � .013, �2 � 0.17, indicating that
priming
scores were greater for target words (encoded with red circles)
than for distractor words (encoded with green circles; M � 57.2
ms
vs. M � 41.5 ms), and there was a significant interaction, F(1,
34) �
10.11, MSE � 644.85, p � .003, �2 � 0.23. The main effect of
attentional condition was nonsignificant, F(1, 34) � 1.60, p �
.213. Follow-up analyses showed an attentional boost effect in
the
DA condition, in which repetition priming was higher for target
than distractor words, F(1, 34) � 16.86, p � .000, �2 � 0.33,
but
not in the FA condition, F(1, 34) � 0.15, p � .698. It is
important
to note that the attentional boost was absolute: Priming scores
for
target words were significantly greater in the DA condition than
in
the FA condition, F(1, 34) � 4.73, p � .037, �2 � 0.12. In
contrast
with Experiment 1, no difference between the two conditions
was
obtained for distractor words, F(1, 34) � 0.01, p � .939,
confirm-
ing that a dual task with infrequent response rates did not
disrupt
implicit memory (Mulligan et al., 2007). Similar results were
obtained when the performance for the target words was condi-
tionalized on prior success at the study-phase detection task; in

OBSERVATIONThe Attentional Cost of Receiving a Cell Phone .docx

OBSERVATIONThe Attentional Cost of Receiving a Cell Phone .docx

Recommended

Recommended

More Related Content

Similar to OBSERVATIONThe Attentional Cost of Receiving a Cell Phone .docx

Similar to OBSERVATIONThe Attentional Cost of Receiving a Cell Phone .docx (20)

More from hopeaustin33688

More from hopeaustin33688 (20)

Recently uploaded

Recently uploaded (20)

OBSERVATIONThe Attentional Cost of Receiving a Cell Phone .docx