Creative Education
2013. Vol.4, No.7A2, 37-41
Published Online July 2013 in SciRes (
Copyright © 2013 SciRes. 37
Working Memory and Distraction: Performance Differences
between College Students with and without ADHD
Tabitha W. Payne, Noah B. Z. Steege
Kenyon College, Gambier, USA
Received May 30th, 2013; revised June 30th, 2013; accepted July 7, 2013
Copyright © 2013 Tabitha W. Payne, Noah B. Z. Steege. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
The goal of this study was to examine potential deficits in working memory capacity for college students
that had a diagnosis of ADHD. College students with ADHD may be a particularly vulnerable group of
individuals, given that success on academic work required focus and working memory for a variety of
problem solving activities. Performance on these assessments involved controlled processing with simul-
taneous memory load. Both verbal and visual complex span tasks were used to assess working memory.
Additionally, students were all administered with the Brown ADD scale in order to examine self-reported
issues with distractibility. Results revealed that ADHD students performed significantly lower on the
verbal complex span measure of working memory. No differences in reported inattentiveness were found.
Findings were discussed in context of varying task demands in working memory and executive function
Keywords: Adult ADHD; Working Memory
The goal of this study was to further examine the hypothesis
that working memory deficits are consistent with a diagnosis of
Attention Deficit/Hyperactivity Disorder (ADHD) in young
adult college students. This population is of particular interest
since attaining academic success at the collegiate level requires
a high degree of controlled attention and executive processing.
Working memory capacity can be defined as an individual’s
ability to control attention for the purpose of maintaining in-
formation in awareness for the purpose of further processing
(e.g., integrating information, transforming information, or re-
hearsal for later use). This individual capacity is important for
academic success in a wide array of activities, such as reading
comprehension (Daneman & Carpenter, 1980), explicit learning
(Unsworth & Engle, 2005), verbal, spatial, and figural reason-
ing (Kane et al., 2004), second language processing (Payne,
Kalibatseva, & Jungers, 2009), and even note-taking and fol-
lowing directions (Engle, 2002). It is well-established in the
literature with neurotypical samples that individual differences
in working memory capacity predict performance on selective
attention measures, from filtering extraneous information dur-
ing the dichotic listening task (Conway, Cowan, & Bunting,
2001) to controlling reflexive eye movements to avoid capture
of attention from an orienting stimulus (Bleckley et al., 2003).
Finding a connection between ADHD and working memory
measures will provide concurrent validity for assessments what
already been shown to be associated with individual differences
in attentive processing with a non-diagnosed population.
It seems highly likely that working memory deficits would
be observable with a young adult college sample with ADHD
since there is evidence that both elementary school-aged chil-
dren and high school students with ADHD show such per- for-
mance decrements In a study by Kofler et al. (2010), ADHD
boys between the ages of 8 and 12 were compared to typically
developing control children on both verbal and visuospatial
working memory assessments. The verbal measure required
memory for a serial list of numbers and a letter, with a subse-
quent transformation of order, prior to recalling a set. The
visuospatial measure involved memory for where targeted dots
appeared in a series of grids. There was a significant difference
in the performance of children with and without ADHD diag-
nosis, which became more prevalent with increased memory set
size, or load on working memory. Consistent findings appear
with high school students with ADHD symptoms, showing
difficulty in both verbal and visual working memory recall
(Caterino & Verdi, 2012). When using a text model in which
visual images appear prior to semantically related text passages,
students without ADHD seem to benefit from this formatting
style, whereas this method hurts the performance of the ADHD
group, with the images serving as a distractor for text recall.
Both of these studies with younger samples indicate that work-
ing memory functioning is problematic for those with ADHD
diagnoses or symptoms. If working memory deficits are pro-
nounced for ADHD, in general, then it is possibly a continuing
condition into adulthood, which should be observable with
comparative samples.
The research with young adult samples has produced incon-
sistent findings of a connection between ADHD and working
memory. In a study using young adults (mean age of 25 years)
who were clinically referred for psychological evaluation (by
vocational rehabilitation counselors), no significant relationship
was found between working memory performance and reported
distractibility on the Brown ADD scale (Stearns, Dunham,
McIntosh, & Dean, 2004). The Brown scale score did predict a
previously provided clinical diagnosis for 60 out of the 70 par-
ticipants. It is possible that the lack of correlation between the
Brown scale and performance on the working memory meas-
ures (WAIS-III and WMS-III) could be due to restricted range
of the sample for the self-reported distractibility items on the
Brown scale since the mean was fairly high for the sample, well
above the criterion for ADHD diagnosis. It is important to con-
sider a wider range of symptoms and executive functioning in
the sample in order to observe the relationship between work-
ing memory and attentional difficulties. However, gaining un-
derstanding of ADHD can benefit from studies publishing with
a wide array of samples.
In a study with an ADHD adults (mean age 21.3) and con-
trols (mean age 22.1), Roberts, Milich, & Fillmore (2012) ex-
amined potential decrements in ADHD performance with in-
creasing load on working memory using the n-back task in
which participants must rely on verbal rehearsal. Although
there was no interaction between ADHD status and size of
memory load on accuracy, there was an interesting finding in
which reaction time on the n-back task was slower for ADHD
individuals with lower memory loads (1-back and 2-back) the
differences were not present with the largest load. Additionally,
the participants with ADHD did show difficulty with response
selection on a dual choice-response task, leaving the authors to
emphasize that cognitive deficits associated with ADHD may
include other more specific processes than working memory
capacity. Regardless of whether memory load was found to be
differentially processed by individuals with and without ADHD,
the importance of this study is establishing a link to working
memory, in general.
One suggestion for researchers has been to examine a wider
array of measures relating to executive functions, since many
standardized cognitive neurological assessments do not show
sensitivity to ADHD symptoms (Torralva et al., 2013). Com-
paring young adult patients of a specialized clinic for adult
ADHD with non-patient controls revealed differences on a
standardized executive function assessments for immediate
memory, as well as a computerized assessment for a challeng-
ing working memory and an ecological adaptation of a working
memory measure (Hotel Task) that involves problem solving
and working memory in the context of an employee task sce-
nario. Even high-functioning ADHD participants showed defi-
cits in working memory. Torralva and colleagues conclude that
ADHD deficits may be observable in tasks that are ecologically
valid and with tasks that have a high demand on working
The current experiment was conducted in order to further at-
tempt to observe and document potential cognitive deficits
associated with ADHD, but specifically with a college student
sample, which could be considered high-functioning, given
their admittance t to a highly selective private liberal arts col-
lege. Although such a scientific endeavor may result in no
ADHD effects due to the limited demographic, finding differ-
ences at this level of academics would be highly indicative of
working memory issues being prevalent in the larger population,
with more diversity. Behavioral evidence indicates that college
students with ADHD report more problems with attentiveness
and more academic complaints, such as having to read material
over and over to understand it or having trouble finishing time
tests, (Lewandowski, Lovett, Codding, & Gordon, 2008). ADHD
College students have also been found to have lower high
school and college GPA and withdraw from classes more than
students without ADHD (Advokat, Lane, & Luo, 2011), indi-
cating the need to better understand the abilities of this popula-
A secondary goal of this study was to use working memory
capacity assessments that have been shown to correlate with
fluid reasoning measures (Kane et al., 2004). The complex span
measures have been widely used as assessments of working
memory capacity and entail simultaneous process and memory
updating (Conway et al., 2005). These measures correlated with
other indicators of selective attention, and thus should be sensi-
tive to differences based on reports of inattentiveness or ADHD
diagnoses. Thus, ADHD students were compared with students
with no diagnosis on tests of visual and verbal working mem-
ory, similar to the Kofler et al. (2010) study with children. Ad-
ditionally, the Brown ADD scale for adults was administered to
assess self-reported distractibility. The central hypothesis was
that students with and without ADHD will differ in working
memory performance. There was a possibility that such differ-
ences will be sensitive to memory load, which was found with
children, but not yet with adults. It was also expected that stu-
dents with ADHD will report more problems with attention on
the Brown scale.
Participants with and without a diagnosis of ADHD were re-
cruited from the Kenyon College student population over 2
semesters (i.e., Fall, 2012 and Spring, 2013). Students were
informed about the study through campus emails and public
fliers that were placed in the campus bookstore and the Student
Health Center, indicating that students both with and without a
pre-existing diagnosis of ADHD by a physician were eligible.
Since the percentage of students with a diagnosis is typically
low at private colleges, the purpose of the Health Center re-
cruitment advertisement was to attempt to reach those with
ADHD to increase the likelihood of participation and thus the
size of the ADHD sample for comparison. The Students volun-
teered in exchange for research credit in psychology courses.
Subsequent to reading and signing an informed consent form,
participants completed a short demographic survey requesting
information such as age, gender, and mental health history re-
garding attention deficit disorders. The only reported disorder
for this sample was specifically Attention Deficit Hyperactivity
Disorder (ADHD). Participants were advised verbally and via
consent form that providing any or all of the requested informa-
tion for either survey was completely voluntary and that items
and measures could be skipped.
Reading Span Task. In order to assess performance for ver-
bal working memory capacity, the Reading Span task was used,
(Kane, et al., 2004). For this version of the Reading Span task,
participants were instructed to read aloud a sentence displayed
on the computer screen and determine whether or not it made
sense with respect to the semantic content by verbalizing “Yes”
or “No”. At the end of each sentence there was a single capital-
ized letter for the participant to memorize. Participants were
Copyright © 2013 SciRes.
advised to take as long as needed to determine if there were
semantic problems with a sentence. Once the yes/no decision
was declared, the participant was informed to immediately read
the letter at presented at the end of the sentence and attempt to
memorize that letter for recall. Once the letter was read aloud
the next sentence-letter pair was presented on the computer
screen. After several sentence/letter pairs, the participant was
asked to recall all the letters displayed and write them in the
order of presentation. Memory set sizes for this assessment
ranged from 2 to 5 sentence-letter pairs, with participants hav-
ing no a priori knowledge of how many would be in any given
set they were working on. There were 3 of each memory set
size (2, 3, 4, 5), making a total of 42 sentence-letter pairs in the
actual test. The Reading Span task began with three practice
sets of sentence/letter pairs (with memory set size of 2). This
particular version of the Reading Span task has good reliability
(with Chronbach’s alpha = .78), and construct validity (Kane et
al., 2004).
Symmetry Span Task. In order to assess visual working
memory capacity performance, the Symmetry Span task was
used, in which both the processing and memory component
were visual in nature. For the initial processing component of
the task, participants viewed a pattern of shaded and non-
shaded boxes on an 8 × 8 grid, and determined whether or not
the pattern was symmetrical, with respect to a vertical axis by
responding “yes” or “no”. Following a verbal response in ref-
erence to vertical symmetry (and a 400 millisecond delay), the
participant was briefly shown a 4 × 4 grid with one square
shaded red (for 650 milliseconds) and asked to remember the
location of the red square. After several iterations of the sym-
metry decisions paired with the memory grid, the participant
indicated the locations of the red squares corresponding to the
order displayed on a response sheet that consisted of 4 × 4 grids,
in which the square remembered to be red was marked. A prac-
tice session involved 3 sets of 2 grid pairs, after which the test-
ing portion presented to the participant 42 grid pairs in groups
of 2 to 5. As with the Reading Span measure, participants were
not aware of the memory set sizes prior to presentation. The
Symmetry Span task has also been have good reliability (with
Chronbach’s alpha = .86) (Kane et al., 2004)). Scoring for the
both the Reading Span task and Symmetry Span tasks entailed
calculating a total score by adding the total number of correct
responses across the entire working memory task, and average
total accuracy was calculated for each memory se, as well.
Brown ADD Scale. This survey requires the respondent to
rate how often the quality or behavior in question applies to
him/her using numbers 0 through 3; 0 corresponds to never, 1
to once per week or less, 2 to twice per week, and 3 indicating
an almost daily occurrence. There are 40 items, and the total
score includes adding the ratings for each. Example items are,
“Listens and tries to pay attention (e.g., in a meeting, lecture, or
conversation) but mind often drifts; misses out on desired in-
formation”, and “Cannot complete tasks in the allotted time;
needs extra time to finish satisfactorily”.
All participants were tested individually in a sound attenu-
ated laboratory setting at the Kenyon College Cognition Lab.
Each participant completed the demographic survey, and sub-
sequently the self-report attention scale, followed by the work-
ing memory capacity assessments, with the Reading Span task
administered first, followed by the Symmetry Span task. At the
end of the study, participants were debriefed and provided with
local information for health and counseling centers wherein
they could receive assistance if concerned about attention and
related cognitive functioning.
Eighty-two students between the ages of 18 and 23 volun-
teered for the study which took place between Sept 2012 and
April 2013. Mean age was 19.46 years old (SD = 1.21). Stu-
dents with reported ADHD diagnoses comprised 9.8% of the
sample. Descriptive data (means and standard error) for both
working memory capacity assessments are listed in Table 1 and
includes accuracy by set size for participants with and without
ADHD. Total sample data, combing groups, is also included.
Set size represents the amount of information to be remembered,
and the means are
Reading Span Results. In order to address the hypothesis
that ADHD students might show differential performance on
the Reading Span task, a General Linear Model (GLM) with
repeated measures was used to test the 2 × 4 mixed factorial
design with participant group having two levels (ADHD vs. No
Diagnosis) and set size with 4 levels (for memory sets of 2, 3, 4,
and 5). The dependent variable is the average accuracy for each
set size. Results indicated that there was a main effect of par-
ticipant group on Reading Span accuracy, (F(1, 80) = 4.926, p
= .029), with the ADHD group yielding lower accuracy, overall.
There was no significant interaction between participant group
and memory set size on accuracy, ((F(3, 79) = 1.142, p = .333).
However, there was a significant main effect of memory set
size on accuracy, (F(3, 79) = 6.017, p = .001), confirming in-
creasing task difficulty with larger memory load. Refer to Fig-
ure 1 for data for working memory performance by set size and
participant group. Note that significant group differences based
on ADHD diagnosis appear with the set size of 3 items, (t(80) =
2.209, p = .030, and also with set size of 4 items, (t(80) = .664,
p = .047).
Symmetry Span Results. To examine group effects on the
visual working memory capacity assessment, a GLM ANOVA
with repeated measures did not result in a significant main ef-
fect of ADHD diagnosis and overall performance on the Sym-
metry Span Task, (F(1, 80) = .744, p = .391). Additionally,
there was no significant interaction between participant group
and memory set size on average total accuracy, (F(3, 79) =
Figure 1.
Reading span memory set accuracy for participants with and without
DHD (with Standard Error Bars). A
Copyright © 2013 SciRes. 39
Copyright © 2013 SciRes.
Table 1.
Descriptive data for verbal and visual working memory measures by memory set size (Means and Standard Error).
Verbal WM Visual WM
Set Size Set Size
2 3 4 5 2 3 4 5
M 1.88 2.45 2.85 2.56 1.41 1.63 2.05 1.34
Total SE 0.026 0.062 0.1 0.122 0.058 0.079 0.104 0.119
M 1.89 2.5 2.91 2.61 1.42 1.63 2.06 1.4
NonADHD SE 0.028 0.065 0.101 0.128 0.061 0.085 0.115 0.127
M 1.83 2.04 2.25 2.08 1.25 1.63 1.96 0.79
ADHD SE 0.063 0.183 0.361 0.387 0.197 0.213 0.117 0.281
1.529, p = .208). As with the Reading Span task, there was also
a significant main effect of set size on accuracy, (F(3, 79) =
12.66, p < .001). Additionally, total accuracy on the visual and
verbal working memory tasks did significantly correlate with
each other (r = .342, p = .002), and each task yielded fairly
good reliability for this sample, with Chronbach’s alpha at .69
for the Reading Span Task and .84 for Symmetry Span.
Test Modality and Set Size. A GLM ANOVA was used to
examine effects and interactions between test modality (verbal,
visual), memory set size (2, 3, 4, 5), as within-subject factors
and ADHD status as the between-subjects variable. Mean ac-
curacy was the dependent variable in this analysis. Results re-
vealed a significant main effect of test modality, (F(1, 81) =
32.69, p < .001), with Reading Span having higher overall ac-
curacy over Symmetry Span. There was also a significant main
effect of memory set size, (F(3, 79) = 12.812, p < .001), as well
as a significant interaction between set size and test modality,
(F(3, 79) = 4.446, p = .005). Refer to Figure 2 for the modality
by set size interaction on accuracy. Note the differences in the
working memory tasks, with verbal yielding higher memory
accuracy, but both tasks showing performance increase with set
size until the set size with 5 items is reached. There is a signifi-
cant drop in performance for all participants from set size 4 to 5
on the Reading Span task, (t(81) = 2.561, p = .012), and also for
the Symmetry Span task, (t(81) = 7.095, p < .001).
Figure 2.
Mean accuracy on verbal and visual working measures as a function of
memory set size (with Standard Error Bars).
22.045). The Brown Scale did not significantly correlate with
Reading Span (r = .60, p = .591), or Symmetry Span (r = .00,
p = .997).
Discussion and Conclusion
Test Modality, Set Size, and ADHD. There was no signifi-
cant interaction between test modality and ADHD group status,
(F(1, 81) = .600, p = .441), nor was there a significant interac-
tion between set size and participant groups, (F(3, 79) = 1.263,
p = .288). Additionally, there was no interaction between mo-
dality, set size, and participant group, (F(3, 79) = 1.351, p
= .259). Lastly, there was no main effect of ADHD status on
overall working memory performance (which combines both
modalities), (F(1, 81) = 3.118, p = .081), however it appears to
be approaching significance.
The important finding of this study was that the college un-
dergraduates with an ADHD diagnosis did show difficulty on at
least one of the working memory measures, the Reading Span
task. Since the working memory system is comprised of do-
main-specific processing with a central executive component
for controlled attention, it is necessary to examine memory load
effects by comparing performance with increasing set size.
Changes in performance as set size increases are an indicator of
whether the differences between groups is based on the central
executive component or on the addition of constraints on
amount of information for verbal rehearsal. By closely examin-
ing the Reading Span data (Figure 1), it is important to note
that there is no change in the performance accuracy of the
ADHD group as set size increases. This group can recall about
2 items, in general, regardless of set size. Participants without a
diagnosis show a significant increase in recall along with mem-
ory load. The two groups clearly differ in performance patterns
up to set size of 4, followed by a drop in performance for those
Brown Scale. The mean for the totaled ratings was 41.768,
(SD = 16.866), with the range of scores from 8 to 95, with a
higher score indicating more reported distraction and problems
with attention. Results of an independent samples t-test indi-
cated no significant difference between ADHD participants and
those without a diagnosis, (t(80) = 1.926, p = .058), however,
there was a trend with ADHD students having a higher total
score, which indicates a higher distraction ratings, (MNonADHD =
40.608, SDNonADHD = 15.971 and MADHD = 52.500, SDADHD =
without ADHD to match the accuracy of the ADHD students.
This change from set size 4 to 5 indicates a capacity limitation
and overload for the non-diagnosed group, whereas the ADHD
individuals remain the same, with maximum recall at 2. The
data pattern is consistent with the notion that ADHD students
have limitations on verbal rehearsal capacity that are related to
the deficits in attention and sustained effort. The results also
contribute to the concurrent validity of the Reading Span task
as a complex span that is measure sensitive to individual dif-
ferences in the ability to control attention (Engle, 2002), since
there is now evidence of its sensitivity to differences in ADHD
The visual complex span task appeared to be significantly
more difficult than the verbal assessment for working memory
capacity. As seen in Figure 2, performance is lower for all
participants on the Symmetry Span task, which could explain
the lack of group differences in recall accuracy. Both groups
perform similarly on the memory sets for Symmetry Span, with
separations in performance emerging with set size of 5, but still
not a significant difference (refer to Table 1 for means). This
task may be too challenging to bring out differences in ADHD
status. Additionally, this test came at the end of the battery of
assessments and could be affected by mental fatigue.
The Brown scale scores for reported inattentiveness were not
different for the participant groups, although the difference was
approach statistical significance (p = .058), with the ADHD
group reporting more problems with attention. The Brown scale
is widely used as an indicator of ADHD symptoms, however, it
is only one assessment that is often used in combination with
other measures for the purpose of diagnosis. Previous research
has not always found a relationship with this scale and working
memory measures (Stearns, Dunham, McIntosh, & Dean, 2004),
and it is possible that cognitive performance measures may be
more accurate indicators of ADHD than self-reported behavior.
The main limitation of the research is the small sample size
of the ADHD group (9.8%), which is perhaps unavoidable
when studying college populations in which admittance is de-
pendent upon meeting high academic entry criteria. However,
the findings of this study are theoretically consistent and are
similar to findings with children (Kofler et al., 2010). Revealing
individual differences in cognitive constraints is highly impor-
tant to understanding the intellectual capacity of college stu-
dents in the academic setting, the authors suggest that research-
ers continue to investigate the relationship between working
memory and ADHD at other institutions for comparison to the
results of this study.
The findings of this research provide converging evidence
with previous research with young adult patients and college
students with ADHD indicating lower performance on a variety
of working memory assessments in comparison to those with-
out a diagnosis (Roberts, Millich, & Fillmore, 2012; Torralva et
al., 2013). To put these findings in context, the working mem-
ory measures assess the ability to maintain information in con-
sciousness while alternately engaged in another cognitive activ-
ity. In the reading span task, reading and comprehension judg-
ments happen while trying to additively remember a list of
letters. Memory updating and attempted rehearsal of the letters
occur while one is “distracted” by the reading aspect. Having
this extra cognitive processing reduces the amount of informa-
tion that can be maintained in comparison to having no dis-
tracting activity, regardless of a diagnosis. The findings reveal
hat students with ADHD hold less in memory when engaged in
this dual-task context. The results have implications for situa-
tions in which college students with ADHD are under cognitive
load. Knowledge that problems with working memory in
ADHD individuals extend into college should be integrated
with findings from research on methods to compensate for po-
tential difficulties and understanding factors involved in over-
coming obstacles for these students.
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