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BRIEF REPORT
Media multitasking and memory: Differences in working memory
and long-term memory
Melina R. Uncapher
1,3
&Monica K. Thieu
1,3
&Anthony D. Wagner
1,2,3
#Psychonomic Society, Inc. 2015
Abstract Increasing access to media in the 21st century has
led to a rapid rise in the prevalence of media multitasking
(simultaneous use of multiple media streams). Such behavior
is associated with various cognitive differences, such as diffi-
culty filtering distracting information and increased trait im-
pulsivity. Given the rise in media multitasking by children,
adolescents, and adults, a full understanding of the cognitive
profile of media multitaskers is imperative. Here we investi-
gated the relationship between chronic media multitasking
and working memory (WM) and long-term memory (LTM)
performance. Four key findings are reported (1) heavy media
multitaskers (HMMs) exhibited lower WM performance, re-
gardless of whether external distraction was present or absent;
(2) lower performance on multiple WM tasks predicted lower
LTM performance; (3) media multitasking-related differences
in memory reflected differences in discriminability rather than
decision bias; and (4) attentional impulsivity correlated with
media multitasking behavior and reduced WM performance.
These findings suggest that chronic media multitasking is as-
sociated with a wider attentional scope/higher attentional im-
pulsivity, which may allow goal-irrelevant information to
compete with goal-relevant information. As a consequence,
heavy media multitaskers are able to hold fewer or less precise
goal-relevant representations in WM. HMMs’wider attention-
al scope, combined with their diminished WM performance,
propagates forward to yield lower LTM performance. As such,
chronic media multitasking is associated with a reduced ability
to draw on the past—be it very recent or more remote—to
inform present behavior.
Keywords Episodic memory .Attention .Distractor
filtering .Impulsivity .Signal detection theory
In a world that affords ubiquitous access to information,
many people often multitask with multiple streams of
media. The rapid rise in Bmedia multitasking^(Rideout,
Foehr, & Roberts, 2010) has generated considerable scientific
and societal interest in the relationship between this behavior
and fundamental aspects of human cognition. Initial studies
have examined aspects of cognitive control, finding that heavy
media multitaskers (HMMs) perform poorly in tasks involving
working memory (Minear, Brasher, McCurdy, Lewis, &
Younggren, 2013) and distractor filtering (Cain & Mitroff,
2011; Ophir, Nass, & Wagner, 2009), with variable effects on
task switching (c.f. Alzahabi & Becker, 2013;Minearetal.,
2013; Ophir et al., 2009). Other studies have examined the
relationship between media multitasking behavior and psycho-
social variables such as trait impulsivity (Minear et al., 2013;
Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013;Shih,
2013). In general, greater self-reported media multitasking
appears associated with higher self-reported measures of
impulsiveness, either on Attention (Sanbonmatsu et al.,
2013) or Motor subscales (Minear et al., 2013; Sanbonmatsu
et al., 2013; c.f. Shih, 2013).
While the direction of causality is unknown—whether fre-
quent media multitasking induces psychosocial and cognitive
Electronic supplementary material The online version of this article
(doi:10.3758/s13423-015-0907-3) contains supplementary material,
which is available to authorized users.
*Melina R. Uncapher
melina.u@stanford.edu
1
Department of Psychology, Stanford University,
Stanford, CA 94305, USA
2
Neurosciences Program, Stanford University, Stanford, CA 94305,
USA
3
Jordan Hall, Bldg 420, Stanford, CA 94305-2130, USA
Psychon Bull Rev
DOI 10.3758/s13423-015-0907-3
control differences or whether people with these differences
gravitate toward more frequent media multitasking—the ini-
tial observations demand a deeper understanding of the cog-
nitive costs (and benefits) associated with frequent media mul-
titasking. This is especially urgent given that more and more
young people, whose brains are still developing, are engaging
in media multitasking (Rideout et al., 2010).
Progress may come from a fuller investigation of how cog-
nitive performance varies as a function of media multitasking
behavior. For instance, while the aforementioned studies point
to working memory (WM) differences, the conditions in
which such differences are obtained remain underspecified.
Using a signal detection decision-making framework, WM
performance can be characterized by a discriminability param-
eter (d’) that indexes the precision or amount of information
held in WM, and a bias parameter (C) that indexes the pro-
pensity to endorse that a signal was detected (Green & Swets,
1966). Given that HMMs demonstrate higher trait impulsivity
(Minear et al., 2013; Sanbonmatsu et al., 2013), it remains an
open question as to whether this population may require less
evidence to reach a decision, which would manifest as a more
liberal response bias when making WM
judgments. Moreover, HMMs’greater sensitivity to internal
and external distraction may manifest as reduced WM perfor-
mance even in the absence of external distractors.
A second line of open questions concerns whether the
WM performance differences in HMMs have consequences
for long-term memory (LTM). To date, investigations of
media multitasking have focused on cognition directed to
the present moment/very recent past or series of moments
(e.g., visual working memory; n-back; task switching). It
remains unknown whether the impairments in moment-by-
moment cognition observed in HMMs have consequences
for future cognition that depends on long-term memories
for those moments.
To address these open questions, we measured discrim-
ination and bias during WM performance, and then related
these measures to corresponding measures during LTM
performance (including measures of LTM for information
encountered in one of the WM tasks) in a large sample of
participants (N= 143).
Method
Participants
We recruited 143 participants (83 females; 18–35 years old,
mean = 22.1 years, SD = 3.65 years) from the Stanford Uni-
versity community. Complete data were collected from 139 of
the participants (data were lost from two participants due to
equipment malfunction and two due to noncompliance). The
experiment was performed in accordance with a protocol
approved by the Stanford University IRB. All participants
gave written informed consent and were remunerated $10/hr.
Procedure
Overview Participants completed a set of questionnaires and
performed four cognitive tasks (see Supplemental Materials
for details). The questionnaires included the Media Multitask-
ing Index (MMI; Ophir et al., 2009) and inventories for im-
pulsivity and ADHD. The cognitive paradigms included two
visual WM tasks and two recognition memory tests. All sig-
nificant effects are reported.
Working memory task: Rectangles Participants first per-
formed a standard visual WM task that required attentional
filtering (Vogel, McCollough, & Machizawa, 2005). Each trial
consisted of an array of two target rectangles, colored red,
along with 0, 2, 4, or 6 distractor rectangles, colored blue
(see Fig. 1a). Participants were instructed to first encode the
orientations of the red rectangles—ignoring blue rectangles—
during the encoding period, then remember these orientations
over the delay period, and finally detect whether either of the
targets changed orientation between encoding and test. Partic-
ipants indicated they detected a change (right index finger
button press) or no change (right middle finger button press).
Working memory task: Objects Participants next performed
a modified version of the visual WM task, wherein rectangles
were replaced with common objects arranged in a circle (see
Fig. 1b). Instructions were the same as in the rectangles task.
Recognition memory tasks Participants next performed (a)
an old/new recognition memory test for target objects from
the WM task, interspersed with new objects (see Fig. 1c)
and then (b) a similar test for distractor objects from the
WM task (see Fig. 1d). Participants responded with an old/
new judgment that included their confidence in the decision
(high or low).
Results
Questionnaires
Media multitasking index Across all 139 participants, the
median MMI score was 4.34 (mean = 4.41 ± 1.91). We iden-
tified 36 HMMs (mean = 6.92 ± 1.23) and 36 LMMs (mean
score = 2.19 ± 0.70).
Impulsivity index The mean BIS-11 score was 61.38
(±10.57); HMMs did not significantly differ from LMMs
across subscales, F(1, 201) = 2.40, p=.12;HMM
All scales
=
62.79 ± 10.81, LMM
All scales
= 59.86 ± 11.57.
Psychon Bull Rev
ADHD index The mean ADHD score was 2.41 (±1.59);
HMMs scored significantly higher than LMMs, F(1, 54) =
9.30, p= .0033; HMM = 2.92 ± 1.61, LMM = 1.97 ± 1.65.
Relationship between MMI, impulsivity, and ADHD
Across all participants, MMI score positively correlated with
ADHD, r
136
= .30, p= .00036, and impulsivity across sub-
scales, r
136
=.17,p= .046. The relationship between impul-
sivity and MMI was driven by the Attention subscale (r= .24,
p= .0046), with no significant effects in the other subscales
(Motor: r=.078,p= .36; Nonplanning: r=.065,p= .45). The
ADHD and overall impulsivity scores significantly correlated,
r
136
=.56,p=1.1*10
-12
.
Working memory and long-term memory performance
We first examined group effects (HMMs vs. LMMs) on per-
formance and then, for effects of interest, we further tested
whether performance continuously scaled with MMI score
(i.e., across all participants).
Working memory: Rectangles We analyzed WM perfor-
mance following Vogel et al. (2005): K=S*(H–F), where
Kis WM capacity, Sthe size of the target array (2), Hthe
proportion of correct changes detected (hit rate), and Fthe
proportion of changes incorrectly reported (false alarm rate).
As measured by K, LMMs were able to hold more task-
relevant information in mind relative to HMMs (see Fig. 2a,
left panel); Group (HMM, LMM) x Distractor Load (0, 2, 4,
6) ANOVA showed a main effect of Group: F(1, 256) =
4.88, p= .028. This difference was driven by a greater
tendency for HMMs to incorrectly endorse a change, when
none occurred (Bfalse alarms^; FAs), ANOVA on FA rate
showed a main effect of Group: F(1, 256) = 7.52, p=
.0065. Hit rate did not significantly differ across Groups:
F(1, 256) = 1.27, p= .26, and the Group x Hit/FA interac-
tion was significant, F(1, 548) = 5.39, p=.021.
We also interrogated the data in a signal detection theory
(SDT) framework (Green & Swets, 1966) to determine
whether HMMs’reduced WM performance reflects (a)
reduced discriminability to detect a change in the WM arrays,
as measured by d’
WM
(d’=Z
Hits
–Z
False Alarms
), and/or
(b) a different bias to report changes, as measured by
C
WM
(C=-½[Z
Hits
+Z
False Alarms
]). Relative to LMMs,
HMMs had a poorer ability to discriminate between the
presence versus absence of change (see Fig. 2a,middle
Fig. 1 Schematic of the working memory and long-term memory tasks.
a. Participants first performed a standard version of a visual WM task that
required attentional filtering at encoding (Vogel et al., 2005). Participants
first viewed an array of colored rectangles (red and blue) and were
instructed to attend to the red and ignore the blue rectangles. Two red
(target) rectangles always appeared, along with 0, 2, 4, or 6 blue
(distracting) rectangles. Participants were instructed to detect whether
either of the red (target) rectangles changed orientation from first to
second presentation. b. The standard WM task was modified to include
trial-unique common objects. Target and distractors (0, 2, 4, or 6) could
appear in any of 8 positions in a circular annulus around fixation (NB,
size of objects depicted relative to frame is not representative).
Participants were again instructed to detect whether either of the red
objects changed orientation from first to second presentation. c. The
ability to retrieve the target objects encountered in the WM
objects
task
was assessed by a recognition memory test, which interspersed objects
that were targets in the WM
objects
task with novel objects. d. The ability to
retrieve distractor objects encountered in the WM
objects
task was assessed
by a recognition memory test, which interspersed objects that were
distractors in the WM
objects
task with novel objects. (Color figure online.)
Psychon Bull Rev
panel), d’
WM
by Group and Distractor Load; main effect
of Group: F(1, 256) = 5.92, p= .016. HMMs and LMMs did
not differ in bias (see Fig. 2a, right panel),C
WM
by Group and
Distractor Load; main effect of Group: F(1, 256) = 1.48, p=
.23. Thus, reduced WM performance in people who frequent-
ly media multitask appears to be driven by discriminability
differences: HMMs hold fewer or less precise representations
of target information in WM.
To determine whether WM performance scales linearly
across all levels of media multitasking, we regressed all 139
participants’MMI scores against their d’
WM
.Thisrevealeda
significant negative relationship: The higher the MMI score,
the lower the WM discriminability, d’
WM
~ MMI, with
Distractor Load as a factor (i.e., d’
WM
~MMI*Load):mul-
tiple regression r=.16;effectofMMI,t=-2.61,p=.0092.As
was the case with group effects, the relationship between bias
and MMI was not significant, C
WM
~ MMI * Load: multiple
regression r= .098; effect of MMI, t= -1.10, p= .27.
Discriminability differences appeared to be due to FA rates
and not hit rates: participants with higher MMI scores exhibited
significantly higher FA rates, FA rate ~ MMI * Load: multiple
regression r=.18;effectofMMI,t= 2.97, p= .0032, but not
significantly lower hit rates, Hit rate ~ MMI * Load: multiple
regression r= .094; effect of MMI, t= -1.20, p=.23.
Working memory: Common objects A similar pattern of
results was observed using the Objects variant of the WM task.
Specifically, HMMs again exhibited significantly lower WM
performance than LMMs (see Fig. 2b, left panel); Kby Group
and Distractor Load, main effect of Group: F(1, 272) = 5.45, p
= .020, and this difference was due to a greater tendency to
endorse a change when none occurred, FA rate by Group and
Distractor Load, main effect of Group: F(1, 272) = 4.49, p=
.035. Again, hit rate did not significantly differ across Groups
(Hit rate by group and distractor load, main effect of Group:
F(1, 272) = 2.19, p= .14. Finally, HMMs demonstrated re-
duced discrimination relative to LMMs (see Fig. 2b, middle
panel); d’
WM
by Group and Distractor Load, main effect of
Group: F(1, 272) = 4.56, p= .034, with no difference in
bias (see Fig. 2b,rightpanel),C
WM
by Group and Distractor
Load, main effect of Group: F(1, 272) < 1.
Fig. 2 Performance on the working memory tasks. a. Light media
multitaskers (LMMs; blue) exhibited better working memory
performance (K; left panel) than heavy media multitaskers (HMMs;
red). This was driven by better discriminability (d’; middle panel) to
detect differences between the presence or absence of a change in
orientation of the target rectangles, and not a more liberal decision bias
to endorse a change (C; right panel). b. This overall pattern was similar
when the WM task required trial-unique objects to be held in mind: WM
performance (K; left panel) was better for LMMs than HMMs, and this
performance was driven by discriminability (d’; middle panel) and not
decision bias (C; right panel). (Color figure online.)
Psychon Bull Rev
Across-participant regression revealed that while higher
MMI scores numerically tended to be associated with lower
WM discriminability, this relationship only trended toward
significance, d’
WM
~ MMI * Distractor Load: multiple regres-
sion r= .16; effect of MMI, t= -1.64, p= .10. As in the
rectangles task, this trend was associated with a slightly greater
tendency to endorse a change when none occurred, although
this relationship again only trended toward significance, FA
rate ~ MMI * Load: multiple regression r= .16; effect of
MMI, t=1.64,p= .10. Finally, MMI again did not correlate
across participants with hit rate, Hit rate ~ MMI * Load: mul-
tiple regression r=.15;effectofMMI,t=-.87,p=.39,orbias,
C
WM
~ MMI * Load: multiple regression r= .099, effect of
MMI t=-.42,p=.68.
Taken together, these two WM studies indicate that––
regardless of the nature of the information (common objects or
rectangles)––HMMs demonstrate a deficit in WM that reflects
a reduction in the number or precision of task-relevant repre-
sentations that they can encode and/or maintain in WM.
Long-term memory: Target objects Paralleling the effects
observed in WM, HMMs, relative to LMMs, exhibited re-
duced LTM performance, manifested as a reduced ability to
discriminate the previously encountered WM targets from
novel objects (see Fig. 3a, left panel); d’
LTM
by Group,
Distractor Load, and Confidence (high vs. low), main
effect of Group: F(1, 532) = 9.39, p=.0023.HereHMMs’
poorer discrimination was accompanied by a more liberal de-
cision bias when looking across all trials, with HMMs dem-
onstrating a stronger bias to endorse objects as recognized,
C
LTM
by Group, Distractor Load, and Confidence, main
effect of Group: F(1, 532) = 5.83, p= .016. However,
when confined to high confidence responses only, HMMs
and LMMs were equally conservative, F(1, 267) = 1.31, p
= .25. Across participants, higher MMI scores correlated
with reduced LTM performance, d’
LTM
~ MMI * Distractor
Load * Confidence: multiple regression r= .65; effect of
MMI, t= -2.67, p = .008, even when confined to high confi-
dence retrieval responses, multiple regression r=.16;effectof
MMI, t=-2.47,p=.014.
To test whether WM performance—using the standard K
metric—predicted LTM performance, we regressed all partici-
pants’LTM discrimination scores (d’
LTM
) onto their perfor-
mance on the WM objects task. There was a significant positive
relationship between the ability to hold objects in WM and the
ability to later recognize those previously encountered objects
(NB, this pattern was significant when LTM performance was
assessed collapsed across decision confidence), r
136
= .31,
p=2.3*10
-4
, as well as when restricted to high confidence
decisions, r
136
=.33,p=8.6*10
-5
; thus, we report high con-
fidence outcomes henceforth (see Fig. 3a, right panel, green).
This relationship between the ability to encode and main-
tain common objects in WM and the ability to later retrieve
those objects from LTM is important, and yet does not adju-
dicate between alternative hypotheses about whether impaired
WM acts to reduce (a) the encoding of information into LTM,
or (b) task performance more generally, perhaps by reducing
the ability to hold information online during LTM tasks.A
first step toward adjudicating between these alternatives may
come from assessing whether WM performance predicts LTM
performance for completely different information. Here, we
tested this hypothesis by determining whether WM perfor-
mance on the rectangles task predicted LTM performance
(for the objects), and found that the predictive relationship
held (see Fig. 3a, right panel, orange); r
132
=.22,p=.0093.
Because WM performance for the two types of material was
correlated, we further examined whether performance on the
rectangles task provided predictive information about LTM
above and beyond that which was provided by the objects
task. A multiple regression analysis revealed a strong predic-
tive relationship, even after removing variance associated with
the WM objects task, multiple regression r=.29; effect of
K
rectangles
,t=3.52,p= .00046, suggesting that WM per-
formance may have a more general impact on LTM.
Taken together, the foregoing results show that people who
frequently engage with multiple media streams during their
daily lives demonstrate worse LTM forpreviously encountered
target information. Importantly, HMMs’diminished LTM and
WM performance occurred for information that was encoun-
tered while the participants were ostensibly single-tasking.
Long-term memory: Distractor objects A final question
concerned the LTM fate of distractor objects encountered dur-
ing the WM objects task, as the answer may shed light on
mechanisms underlying how HMMs manage competing rep-
resentations in the WM task. We predicted two possible sce-
narios: (1) at encoding, HMMs attend to distractor objects at
the expense of target objects, resulting in better representation
of distractor objects in WM for HMM vs. LMMs, and ulti-
mately leading to better LTM of distractor objects for HMMs,
or (2) the ability to interrogate representations held in mind,
whether during WM or LTM tasks, is reduced in HMMs,
manifesting as worse LTM performance in HMMs than
LMMs, for both targets and distractor objects.
An ANOVA of distractor LTM performance revealed a
trend favoring the second scenario, in that HMMs remembered
the distractors more poorly than LMMs (see Fig. 3b, left
panel); d’
LTM
by Distractor Load (2, 4, 6), Group, and Confi-
dence: main effect of Group: F(1, 399) = 3.47, p=.063.Inter-
estingly, the number of times a distractor was displayed in the
array (i.e., Distractor Load) had no effect on LTM for
distractors, F(1, 399) < 1.
We next examined whether WM performance predicts
LTM performance for the distractor objects (as it did for
target objects). To do so, we regressed all participants’
ability to retrieve distractors from LTM (d’
LTM
)ontotheir
Psychon Bull Rev
performance in the WM objects task (K, the index of how
well target information was held in mind). We found a
positive relationship between K and the ability to later
confidently recognize distractor objects (see Fig. 3b, right
panel, green); K
objects
~d’
LTM -d istra ct or s
* Confidence: mul-
tiple regression r=.72,effectofK
objects
,t=4.48,p=1.1
*10
-5
. This relationship was similar across WM tasks,
with WM performance in the rectangles task also
predicting long-term memory for distractor objects (see
Fig. 3b, right panel, orange); K
rectangles
~d’
LTM -d ist ra cto rs
*
Confidence: multiple regression r= .33, effect of K
rectangles
t=2.11,p= .036, although the relationship was not signif-
icant after removing variance associated with WM perfor-
mance for the objects task, likely due to floor effects, mul-
tiple regression r=.32;effectofK
rectangles
,t<1.
Together, these findings show that WM performance in gen-
eral—across different tasks (rectangles/objects) and different
information (target/distractor objects)—predicts LTM perfor-
mance, suggesting that WM deficits are likely exerting their
effects at both encoding and retrieval.
Relationship between task performance and impulsivity
Given the observed relationship between impulsivity and
MMI score—driven by the Attentional Impulsivity sub-
scale—we examined whether this subscale predicted task per-
formance (d’and Cin WM and LTM tasks). Across all par-
ticipants, the Attentional subscale negatively predicted d’in
both WM tasks, rectangles: attentional impulsivity ~ d’
WM
*
Load: multiple regression r=.14,effectofd’,t=-2.15,p=
.032; objects: multiple regression r=.15,effectofd’,t=-
2.75, p= .0062, but did not show a relationship with d’in the
LTM ta sk (p> .6) or with Cin any task (all ps > .05). Thus,
higher self-reported attentional impulsivity was associated
with worse discrimination in both WM tasks.
Fig. 3 Performance on the long-term memory tasks, for target and
distractor objects encountered in WM objects task. a. Target objects
encountered in the WM
objects
task were better remembered by LMMs
than HMMs (left panel), and, across participants, WM
object
performance
predicted later LTM for the target objects (right panel). b. Distractor
objects encountered in the WM
objects
task were also better remembered
by LMMs than HMMs (though they were poorly remembered, overall, by
both groups; left panel), and, across participants, WM
object
performance
predicted later LTM for the distractor objects (right panel). (Color figure
online.)
Psychon Bull Rev
Discussion
The study yielded four important findings. First, in two
independent tasks, HMMs showed reduced WM perfor-
mance regardless of whether external distractors were pres-
ent or absent. This performance decline was evident in
reduced Kand d’measures of WM ability. Moreover, when
media multitasking was treated as a continuous variable, a
negative relationship between chronic media multitasking
behavior and WM performance was observed. Second,
there was a coupling between WM and LTM, with LTM
performance predicted by WM abilities more broadly
(across different WM tasks and different content). This pat-
tern suggests that WM deficits likely exert effects on LTM
at both encoding and retrieval rather than selectively reduc-
ing the fidelity of the representations encoded into LTM.
Third, the observation that discriminability and not decision
bias accounted for differences in WM and LTM perfor-
mance suggests that HMMs’reduced amount or precision
of information held in mind—whether during WM or LTM
tasks—drives performance differences. Finally, in contrast
to our predictions, the higher impulsivity of HMMs corre-
lated with their reduced WM discrimination but not with a
tendency to require less evidence to reach a decision.
A small but growing number of studies have investigated
task performance of heavy and light media multitaskers or
have correlated MMI score with task performance, revealing
various behavioral differences. For instance, HMMs were
observed to have difficulty (a) filtering distracting informa-
tion, whether the information came from the environment
(external distraction) or from memory (internal distraction;
Ophir et al., 2009), and (b) ignoring attention-capturing
information, regardless of whether or not they were instructed
to ignore the information (Cain & Mitroff, 2011). HMMs were
observed to adopt a split visuospatial attention mode
(the allocation of attention to multiple locations), whereas
LMMs adopt a more unitary mode (Yap & Lim, 2013), and
individuals with higher media multitasking scores exhibit
enhanced multisensory integration (Lui & Wong, 2012).
Other studies investigating task-switching abilities have
reported equivocal results, showing that, relative to LMMs,
HMMs were worse (Ophir et al., 2009; Sanbonmatsu et al.,
2013), better (Alzahabi & Becker, 2013), or equivalent
(Alzahabi & Becker, 2013;Minearetal.,2013).
One mechanism proposed to underlie the differences asso-
ciated with chronic media multitasking isthat HMMs exhibit a
broader attentional scope (Cain & Mitroff, 2011; Lui & Wong,
2012; Ophir et al., 2009). A wider scope may change the
manner in which available information is filtered in order to
optimize task goals, manifesting as attention to both goal-
relevant and goal-irrelevant information. As a consequence,
goal-irrelevant information may compete with goal-relevant
information, reducing task performance.
Here, a wider attentional bias at encoding (i.e., WM objects
task) would result in competing WM representations of targets
and distractors, giving rise to lower fidelity LTM representa-
tions of both, as was observed. However, the amount of ex-
ternal distraction present during WM did not differentially
affect HMMs (c.f., Ophir et al., 2009), which suggests that
their lower performance may be a result of continual distrac-
tion by information not under experimental control. Addition-
ally, the present data suggest that lower fidelity encoding is not
the only mechanism contributing to HMMs’poor LTM per-
formance: that performance on an entirely different WM task
(WM rectangles) predictedLTMforbothtargetsand
distractors suggests that HMMs exhibit a generalized reduc-
tion in the ability to hold or interrogate precise representations
in mind, whether during WM or LTM tasks. Thus, the pattern
of findings suggest that HMMs’reduced discrimination in
WM and LTM may be a result of a wider attentional scope
at both encoding and retrieval, allowing task-irrelevant infor-
mation to continually compete with task-relevant information.
This wide scope first serves to reduce the amount or precision
of goal-relevant information held in mind and therefore
encoded into LTM; during the subsequent retrieval from
LTM, the wider attentional scope may result in the intrusion
of task-irrelevant information, further degrading the ability to
make accurate retrieval decisions.
Further bolstering the idea that a wider attentional scope
impacts cognition at both encoding and retrieval is the finding
that LTM for study distractors was worse, rather than better,
for HMMs. To the extent that a wider attentional scope at
encoding allowed more distractor information into WM for
HMMs, distractors could have been better encoded by HMMs
than by LMMs, which should have then led to better distractor
memory. Instead, here we found distractor memory to be
slightly worse in HMMs, suggesting that the seemingly wider
attentional scope of HMMs has an impact on task perfor-
mance more generally. It will be important in future investi-
gations to determine just how extensively WM deficits impact
cognition in HMMs.
Our findings additionally revealed that attentional impulsiv-
ity positively related to the degree to which participants
multitasked with media. The BIS-Attention subscale has been
shown to index self-reported factors of attention (Bfocusing on
the task at hand^) and cognitive instability (Bthought insertions
andracingthoughts^; Patton et al. 1995). These factors may
describe well the phenomenology associated with adopting a
broad attentional scope/reduced filter (see Supplement for
further discussion).
In conclusion, the present findings point to a parsimonious
and mechanistic explanation for many of the performance dif-
ferences observed in the growing literature investigating
chronic media multitaskers. That chronic media multitasking
is associated with deficits in cognitive abilities that are critical
for successful navigation through life—including holding
Psychon Bull Rev
information in mind and retrieving information from
memory—calls for systematic investigations into what is cause
and what is effect. Our increasingly media-saturated world
may be nudging us toward an increasingly wider scope of
attention, in which case how we choose to interact with media
may significantly impact cognitive performance. On the other
hand, adopting healthy media hygiene may make no difference
if one’s media multitasking behavior is due to a cognitive pre-
disposition (e.g., impulsivity) that leads to, rather than is
caused by, such multitasking. The relationship between media
multitasking and academic outcomes also remains un-
known, in college-age adults, as well as in younger students.
Given the increasing understanding of the importance of WM
and LTM to academic achievement, future studies should aim
to determine whether and how media multitasking behavior
relates to academic outcomes. Poorer WM and LTM could
give rise to reduced classroom-based learning and testing per-
formance. By contrast, there may be instances where the cog-
nition associated with HMM behavior gives rise to superior
academic outcomes. For example, if a broader attentional
scope allows for reinstatement of related memories (e.g., Kuhl
et al. 2011; Shohamy & Wagner, 2008), this may support the
generation of cognitive schemas that facilitate learning
of academic content. Recommendations for parents, educators,
students, and policymakers will depend on understanding the
direction of causality between media multitasking and cogni-
tive differences in students as well as in the general population.
Acknowledgements This work was supported by NIMH grant
R21-MH099812.
Conflict of Interest The authors declare no competing financial
interests.
References
Alzahabi, R., & Becker, M. W. (2013). The association between media
multitasking, task-switching, and dual-task performance. Journal of
Experimental Psychology—Human Perception and Performance.
doi:10.1037/a0031208
Cain, M. S., & Mitroff, S. (2011). Distractor filtering in media
multitaskers. Perception, 40, 1183–1192. doi:10.1068/p7017
Green, D. M., & Swets, J. A. (1966). Signal detection theory and
psychophysics.NewYork,NY:Wiley.
Kuhl, B., Rissman, J., Chun, M., & Wagner, A. (2011). Fidelity of neural
reactivation reveals competition between memories. Proceedings of
the National Academy of Sciences of the United States of America,
108(14), 5903–5908. doi:10.1073/pnas.1016939108
Lui, K. F. H., & Wong, A. C. N. (2012). Does media multitasking always
hurt? A positive correlation between multitasking and multisensory
integration. Psychonomic Bulletin and Review, 19(4), 647–653. doi:
10.3758/s13423-012-0245-7
Minear, M., Brasher, F., McCurdy, M., Lewis, J., & Younggren, A.
(2013). Working memory, fluid intelligence, and impulsiveness in
heavy media multitaskers. Psychonomic Bulletin and Review, 20(6),
1274–1281. doi:10.3758/s13423-013-0456-6
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media
multitaskers. Proceedings of the National Academy of Sciences of
the United States of America, 106(37), 15583–15587. doi:10.1073/
pnas.0903620106
Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of
the Barratt impulsiveness scale. Journal of Clinical Psychology,
51(6), 768–774.
Rideout, V. J., Foehr, U. G., & Roberts, D. F. (2010). Generation M2:
Media in the lives of 8- to 18-year-olds. Oakland, CA: Henry J.
Kaiser Family Foundation.
Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M.
(2013). Who multi-tasks and why? Multi-tasking ability, perceived
multi-tasking ability, impulsivity, and sensation seeking. PLoS ONE,
8(1), e54402. doi:10.1371/journal.pone.0054402
Shih, S.-I. (2013). A null relationship between media multitasking and
well-being. PLoS ONE, 8(5), e64508. doi:10.1371/journal.pone.
0064508
Shohamy, D., & Wagner, A. D. (2008). Integrating memories in the hu-
man brain: hippocampal-midbrain encoding of overlapping events.
Neuron, 60(2), 378–389. doi:10.1016/j.neuron.2008.09.023
Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005).
Neural measures reveal individual differences in controlling access to
working memory. Nature, 438(7067), 500–503. doi:10.1038/
nature04171
Yap, J. Y., & Lim, S. W. H. (2013). Media multitasking predicts
unitary versus splitting visual focal attention. Journal of
Cognitive Psychology, 25(7), 889–902. doi:10.1080/
20445911.2013.835315
Psychon Bull Rev
