COMT and ANKK1-Taq-Ia Genetic Polymorphisms
Influence Visual Working Memory
Marian E. Berryhill1*, Martin Wiener2, Jaclyn A. Stephens1, Falk W. Lohoff3, H. Branch Coslett2
1Memory and Brain Laboratory, Department of Psychology, University of Nevada, Reno, Nevada, United States of America, 2Laboratory for Cognition and Neural
Stimulation, Department of Neurology, Perelman School of Medicine of the University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania, United
States of America, 3Translational Research Laboratory, Center for Neurobiology and Behavior, Department of Psychiatry, Perelman School of Medicine of the University of
Pennsylvania, Philadelphia, Pennsylvania, United States of America
Complex cognitive tasks such as visual working memory (WM) involve networks of interacting brain regions. Several
neurotransmitters, including an appropriate dopamine concentration, are important for WM performance. A number of
gene polymorphisms are associated with individual differences in cognitive task performance. COMT, for example, encodes
catechol-o-methyl transferase the enzyme primarily responsible for catabolizing dopamine in the prefrontal cortex. Striatal
dopamine function, linked with cognitive tasks as well as habit learning, is influenced by the Taq-Ia polymorphism of the
DRD2/ANKK1 gene complex; this gene influences the density of dopamine receptors in the striatum. Here, we investigated
the effects of these polymorphisms on a WM task requiring the maintenance of 4 or 6 items over delay durations of 1 or 5
seconds. We explored main effects and interactions between the COMT and DRD2/ANKK1-Taq-Ia polymorphisms on WM
performance. Participants were genotyped for COMT (Val158Met) and DRD2/ANKK1-Taq-Ia (A1+, A12) polymorphisms.
There was a significant main effect of both polymorphisms. Participants’ WM reaction times slowed with increased Val
loading such that the Val/Val homozygotes made the slowest responses and the Met/Met homozygotes were the fastest.
Similarly, WM reaction times were slower and more variable for the DRD2/ANKK1-Taq-Ia A1+ group than the A12 group.
The main effect of COMT was only apparent in the DRD2/ANKK1-Taq-Ia A12 group. These findings link WM performance
with slower dopaminergic metabolism in the prefrontal cortex as well as a greater density of dopamine receptors in the
Citation: Berryhill ME, Wiener M, Stephens JA, Lohoff FW, Coslett HB (2013) COMT and ANKK1-Taq-Ia Genetic Polymorphisms Influence Visual Working
Memory. PLoS ONE 8(1): e55862. doi:10.1371/journal.pone.0055862
Editor: Huiping Zhang, Yale University, United States of America
Received September 27, 2012; Accepted January 3, 2013; Published January 31, 2013
Copyright: ? 2013 Berryhill et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by faculty start-up funds generously provided by the University of Nevada to MB and by COBRE 1P20GM103650-01 (PI M.
Webster, project PI MB) and R15EY022775 to MB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Working memory (WM) refers to the ability to maintain and
manipulate information ‘on-line’ in the face of disruptions such as
eye movements. Examples include remembering the number you
looked up to dial or the location of your coffee mug while you
continue to look at your computer. WM is studied using the full
experimental toolkit including neuroimaging, investigations in
participants with brain lesions, brain stimulation and behavioral
tasks in normal participants. Advances in molecular genetics now
make it practicable to study the underlying mechanisms of WM by
looking at an individual participant’s genotype. In WM the focus
has been on several genes that modulate the dopamine concen-
tration. Successful WM is believed to depend on an optimal
dopamine concentration and too much or too little dopamine is
considered to be detrimental to executive function (reviewed in
[1,2,3,4,5,6]. Here we investigated the effects on WM of two genes
that affect dopamine activity through two single nucleotide
polymorphisms that are common in the general population.
One well studied genetic polymorphism codes for two versions
of the catechol-O-methyltransferase (COMT) enzyme. In the
prefrontal cortex (PFC), COMT is the primary enzyme that breaks
down dopamine and other catecholamines [7,8,9,10]. There is a
common single nucleotide polymorphism in COMT that replaces
a valine with a methionine (Val158Met, rs4680). The rate of
COMT enzymatic activity is reduced by a factor of four in the
Met/Met homozygote population . In other words, the
efficient COMT enzyme (Val/Val) breaks down dopamine quickly
leaving little dopamine in the synapse whereas the less efficient
COMT enzyme (Met/Met) leaves dopamine in the synapse over a
longer period of time. Behavioral findings suggest that Met/Met
homozygotes perform better on a number of executive function
tasks including the Wisconsin Card-Sorting Task [12,13,14,15];
reviewed in , Complex Working Memory Span , and n-
back WM tasks [14,18,19]; see also reviews in [6,20]. Further-
more, differences become more apparent with age [21,22,23]. A
recent meta-analysis of twenty relevant neuroimaging studies
clarified the link between COMT genotype, prefrontal dopamine
and cognitive task performance . Across these studies the
authors observed a consistent relationship (effect size of.73)
between prefrontal activation and COMT genotype. However,
other reports are inconsistent with these findings. For example, a
recent study with 86 participants failed to find any effect of
COMT in a change blindness WM study . In a second study,
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Bruder and colleagues tested 402 participants in four WM tasks (n-
back, serial position, spatial delayed response, letter-number
sequencing) and only found a Met/Met benefit for letter-number
sequencing . Finally, a recent meta-analysis evaluating a series
of cognitive tasks and COMT genotype observed no consistent
relationship between performance and genotype . These
inconsistent findings point towards an incomplete understanding
of COMT effects on cognitive performance that is compounded
by task differences, the need for large numbers of participants, and
perhaps most importantly by unknown interactions with other
genes across multiple brain regions.
There is a parallel literature investigating polymorphisms
influencing striatal dopamine. There are strong frontostriatal
connections and evidence supporting a role of the striatum in
higher cognition, including in WM [28,29]. Although the striatum
is classically associated with habit learning it has also been
associated with updating the contents of WM . Indeed, an
individual’s WM capacity predicts striatal dopamine synthesis
. Furthermore, there is experimental evidence to suggest that
WM requires the basal ganglia for gating what enters WM and the
prefrontal cortex for WM maintenance [29,32], which is in accord
with computational models [33,34,35]; see also . In the
striatum, D2 receptors are the most common dopamine receptor
. The density of D2 dopamine receptors in the striatum is
influenced by polymorphisms in the DRD2/ANKK1-Taq-Ia
fragment [37,38], also referred to as the ANKK1 polymorphism.
The presence of a single copy of the A1 allele (A1+) is associated
with a 30–40% reduction in D2 receptor density ; but see 
and reduced cognitive performance when compared to partici-
pants lacking this polymorphism (A12). Carriers of the A1 allele
perform worse on the California Verbal Learning Test of memory
[40,41] and other cognitive tasks (reviewed in [20,42].
Thus, because dopaminergic frontostriatal pathways modulate
WM performance there is good reason to investigate COMT and
DRD2/ANKK1-TAQ-Ia simultaneously. The COMT Val158Met
polymorphism dictates dopamine concentration in the PFC but
not in the striatum . Likewise, there are few D2 receptors in
the PFC but many in the striatum . One recent study explored
both COMT and DRD2 effects on a series of WM tasks .
Stelzel et al. (2009) reported that Met/Met homozygotes
performed better across WM tasks, but only when they were
A12. In short, both a slow acting dopamine-catabolizing enzyme
and a high concentration of dopamine receptors were associated
with good WM performance . Val158Met and the DRD2/
Ankk1-Taq-Ia polymorphisms may interact to produce differential
phenotypes at the behavioral level.
In addition to WM load and genotype we were also interested in
the relative contributions of COMT and DRD2/ANKK1-Taq-Ia
polymorphisms with regard to interval timing. Interval timing
refers to the ability to discriminate between different temporal
durations. Recently, Wiener and colleagues used molecular
genetics to reveal two timing circuits . Participants were asked
to discriminate short (500 ms) and long (2000 ms) time intervals.
Response time variability increased at short intervals for the A1+
DRD2/ANKK1-Taq-Ia group and at the long intervals for the
COMT Val+ group. The conclusion was that separate temporal
mechanisms in the striatum and the PFC are optimized for short
and long intervals, respectively. However, one unresolved issue
from these findings is the cause of the disruption in timing
performance. One untested possibility was that the differential
response for Val+ carriers for longer durations might relate to WM
load rather than timing per se. The effects for longer stimuli could
simply reflect a WM deficit rather than the assumed effect of
To explore these issues we investigated the effects of COMT
and DRD2/ANKK1-TAQ-Ia polymorphisms on WM perfor-
mance in healthy adults. In addition, we included maintenance-
delay and set-size manipulations to investigate differential striatal
and PFC involvement for short (1000 ms) or long (5000 ms) delays
with small (4-element) or large (6-element) WM maintenance
requirements. With regard to COMT, we predicted that the Met/
Met homozygotes would perform significantly better than the
Met/Val or Val/Val groups. These predictions were based on
previous findings generally reporting superior WM performance in
Met/Met participants (reviewed in . Based on the work of
Stelzel and colleagues (2009), we further predicted that in DRD2/
ANKK1-TAQ-Ia A12, COMT Met/Met participants, we would
see better WM performance when compared to all other groups.
We also investigated whether varying the WM maintenance delay
would interact with participants’ genotypes as demonstrated by
Wiener and colleagues (2011). We had two a priori
predictions. First, we predicted that COMT Val+ carriers would
be disproportionately impaired at higher WM demands: longer
delays and larger set sizes. Second, we predicted that the A1+
DRD2/ANKK1-TAQ-Ia carriers would be disproportionately
impaired at shorter delays. Finally, set size was manipulated to
avoid WM floor effects in participants with high WM capacity.
Ethics Statement and Participants
134 participants (mean age 22.8; standard deviation=6.00,
range=18–57, 53 male, aged 36–57: N=6, aged 26–35: N=25,
aged 18–24: N=103) from the University of Pennsylvania and
University of Nevada communities were recruited. Participants
received payment or undergraduate course bonus credit for
participation. The majority of participants were Caucasian.
Participants were screened so that they had normal or correct-
ed-to-normal vision. All participants consented to the experimen-
tal procedures and the collection of saliva for DNA analysis. The
Institutional Review Boards of the University of Pennsylvania and
the University of Nevada approved all experimental protocols.
Participants signed informed consent documents.
Saliva samples were collected with an OG-100 Oragene
collection kit (DNA Genotek, Ontario, Canada), and DNA was
extracted using standard Methodology. One participant did not
provide a sample with sufficient DNA for analysis and a second
was eliminated due to problems with sample/data labeling.
Genotyping was performed using standard Applied Biosystems
ABI Taqman genotyping. Quality control included genotyping of
10% duplicates. Concordance rates were 100%.
For the COMT Val158Met polymorphism (rs4680), we identi-
fied 43 subjects homozygous for the Val allele, 63 Val/Met
heterozygotes, and 27 Met homozygotes. This distribution was
consistent with the Hardy-Weinberg equilibrium (X2(1)=23,
p.75). For the DRD2/ANKK1-Taq-Ia (rs1800497) analysis we
collapsed across A1 carriers because of low frequency of A1/A1
homozygotes (e.g. , awe combined A1/A1 homozygotes and
A1/A2 heterozygotes as A1+ carriers that violated Hardy-
Weinberg equilibrium. The results of our genotyping analysis
identified 61 subjects with the DRD2/ANKK1-Taq-Ia polymor-
phism (A1 allele carriers: A1+), 72 subjects who lacked the
polymorphism (A2 homozygotes: A12). In the A1+ group there
were 13 Met/Met, 28 Val/Met, 20 Val/Val; in the A12 group
there were 14 Met/Met, 35 Val/Met, 23 Val/Val participants.
WM and Genetic Polymorphisms
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We used a sequential presentation object WM paradigm
(e.g.[46,47,48]. During each trial, participants viewed a series of
sequentially presented circular color patches (1000 ms/stimulus);
see Figure 1. Non-primary colors (e.g. peach, teal, chartreuse) were
selected to avoid a verbal strategy based on over-learned labels.
Trials with four or six stimuli were equally likely and pseudor-
andomly interleaved. Next, a checkerboard mask was presented
during the variable delay duration (1000 ms or 5000 ms). Both
delay durations were equally likely and unpredictable. A probe
item appeared and participants made a button press response to
indicate whether the probe had been presented earlier among the
stimuli or not (chance=50%). There were a total of 104 trials and
sessions lasted approximately 15 minutes. Participants were
instructed to respond as quickly and as accurately as possible.
The median correct reaction time data were included in the
following analyses. To assess reaction time variability, the standard
deviation for each participant for each set size and delay were also
subjected to analysis. Accuracy was measured by corrected
recognition (hits-false alarms). In corrected recognition measures
chance is equal to 0. This measure permits calculation when the
values are 0 or 1, unlike d’. The same pattern of results is expected
when using corrected recognition or d’ . Identical analyses
were also conducted using d’ values (z(hits)–z(false alarms))
replacing values of 0 and 1 with. 001 and .99, respectively. All
pairwise comparisons were Bonferroni corrected for multiple
The reaction time data were subjected to repeated measures
ANOVA with the within-subject factors of set size (4, 6), and delay
(1 s, 5 s), and the between-subjects factors of genotype: COMT
(Met/Met, Met/Val, Val/Val), and DRD2/ANKK1-Taq-Ia
(A1+, A12). As expected, there was a main effect of set size (F1,
127=9.24, p=.003, partial g2=07) and delay (F1, 127=55.79,
p,001, partial g2=31) such that performance was faster when
there were fewer items or shorter delays. There was a main effect
of COMT genotype (F2, 127=2.98, p=05, partial g2=05); see
Figure 2a. Pairwise comparisons indicated that the Met/Met
homozygotes were significantly faster than the Val/Val homozy-
gotes (M Met/Met=1261 ms, M Val/Val=1468 ms; p=045)
but neither group was significantly different from the intermediate
Val/Met heterozygous group (M Met/Val=1393; p’s.29); see
Figure 2a. There was also a significant main effect of DRD2/
ANKK1-Taq-Ia genotype (F1, 127=4.60, p=03, partial g2=03)
such that the A12 group responded more quickly than the A1+
group (M A12=1311 ms, M A1+=1444 ms); see Figure 2b.
None of the within- or between-subjects factors interactions,
including the set size6delay or the COMT6DRD2/ANKK1-
Taq-Ia interaction, approached significance (all p’s.21); see
Figure 2c. To assess changes in reaction time variability the
standard deviations of each participant’s reaction times were also
subjected to the same analysis. There was a main effect of set size
(F1, 127=4.31, p=04, partial g2=03; M standard deviation set
size 4=771.88, set size 6=856.19) and delay (F1, 127=15.66,
p,001, partial g2=11; M 1 s delay=723.81, 5 s delay=904.28)
such that variability increased at the higher set size and longer
delay. There was a main effect of DRD2/ANKK1-Taq-Ia
genotype (F1, 127=3.95, p=05, partial g2=03) such that the
A12 genotype was less variable than the A1+ genotype (M A12
standard deviation=717.50, A1+=910.56), but no main effect of
COMT genotype (F2,
interaction (F2, 127=1.87, p=16). No other interactions ap-
proached significance (all p’s.29).
Because of our a-priori assumptions regarding COMT Met/
Met and A12 genotype interactions, we conducted a second set of
repeated measures ANOVA in which we investigated the main
effect of COMT genotype separately in the A12 and the A1+
groups. In the A12 group there were the expected main effects of
set size (F1, 69=4.36, p=04, partial g2=06) and delay (F1,
69=24.46, p,001, partial g2=26). Here, however, the main
effect of COMT genotype reached significance (F2, 69=4.68,
p=012, partial g2=12). The A12 Met/Met homozygotes
trended towards responding more quickly (M=1124 ms) than
the A12 Met/Val heterozygotes (M=1356 ms, p=07) and
responded significantly more quickly than the A12 Val/Val
homozygotes (M=1452 ms, p=01). There was no significant
pairwise difference between the A12 Met/Val and the A12 Val/
Val groups (p=79). No interactions approached significance (all
In the A1+ participants, the main effects of set size (F1, 58=4.85,
p=03, partial g2=03) and delay (F1, 58=30.38, p,001, partial
g2=34) reached significance. Importantly, there was no main
effect of COMT genotype in the A1+ group (F,1, p=82) alone.
None of the interactions approached significance (all p’s.32).
127=2.06, p=13) and no gene6gene
Figure 1. Trial sequence. After an initial fixation cross 4 or 6 stimuli were presented (1000 ms/stimulus). The WM maintenance period was either 1
or 5 s in duration and it was immediately followed by a probe image. Participants determined whether the probe image matched a previously shown
stimulus. The participant initiated the next trial with a button press response.
WM and Genetic Polymorphisms
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The accuracy measure, corrected recognition (hits–false alarms),
also revealed the expected main effects of set size (F1, 127=36.92,
p,001, partial g2=23) and delay (F1, 127=61.72, p,001, partial
g2=33) such that performance was better when set sizes were
smaller (M set size 4=37, 6=26) and delays were shorter (M 1 s
delay=38, 5 s=25). None of the within-subjects or mixed within-
and between-subject factor interactions approached significance
(all p’s.16). Neither was there a significant main effect of COMT
or DRD2/ANKK1-Taq-Ia genotype (F’s,1, p=ns). However,
the interaction of COMT and DRD2/ANKK1-Taq-Ia ap-
proached significance (F1, 127=2.92, p=.057, partial g2=04).
The nature of this borderline significant interaction was the
following: for the A12 group, Val loading was associated with
numerically greater corrected recognition performance (Mean
Val/Val=36, Met/Val=32, Met/Met=28). This pattern was
the opposite of that observed in the reaction time data where Val
loading was associated with slower performance. A detrimental
effect of Val loading was observed in the A1+ group where the
Val/Val group performed worse than the Met/Val or Met/Met
groups (Mean Val/Val=27, Met/Val=34, Met/Met=34).
These trends were not apparent in repeated measures ANOVAs
evaluating set size, delay and COMT genotype conducted
separately on the A12 and A1+ data (COMT main effect:
A12: F2, 69=1.59, p=21, A1+: F2, 58=1.53, p=22); see Figure 3.
Although corrected recognition is common in WM studies because
it can be calculated at ceiling and floor values, we also calculated
and analyzed d (z(hits)–z(false alarms)). The results remained
consistent (main effects of set size: F1, 127=43.38 p,001, partial
g2=26) and delay (F1, 127=59.13, p,001, partial g2=32, no
main effect of COMT or DRD2/ANKK1-Taq-Ia F’s,1, p=ns,
or interaction COMT6DRD2/ANKK1-Taq-Ia; F2, 127=1.45,
The present study confirmed that genotypes modulating
dopamine concentration, COMT and DRD2/ANKK1-Taq-Ia,
have main effects on WM performance. The reaction time and
accuracy data provide slightly different perspectives on the nature
of this influence. The reaction time data revealed that COMT
genotype followed a stepwise slowing with Val loading: Met/
Met,Met/Val,Val/Val. There was also a relationship between
DRD2/ANKK1-Taq-Ia genotype such that carriers of the
polymorphism (A1+) were slower and more variable than the
non-carrier group (A12). We also observed a numerical, but non-
significant, interaction suggesting that the COMT effect was
predominant in the DRD2/ANKK1-Taq-Ia A12 group. This
finding provides some support for the view that there may be a
degree of non-independence between these two genes. The
accuracy data add complexity in interpreting these findings. A
borderline significant interaction showed that behavior patterns in
DRD2/ANKK1-Taq-Ia groups varied as a function of their
Figure 2. Reaction time findings. a, b) Main effects of COMT and DRD2/ANKK1-TaqIa on WM reaction time. c) The interaction of COMT and DRD2/
ANKK1-TaqIa on WM performance. In each figure the data are grouped as a function of delay duration (1 s, 5 s) and set size (4, 6). Error bars represent
the standard error of the mean.
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COMT status. COMT Val loading helped the A12 group and
hurt the A1+ group. Thus, we observed hints that COMT Val
loading could hurt WM performance in different ways. In the
A12 group it tended to slow reaction times and in the A1+ group
it tended to lower accuracy. However, these epistatic effects were
not sufficiently strong to produce significant gene6gene interac-
tions and thus must be treated with appropriate caution.
Possible Epistatic Interactions
Linking individual genotypes with performance differences is
interesting, but of course genes do not operate in a vacuum and
ultimately the goal is to understand how genes interact with each
other. We found some suggestion of COMT and DRD2/
ANKK1-Taq-Ia gene interactions. However, because there were
no statistically significant gene6gene interactions any discussion
remains speculative. We did observe a linear relationship between
reaction time and Val loading in the A12 group but not the A1+
group. This means that when there is a low concentration of
striatal D2 receptors, as in the presence of an A1 allele (A1+),
reaction time was slower across all COMT genotypes. Further-
more, in A1+ participants, accuracy tends to worsen with Val
loading. One possibility is that individuals with Met/Met
genotypes are not at the peak of the inverted-U shaped function
of optimal dopamine availability if they are also A1+; see Figure 4.
Epistatic interactions may abolish the inverted-U shaped function
under certain conditions, such as in the presence of the A1+ allele.
To confirm this prediction future studies will need to analyze all
three COMT Val158Met genotypes without collapsing across
groups to increase power (e.g. .
Second, a few other studies evaluate COMT and DRD2/
ANKK1-Taq-Ia genotypes. Stelzel and colleagues investigated
epistatic interactions with regard to WM performance . Factor
analysis indicated that there was a COMT6DRD2/ANKK1-
Taq-Ia interaction loading on WM manipulation but not on WM
maintenance or inhibition. They concluded that COMT effects
are modulated by DRD2/ANKK1-Taq-Ia genotype such that
they are apparent only in the A12 group. Wishart and colleagues
(2011) tested performance on the Trail Making test, in which
participants connect numbers (Trails A) or alternate numbers and
letters (Trails B) . They observed a task6genotype interaction
for the more complex Trails B task with the worst performance
observed in the COMT Val+/DRD2/ANKK1-Taq-Ia A1+
group. Again, task complexity mattered. We propose that a
COMT6DRD2/ANKK1-Taq-Ia interaction extends across tasks
that do and do not require manipulation in WM, but these
interactions become significant when manipulation is required.
The present findings support the analysis of both reaction time
and accuracy measures. We observed the emergence of a speed-
accuracy tradeoff selectively in A12 subjects such that those with
the Met/Met genotype reacted more quickly but less accurately.
This trend is consistent with the view that corticostriatal circuitry is
involved in mediating speed-accuracy tradeoffs (reviewed in [51–
52]. DRD2/ANKK1-Taq-Ia genotype determines striatal D2
levels which may alter speed-accuracy thresholds via basal ganglia
output to the PFC. Thus, lower striatal D2 in A1+ participants
leads to a higher threshold requirement for responses. The benefit
of optimal dopamine levels in the PFC may relate to recent
findings that when accuracy is emphasized sensory accumulation
proceeds near-optimally .
Figure 3. Accuracy findings. abc) These figures follow the same conventions as in Figure 2 but plot the accuracy (corrected recognition) data.
WM and Genetic Polymorphisms
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Delay Duration and Temporal Processing
We manipulated WM demands by varying the number of
memoranda and the maintenance duration. The analysis revealed
non-interacting main effects for both factors without observing any
influence of genotype on either factor. Previous results suggest a
double dissociation between COMT and DRD2/ANKK1-Taq-Ia
genotypes between short, sub-second and long, supra-second
duration processing . The alternative possibility remained that
differences in COMT groups at the supra-second range were due
to different WM demands. Because the effect of COMT genotype
on WM performance was independent of either set size or delay
we can lay this alternative to rest and conclude that the previously
reported effects of COMT on duration processing reflected
differences in temporal processing. Finally, we note that there
was a significant increase in reaction time variability as a function
of DRD2/ANKK1-Taq-Ia genotype (A1+.A12). This is consis-
tent with previous reports identifying greater variability in this
group at sub-second delay durations . The influence of the
DRD2/ANKK1-Taq-Ia genotype may thus extend beyond the
sub-second durations originally reported at least when these WM
task demands are imposed.
Molecular Genetics and Synthesis
Studies looking at the molecular genetics of behavior are
necessarily limited in scope. An overly simplistic trap would be to
assign a ‘good’ or ‘bad’ label to a particular genotype. The COMT
Val158Met literature provides an example case. In the cognitive
literature the COMT Met/Met genotype is associated with
superior executive function. However, other literatures reveal a
‘flip side’ and people with the Met/Met genotype are more
vulnerable to alcoholism [54,55]; reviewed in , post-traumatic
stress disorder , and nicotine addiction (reviewed in .
Furthermore, Val+ individuals perform well on task switching
paradigms, which require flexibility ; reviewed in [60,61]. This
balance between stability and flexibility may reflect the tuning of
cognitive representations . In summary, in the COMT
Val158Met polymorphism the low-activity Met allele offers certain
benefits to cognition and the high-activity Val allele offers others.
In contrast, there appears to be a simpler story with regard to
the A1+ polymorphism of the DRD2/ANKK1-Taq-Ia gene. To
date, the presence of A1+ appears to be uniformly negative. It is
associated with poorer cognitive performance but it is also
associated with alcoholism, problem gambling, smoking and other
addictions . Importantly, the question remains open as to how
these isolated experimental findings can be synthesized to develop
a better understanding of brain function. One challenge to this
synthesis is to bring together findings from a diverse range of
literatures in which the findings have been published.
Conceived and designed the experiments: MEB MW HBC. Performed the
experiments: MEB JAS MW FWL. Analyzed the data: MEB JAS MW
FWL. Contributed reagents/materials/analysis tools: FWL. Wrote the
paper: MEB MW FWL HBC.
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Figure 4. The hypothetical relationship between dopamine (DA) concentration and performance as measured by accuracy (left) or
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