Dopaminergic and Cholinergic Modulations of Visual-Spatial Attentionand Working Memory: Insights From Molecular Genetic Research

and Implications for Adult Cognitive Development

Viola S. Störmer and Susanne PassowMax Planck Institute for Human Development, Berlin, Germany

Julia BiesenackMax Planck Institute for Molecular Genetics, Berlin, Germany

Shu-Chen LiMax Planck Institute for Human Development, Berlin, Germany

Attention and working memory are fundamental for selecting and maintaining behaviorally relevantinformation. Not only do both processes closely intertwine at the cognitive level, but they implicatesimilar functional brain circuitries, namely the frontoparietal and the frontostriatal networks, which areinnervated by cholinergic and dopaminergic pathways. Here we review the literature on cholinergic anddopaminergic modulations of visual-spatial attention and visual working memory processes to gaininsights on aging-related changes in these processes. Some extant findings have suggested that thecholinergic system plays a role in the orienting of attention to enable the detection and discrimination ofvisual information, whereas the dopaminergic system has mainly been associated with working memoryprocesses such as updating and stabilizing representations. However, since visual-spatial attention andworking memory processes are not fully dissociable, there is also evidence of interacting cholinergic anddopaminergic modulations of both processes. We further review gene–cognition association studies thathave shown that individual differences in visual-spatial attention and visual working memory areassociated with acetylcholine- and dopamine-relevant genes. The efficiency of these 2 transmittersystems declines substantially during healthy aging. These declines, in part, contribute to age-relateddeficits in attention and working memory functions. We report novel data showing an effect of dopamineCOMT gene on spatial updating processes in older but not in younger adults, indicating potentialmagnification of genetic effects in old age.

Keywords: neuromodulation, attention, working memory, aging

One critical aspect of human cognition is the ability to selec-tively attend to relevant and ignore irrelevant information in thephysical environment and in mental representations. Efficient se-lection and active maintenance of behaviorally relevant informa-tion are the key processes that guide and optimize one’s perception

and action. Over the lifespan, the ability to selectively processinformation is subject to substantial change. Children and adoles-cents have more difficulties in tasks that require selective process-ing, because the attention networks are still maturing (Rueda et al.,2004; Waszak, Li, Hommel, 2010). During the course of healthyaging, the ability to ignore irrelevant information declines substan-tially (for reviews, see Guerreiro, Murphy, & Van Gerven, 2010;Kok, 2000). Likewise, processes of maintenance and updating ofinformation, termed working memory (WM), are less mature inchildhood and adolescents (e.g., Cowan, Naveh-Benjamin, Kilb, &Saults, 2006; Gathercole, 1999) and are impaired in old age (e.g.,Babcock & Salthouse, 1990; Cowan et al., 2006; Li et al., 2008;Park & Payer, 2006; Schmiedek, Li, & Lindenberger, 2009).

Attention and WM have mostly been studied independently, butmore recent research has suggested that attention and WM interactat multiple levels. The notion that attention forms the basis of WMis conceptualized in Cowan’s (1995) integrative framework ofattention and memory. Cowan postulated that contents of WM areactivated representations from long-term memory (or perception)that are temporarily in the focus of attention. Empirical studieshave supported a close interplay between attention and WM pro-cesses, both at the cognitive and the neurofunctional level. Fur-thermore, recent advancements in molecular genetics have openednew avenues for exploring how genetic variation affecting neu-

Editor’s Note. Jacquelynne S. Eccles served as the action editor for thisarticle.—JSE

This article was published Online First November 21, 2011.Viola S. Störmer and Susanne Passow, Max Planck Institute for Human

Development, Berlin, Germany; Julia Biesenack, Max Planck Institute forMolecular Genetics, Berlin, Germany; Shu-Chen Li, Max Planck Institutefor Human Development.

Viola S. Störmer was supported by the International Max Planck Re-search School: The Life Course: Evolutionary and Ontogenetic Dynamics(LIFE), and Susanne Passow was supported by the Max Planck Interna-tional Research Network on Aging (MaxNetAging). We thank the studentassistants for help with data collection and Florian Schmiedek for hissupport and advice regarding the spatial updating task.

Correspondence concerning this article should be addressed to Viola S.Störmer or Shu-Chen Li, Max Planck Institute for Human Development,Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany.E-mail: stoermer@mpib-berlin.mpg.de or shuchen@mpib-berlin.mpg.de

Developmental Psychology © 2011 American Psychological Association2012, Vol. 48, No. 3, 875–889 0012-1649/12/$12.00 DOI: 10.1037/a0026198

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rotransmitter systems may underlie individual differences in atten-tion and WM performance. Of particular interest are theoreticalmodels and empirical data that have linked age-related differencesin attention and WM to age-graded differences in these neurotrans-mitter systems. This review selectively focuses on recent studiesinvestigating the effects of genetic variations relevant for neu-rotransmission underlying visual-spatial attention and WM pro-cesses. The article is organized as follows: Evidence for cognitiveand neurofunctional interactions between visual-spatial attentionand WM is presented first, followed by an overview of the neu-rotransmitter systems related to attention and WM. The core of thearticle focuses on evidence that addresses the tripartite interrela-tions between genotypes, attention, and WM, as well as on howgene–cognition relations are modulated by age. Studies on atten-tional processes relevant for visual-spatial WM, as well as findingsfrom attentional orienting tasks that are more independent of WMoperations, are discussed. Finally, we report novel data that illus-trate how genetic effects on spatial WM updating can be magnifiedin old age (cf. Lindenberger et al., 2008).

Working Memory Interacting With Attention:Cognitive and Neurofunctional Associations

Working memory (WM) refers to the system that keeps infor-mation active or available for immediate access and manipulation.The capacity of WM (i.e., how many items can be actively main-tained simultaneously) is limited (Luck & Vogel, 1997; Miller,1956) and declines with healthy aging (e.g., Babcock & Salthouse,1990; Cowan et al., 2006; Li et al., 2008; Schmiedek, Li, &Lindenberger, 2009). WM capacity limit necessitates careful se-lection of what information to encode, to actively maintain, and toretrieve (Awh, Vogel, Oh, 2006; Cowan, 1995). Top-down atten-tional control guides this selection by enhanced processing ofrelevant over irrelevant information (Desimone & Duncan, 1995;Kastner & Ungerleider, 2000). Classically, selective attention hasbeen studied in the visual-spatial modality, showing that the ori-enting of attention to a location in space can improve and accel-erate target detection and discrimination at that location (Posner,1980). Efficient processing of stimuli in a person’s visual envi-ronment is one significant aspect for forming stable representa-tions that are subsequently maintained in WM. That is, selectiveattention defines what information enters WM and dictates thequality of the subsequent representation (Awh & Vogel, 2008;Rutman, Clapp, Chadick, & Gazzaley, 2010). Whereas it seemsstraightforward to attribute mechanisms of attention to the encod-ing phase of WM, it might be less intuitive to link attentionalprocesses to the maintenance phase of WM. However, not onlyinitial information processing during perception but also the sub-sequent maintenance of information involves selective attention(for an overview, see Awh & Jonides, 2001). Only by selectivelyactivating relevant information over irrelevant information areWM contents made available. Several empirical studies have il-lustrated how the focusing of attention influences the efficiency ofWM maintenance. For example, Griffin and Nobre (2003) showedthat spatial orienting cues that appear after the to-be-rememberedstimuli can retrospectively enhance memory performance to adegree similar to that of cues that appear before the memorydisplay, implying that focusing attention facilitates WM perfor-mance (Griffin & Nobre, 2003). In fact, it has been proposed that

the maintenance of spatial information relies on an attention-basedrehearsal mechanism (Repovs & Baddeley, 2006; Smyth &Scholey, 1994). Consistent with this idea, a series of behavioraland neuroimaging studies showed that attention is allocated tospatial locations held in WM during the retention interval (Awh,Anllo-Vento, & Hillyard, 2000; Awh, Jonides, & Reuter-Lorenz,1998; Awh et al., 1999). For instance, Awh et al. (1999) usedfunctional magnetic resonance imaging (fMRI) to measure theblood-oxygen-level-dependent (BOLD) signal—an indirect mea-sure of neural activity—while participants performed a spatialWM task. Participants were asked to remember three locations ofstimuli that were presented in either the left or right visual field.During the retention interval, a bilateral flickering checkerboardwas presented to measure the differences in sensory activity invisual cortex contralateral and ipsilateral to the memorized stim-ulus locations. When the to-be-memorized stimuli were located inthe right visual hemifield, the neural response in visual cortex wasgreater in the left hemisphere, and vice versa. The amplification ofthe visual response contralateral to the to-be-remembered locationsis similar to what is usually found for attended locations (Heinzeet al., 1994; Hillyard & Anllo-Vento, 1998). Similarly, an electro-physiological study investigated the early visually evoked re-sponse of the event-related potential (ERP) during the maintenancephase of a spatial WM task (Awh et al., 2000). In particular,task-irrelevant probes were presented during the retention intervalwhile participants remembered locations of stimuli. It was foundthat early sensory ERP components showed larger amplitudes toprobes that appeared at memorized locations relative to nonmemo-rized locations. The amplitude modulations were similar in latencyand voltage distribution to amplitude modulations of sensory ERPsduring a spatial attention task (Awh et al., 2000; see also Fukuda& Vogel, 2009; Lepsien & Nobre, 2007). These findings provideevidence that attention-induced modulations in sensory areas playa key role in keeping spatial representations active in WM.Whereas spatial orienting tasks mostly tap into the transient de-ployment of attention and its immediate effect on perception, WMtasks involve both the initial orienting to information during en-coding and the sustained process of keeping that information in thefocus of attention over time. Thus, the transient orienting ofattention during spatial cuing tasks can be seen as separate fromsustained WM processes, such as memory maintenance and up-dating. In contrast, sustained maintenance and updating processesof visual-spatial information are difficult to fully delineate fromattentional processes.

Focusing attention during WM maintenance is especially im-portant when interference occurs. Such interference from distract-ing stimuli can disrupt WM maintenance processes and lead toclear drops in WM performance, particularly in old age (e.g.,Gazzaley, Sheridan, Cooney, & D’Esposito, 2007; Li et al., 2008;Solesio-Jofre et al., 2011). Specifically, age-related impairmentsare often observed only in conditions with both high memory loadand distraction (Gazzaley et al., 2007). Furthermore, when stimulithat are presented during the delay period of a visual WM taskneed to be either ignored (i.e., distractions) or attended (i.e.,interruptions), older adults are more affected by these interferingstimuli relative to younger adults (Clapp & Gazzaley, 2010).

The allocation of attention as well as the stabilization andupdating of information in WM is mediated by similar parietal andfrontal brain regions. FMRI studies have revealed that attentional

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control is associated with activation in the dorsal parietal cortexalong the intraparietal sulcus (IPS), extending dorsomedially intothe superior parietal lobule (SPL) and anteriorly toward the post-central sulcus and the dorsal frontal cortex at the intersection of theprecentral and superior frontal sulci (Corbetta, 1998; Corbetta,Kincade, Ollinger, McAvoy, & Shulman, 2000; Hopfinger,Buonocore, & Mangun, 2000; Kastner, Pinsk, De Weerd, Desi-mone, & Ungerleider, 1999; for an overview, see Corbetta &Shulman, 2002). Similarly, WM involves the interaction betweenseveral networks in frontal and parietal brain regions (D’Esposito,2007; Postle, 2006). Specifically, WM has been associated withactivation in the middle frontal gyrus (MFG), the inferior frontalgyrus (IFG) and the superior frontal gyrus (SFG; Courtney, Un-gerleider, Keil, & Haxby, 1997; D’Esposito, 2007; Postle, 2006).Some studies have revealed different activation patterns for dif-ferent processes involved in WM. In particular, the dorsolateralprefrontal cortex (PFC) seems to be mostly involved in manipu-lating and updating WM contents, whereas the ventrolateral PFCseems to be mostly involved in maintaining and retrieving WMcontents (D’Esposito, Postle, Ballard, & Lease, 1999; Owen et al.,1999). Moreover, activation in the IPS increases as the number ofto-be-stored items increases and reaches an asymptote once indi-vidual WM capacity is exceeded, indicating that the IPS is sensi-tive to WM capacity (Todd & Marois, 2004; Vogel & Machizawa,2004). In addition to these frontal and parietal areas, striatal brainregions are involved in stabilizing and updating representation(Bledowski, Kaiser, & Rahm, 2010; Leung, Oh, Ferri, & Yi,2007). More direct evidence for overlapping brain regions ofattention and WM is provided by studies that compared brainactivation between attentional and WM tasks in within-subjectdesigns. By instructing participants to shift attention either toexternal stimuli or between locations maintained in WM, Nobre etal. (2004) found a large network of overlapping brain regionsinvolving parietal, frontal, and visual cortices. In particular, over-lapping activation was found in the posterior parietal cortex bilat-erally, including the precuneus, the superior parietal lobule, andthe IPS as well as the frontal eye fields and more inferiorly in thelateral premotor cortex bilaterally (Nobre et al., 2004). Differencesin activation were also observed, with right parietal regions beingmore active during spatial orienting to external stimuli and frontalregions being more activated during spatial orienting to internalrepresentations (see also Lepsien & Nobre, 2006; Lepsien, Thorn-ton, & Nobre, 2011). Another study suggested functional overlapduring a verbal WM and spatial attention task and reported com-mon activation in several frontal and parietal brain regions, includ-ing the IPS, the ventral precentral sulcus, the supplementary motorarea, the frontal eye field, the thalamus, the cerebellum, the lefttemporal cortex, and the right insula (LaBar, Gitelman, Parrish, &Mesulam, 1999). Taken together, these findings indicate that sim-ilar neural substrates implicate attention and WM processes. Theoverlap in brain networks is likely to be stronger when WMrequires attention-based rehearsal or updating mechanisms orwhen WM maintenance is especially demanding, for instancewhen WM load is high or interference is strong.

Neuromodulation of Attention

Other than investigations at the behavioral and brain functionallevels, increasing research effort has been devoted to the neu-

rotransmitter systems that are associated with the attention net-work. The IPS, underlying attentional control (Corbetta & Shul-man, 2002), is innervated by the ascending basal forebraincholinergic pathway (see Figure 1; Everitt & Robbins, 1997),which suggests that the cholinergic neurotransmitter system mayplay a critical role in attentional processes (Sarter & Parikh, 2005).In animals it has been shown that the disruption of cholinergicfibers originating in the basal forebrain results in persistent atten-tional impairment (Voytko et al., 1994). Furthermore, attentionaldeficits seen in Alzheimer’s disease have been suggested to arisepartly from cholinergic dysfunction (Nobili & Sannita, 1997; Para-suraman, Greenwood, Haxby, & Grady, 1992).

Cholinergic neurotransmission in the brain involves the presyn-aptic transporters (Sarter & Parikh, 2005) as well as the postsyn-aptic muscarinic and nicotinic receptors (Alkondon, Pereira,Eisenberg, & Albuquerque, 2000). Animal studies have shown thattasks that require sustained attention are associated with increasedcapacity of high-affinity choline uptake transporters in the rightmedial frontal cortex (Apparsundaram, Martinez, Parikh, Kozak,& Sarter, 2005). Studies on cholinergic modulation of visualattention focusing on receptor mechanisms indicate that the stim-ulation of muscarinic receptors affects activation in the primaryvisual cortex, whereas actions of the nicotinic receptors are mostprominent in the parietal cortex (Mentis et al., 2001; Thiel & Fink,2008). These findings suggest that the cholinergic muscarinicreceptors modulate attention during sensory processing in visualcortex, whereas the nicotinic receptors exert their effects in highersensory areas or underlie the employment of top-down attentionalcontrol (Herrero et al., 2008).

Several pharmacological studies in humans have supported therole of the cholinergic system for attention. For instance, Bentley,Husain, and Dolan (2004) investigated the neural and behavioraleffects of the anticholinesterase physostigmine while participantsperformed a sustained spatial attention task. Physostigmine indi-rectly stimulates both nicotinic and muscarinic receptors. On thebehavioral level, physostigmine led to a reduction in overall reac-tion time and increased accuracy. On the neural level, physostig-mine increased activation in extrastriate visual cortex, but theenhancement was spatially unspecific. In particular, activity con-

Figure 1. Schematic diagram of dopaminergic and cholinergic pathwaysin the brain.

877NEUROMODULATION, ATTENTION, AND WORKING MEMORY

tralateral and ipsilateral to the attended side was enhanced simi-larly, reducing the retinotopic selectivity that characterizes spatialattention. This finding was interpreted in terms of cholinergicenhancement reducing the costs rather than increasing the benefitsof spatial attention. Another recent fMRI study investigated theeffects of nicotine while participants performed a typical spatialcuing task (Thiel & Fink, 2008). It was found that nicotine reducedreaction times to invalidly cued targets, thus reducing the costs ofattentional reorienting. This finding possibly indicates that nicotineintake results in easier disengagement of attention (Witte, David-son, & Marrocco, 1997) or reduced reliance on top-down cuing(Yu & Dayan, 2005). These behavioral effects of nicotine wereaccompanied by reduced activity in various parietal and medialtemporal regions (i.e., the right inferior parietal cortex, the inferiorparietal cortex, the right middle temporal gyrus, the left middlefrontal gyrus, the left parahippocampal gyrus). In contrast to thestudy by Bentley et al. (2004), here there was no effect of nicotineon activation in the visual cortex. These results might suggestspecific effects of the drugs employed in the two studies: Whereasnicotine specifically affects nicotinic receptors whose actions aremostly observed in parietal cortex, physostigmine affects bothnicotinic and muscarinic receptors, the latter being mainly presentin visual cortex (Thiel & Fink, 2008).

Besides the cholinergic system, the roles of striatal dopamineinputs in regulating attentional control functions in humans haverecently been investigated (e.g., Landau, Lal, O’Neil, Baker, &Jagust, 2009). For instance, in an earlier positron emission tomog-raphy (PET) study using 11C-labeled raclopride as the radioligand,Koepp et al. (1998) showed evidence for transient striatal dopa-mine releases while younger adults played a video game thatrequired sustained and selective visual attention.

Neuromodulation of Working Memory

It is well established that WM is modulated by the dopaminergicsystem, particularly through the striatal-frontal pathway (e.g.,Cools, Sheridan, Jacobs, & D’Esposito, 2007; Durstewitz, Sea-mans, & Sejnowski, 2000; Gruber, Dayan, Gutkin, & Solla, 2006;Seamans & Yang, 2004). The physiological effects of dopamine(DA) are mediated pre- and postsynaptically. Relevant for dopa-minergic neurotransmission are the dopamine transporters (DATs)and receptors. The DATs regulate the temporal and spatial activityof DA by rapid reuptake of DA into presynaptic terminals (Giros,Jaber, Jones, Wightman, & Caron, 1996), whereas DA receptors(the D1 and D2 families) regulate postsynaptic DA concentration(Cools, Gibbs, Miyakawa, Jagust, & D’Esposito, 2008). Both DAtransporter and DA receptors are highly concentrated in the stria-tum (Cools et al., 2008; Hall et al., 1994; Kessler et al., 1993). DA

D1 receptors have also been identified in the PFC (Sawaguchi &Goldman-Rakic, 1991). Depending on the site of modulation, DAcan have opposite effects. In the PFC, DA has been shown tostabilize representations by reducing susceptibility to distractions(Cools et al., 2007; Durstewitz et al., 2000; Seamans & Yang,2004), whereas in the striatum DA is thought to be responsible forrapid updating of representations in a task-relevant manner (Coolset al., 2007; Gruber et al., 2006). Furthermore, an extracellularenzyme, catechol-O-methyltransferase (COMT), is known to de-grade DA in the synapses. In humans, COMT is mostly expressedin the PFC and thus has a direct influence on prefrontal DA level(Backman, Nyberg, Lindenberger, Li, & Farde, 2006; Rosa, Dick-inson, Apud, Weinberger, & Elvevag, 2010).

The relation between DA signaling and WM performance can-not be captured by a simply linear function but rather by aninverted -shaped function (Arnsten, 1998; Goldman-Rakic, Cast-ner, & Williams, 2000; Li & Sikström, 2002; Mattay et al., 2003).Neurocomputational approaches have modeled DA’s role in reg-ulating the signal-to-noise ratio of neural information processing(Servan-Schreiber, Printz, & Cohen, 1990) and subsequent influ-ences on the properties of neural representations and the resultingconsequences on WM performance (Li & Sikström, 2002). Forinstance, deficient or excessive dopaminergic modulation can besimulated by attenuating or increasing the gain parameter of aneural network’s activation, respectively. Li and Sikstrom (2002)showed that, both manipulations reduced the signal-to-noise ratioof the neural network and increased random fluctuations in acti-vation, which subsequently reduced the distinctiveness of neuralrepresentations of stored memory items. The less distinct repre-sentation resulted in reduced WM performance that was due toeither excessive or attenuated gain control in the computationalsimulations (see Figure 2; Li et al., 2001; Li & Sikström, 2002).The simulations captured empirical findings of an inverted-shaped function that relates DA signaling to WM performance

(Arnsten, 1998; Sawaguchi & Goldman-Rakic, 1991). Specifi-cally, Sawaguchi and Goldman-Rakic (1991) found that both aninsufficient as well as an excessive level of DA D1 receptorstimulation in the PFC impaired WM performance in rhesus mon-keys. The inverted -shaped function may also underlie oppositeeffects of administration of DA D2 receptor agonists on cognitionacross individuals with different baseline levels of WM capacity.After DA D2 receptor agonist administration, WM performanceincreased in individuals with low WM capacity but decreased inindividuals with high WM capacity (Cools et al., 2007; Frank &O’Reilly, 2006; Kimberg, D’Esposito, & Farah, 1997). Thesecontrasting effects of the DA D2 receptor agonist may reflectdifferences in baseline levels of DA with low DA levels in indi-

Figure 2 (opposite). Panels A–C: Dopaminergic modulation of neuronal noise and representation distinctiveness. The role of dopamine (DA) in affectingneuronal signal-to-noise ratio can be modeled by the gain (G) parameter of the sigmoidal activation function (Li et al., 2001; Servan-Schreiber et al., 1990).Panel A: The neuronal input–response mapping functions of individuals with suboptimal DA modulation because of aging (or disadvantageous genotypes)are captured by less steep activation functions with lower G and signal-to-noise ratio. Panel B: G modulation of signal-to-noise ratio affects randomactivation variability. Panel C: The representational distinctiveness of activation patterns. Panel D: G modulation of processing noise and representationaldistinctiveness capture the inverted-U function relating DA modulation and working memory. Extremely small or large G values result in reduced memorycapacity (adapted from Li et al., 2001, and Li & Sikström, 2002, with permission; copyright Elsevier). Panel E: Empirical evidence of insufficientD1-receptor stimulation on spatial working memory performance (adapted from Arnsten, 1998, with permission; copyright Elsevier).

878 STORMER, PASSOW, BIESENACK, AND LI

Figure 2 (opposite).

879NEUROMODULATION, ATTENTION, AND WORKING MEMORY

viduals with low WM capacity and high DA levels in individualswith high WM capacity. Thus DA D2 receptor agonist adminis-tration either increased the DA level to an optimal level or ex-ceeded the optimal DA level, depending on individual differencesin the baseline DA levels. More recently, Cools et al. (2008)provided empirical evidence for the link between WM capacityand DA level. Specifically, Cools et al. (2008) showed that indi-vidual differences in WM capacity were associated with DAsynthesis capacity in the striatum.

As reported earlier, influences of cholinergic neurotransmissionon executive control functions is primarily seen with attention, butthere is also some evidence that the cholinergic system influencesWM function (Ellis et al., 2006; Green et al., 2005; Levin, Briggs,Christopher, & Rose, 1993; Levin, Kaplan, & Boardman, 1997).Animal studies have shown that nicotine causes significant WMimprovement and a muscarinic antagonist evokes significant im-pairment in WM (Levin et al., 1997). Regarding studies in humans,Green et al. (2005) showed that selective muscarinic antagonismimpaired WM performance, whereas selective nicotinic antago-nism did not. Simultaneous nicotinic and muscarinic antagonism,however, produced the largest impairments. Interestingly, theseeffects occurred for both an object-based and a spatial-basedn-back WM task, suggesting that acetylcholine muscarinic andnicotinic receptor modulation of WM may be domain-general.Further, the findings demonstrate that muscarinic and nicotinicreceptors may interact functionally to modulate WM performancein a synergistic way.

Candidate Genes Relevant for Neuromodulation ofAttentional Processes

Whereas pharmacological studies have examined the effects ofneuromodulation on attention by manipulating the efficiency ofcholinergic neurotransmission directly, more recent research hasshown that polymorphisms of genes that code for cholinergicneurotransmission in the brain are associated with individual dif-ferences in attentional processes (for a review, see Bellgrove &Mattingley, 2008). Most prominently, the neurotransmitter geneCHRNA4 has been linked to individual differences in the orientingof visual-spatial attention. For example, one study that used aclassic spatial cuing paradigm to tax the orienting system ofattention (Posner, 1980) showed that an increasing dose of the Callele (from 0 to 1 to 2 C alleles; i.e., the T/T, T/C, C/C genotypes,respectively) was beneficial for attentional orienting (Parasura-man, Greenwood, Kumar, & Fossella, 2005). Specifically, Para-suraman et al. (2005) found that increasing C allele dosage wasrelated to an increase in reaction time benefits and a decrease inreaction time costs. At the same time, C allele dosage had no effecton performance in a spatial WM task, displaying the specificity ofthe CHRNA4 gene for attentional orienting. Another study em-ployed a visual search task in which the location of the target wascued with variable precision (Greenwood, Fossella, & Parasura-man, 2005). In particular, the size of the cue was parametricallyscaled and could surround either one stimulus location or up tonine stimuli, thereby changing the scaling of attentional selection.When cues were small, genotype did not affect search times.Different from the findings of the spatial cuing task by Parasura-man et al. (2005), with increasing cue size, however, carriers ofhigher C allele dosage showed slower search times, even more so

for conjunction search compared with a simple feature searchcondition. The authors argued that the selective genotype effect forimprecise cues denotes that processes of disengaging and reorient-ing attention are particularly affected by this genotype. Similar tothe attentional scaling study, Espeseth, Sneve, Rootwelt, andLaeng (2010) reported detrimental effects of the C allele ofCHRNA4 on performance in a multiple-object tracking (MOT)task and a visual search task. In particular, homozygotes for the Tallele showed better tracking performance in the MOT task andfaster target detection times in the visual search task, indicatingthat increasing C allele dosage was disadvantageous. The geneticeffects on performance emerged only in the high attentional loadcondition. The findings that C allele carriers showed slower reac-tion times and lower performance in the studies of attentionalscaling, MOT, and visual search are at odds with the results of theprevious study showing better attentional orienting in a spatialcuing task for C allele carriers (Parasuraman et al., 2005). Theresults may point to the specificity of molecular effects regardingdifferent aspects of the attention system. They might denote thathigher C allele dosage is associated with deficiencies in selectingand reorienting to information in cluttered visual scenes that wouldrequire disengagement in addition to simple orienting (i.e., visualsearch, MOT; Espeseth et al., 2010; Greenwood et al., 2005),whereas higher C allele dosage might be beneficial for the orient-ing of attention (Parasuraman et al., 2005).

Genes that code different components of the cholinergic systeminteract with each other in affecting attention, a phenomenoncalled epistasis. To understand the specificity of cholinergic neu-rotransmission and different aspects of attentional functioning,Greenwood, Lin, Sundararajan, Fryxell, and Parasuraman (2009)investigated interactive effects of the cholinergic nicotinic(CHRNA4) and muscarinic (CHRM2) receptor genes on the scal-ing of the attentional focus. Using the same cued visual search taskdescribed earlier, the authors showed the synergistic effects of thetwo genotypes. Participants who were CHRNA4 C/C homozy-gotes and also CHRM2 T/T homozygotes showed the strongesteffects of attentional scaling. The findings were interpreted interms of Sarter’s distinction of tonic and phasic modes of cholin-ergic activity (Briand, Gritton, Howe, Young, & Sarter, 2007),concluding that the reorienting of attention after invalid cuesrequires a phasic response (nicotinic), whereas the orienting ofattention to valid cues requires a tonic response (muscarinic). Theauthors further tested the specificity of the effect by relating thevariation in genotypes to WM performance in the same partici-pants. These two genotypes did not affect WM performance, inline with previous findings (Greenwood, Lin, et al., 2009).

The effects of genotype on attentional performance are alsoaccompanied by differences in brain activation. Using fMRI, Win-terer et al. (2007) reported that in a visual oddball task in whichinfrequently presented stimuli need to be detected, T/T homozy-gotes of CHRNA4 showed the strongest and C/C homozygotes theweakest BOLD response in left parietal cortex. Similarly, in anelectrophysiological study using an auditory and visual oddballtask, Espeseth, Endestad, Rootwelt, and Reinvang (2007) foundlarger ERP amplitudes in early sensory components (P1, N1) forT/T homozygotes relative to C carriers. The observation of largercue effects on the behavioral level (Parasuraman et al., 2005) incombination with reduced brain activity for C carriers has beeninterpreted as more efficient processing with increasing C allele

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dosage (Greenwood et al., 2005). At present the efficiency inter-pretation can only be considered tentatively, given inconsistentresults on whether the C allele is beneficial for attentional pro-cesses in general.

The associations between genes relevant for cholinergic modu-lation and attention have also gained support from child develop-mental research. For instance, it was found that in 7-month-oldchildren the CHRNA4 polymorphism was related to anticipatorylooking, an indicator for the development of executive attention(Posner & Rothbart, 2009). More specifically, homozygotes of theT allele of CHRNA4 showed better performance in anticipatorylooking relative to C homozygotes. The effect was reversed in2-year-olds, with C homozygotes obtaining higher scores in ef-fortful control, another aspect of executive attention, relative to Thomozygotes (Voelker, Sheese, Rothbart, & Posner, 2009). Theseage-dependent effects of CHRNA4 in children may indicate thatgenetic variation affects the trajectories of the development of theattentional control network differentially (Posner & Rothbart,2009). Also, they may reflect interactions between maturation ofthe attentional control network and individual differences in ge-netic effects (Lindenberger et al., 2008). Longitudinal studiescould potentially aid our understanding of how genetic influenceson neuromodulation may change throughout child and adult de-velopment. In sum, several studies have shown links betweenpolymorphisms in genes coding for cholinergic neurotransmissionand visual-spatial attention. Even though the results are mixed, theexisting findings do support the cholinergic system’s role in mod-ulating processes of attention.

As reported earlier, DA has mostly been associated with WMprocesses, but there is some evidence that DA-relevant genes playa role in attentional processes, too. There is some consensus thatattention deficits in psychiatric disorders, such as attention-deficit/hyperactivity disorder (ADHD) are in part associated with dys-functions of the DA system (Cook et al., 1995; Swanson, Baler, &Volkow, 2011). Other studies that do not involve the clinicalpopulations also lend support to DA’s role in affecting attention.For instance, a recent study employed a spatial attention task andinvestigated effects of genetic variation in the dopamine trans-porter (DAT) gene in healthy children and adolescents, ages 9–16years (Bellgrove et al., 2007). DAT serves as DA reuptake and ismostly expressed in the striatum and to a much less extent inprefrontal regions. In that study, two variable number of tandemrepeat (VNTR) polymorphisms of DAT1 were examined (lo-cated in the region 3�UTR and in intron 8 of the gene). Ho-mozygotes for the 10-repeat allele of the 3�UTR DAT1 poly-morphism and homozygotes of the 3-repeat allele of the intron8 polymorphism exhibited an attention deficit for left-sidedstimuli, whereas heterozygotes (10/9-repeat and 3/2-repeat) didnot. Additionally, the examination of the 10/3 haplotype (com-bination of 10-repeat and 3-repeat allele) revealed spatiallyspecific attention deficits for carriers of the risk alleles (10/10-repeat and 3/3-repeat alleles). These findings suggest that in-creased expression of DAT is associated with higher costs inspatial attention. In contrast to other findings discussed earlier,no effect of the cholinergic polymorphism (�-4 nicotinic recep-tor gene C1545T polymorphism) was observed. Another studyreported an association between the development of executiveattention as measured with the attention network test and vari-ation of the DAT1 gene in 4- and 6-year-old children (Rueda,

Rothbart, McCandliss, Saccomanno, & Posner, 2005). In theexecutive attention task, carriers of the long 10-repeat allele hadlower conflict costs in reaction times compared with carriers ofthe short/long or short form of the allele (9/10-repeat, 9-repeat,respectively). Further, there is evidence that the Val/Met poly-morphism of the COMT gene is related to anticipatory lookingin 18- to 21-month-old children (Voelker et al., 2009). Thesefindings imply that variations in genes that code for dopami-nergic neurotransmission play a role in attentional processesand their development.

Candidate Genes Relevant for Neuromodulation ofWorking Memory Processes

Variations in single nucleotide polymorphisms (SNPs) of thecatecholamine-O-methyltransferase (COMT) gene and of the DAD2 receptor (DRD2) gene have been shown to influence WMperformance (e.g., Goldberg et al., 2003; Stelzel, Basten, Montag,Reuter, & Fiebach, 2009; Xu et al., 2007). The association likelystems from the effect COMT has on PFC function (e.g., Chen etal., 2004; Goldberg et al., 2003; Lindenberger et al., 2008; Meyer-Lindenberg et al., 2006). Specifically, COMT enzymatic activitydegrades DA in the PFC. The Val/Met polymorphism in theCOMT gene leads to changes in PFC enzymatic activity, withabout one third less enzymatic activity in the PFC in the Methomozygotes (Met/Met) relative to Val homozygotes (Val/Val).This results in lower DA availability at prefrontal receptors inVal/Val carriers (Chen et al., 2004). In support of the associationbetween COMT and PFC functioning, Goldberg et al. (2003)found that Met homozygotes performed better than Val homozy-gotes did in a WM task. Specifically, Goldberg et al. used a verbaln-back WM task in which participants viewed numbers that werepresented sequentially on the screen and had to press the buttoncorresponding to the current number (0-back, no WM load) or thebutton corresponding to the number 1-back or 2-back with regardto the current trial. Met/Met carriers showed overall better WMperformance than did Val/Val carriers in the memory conditions.Furthermore, a recent study by Tunbridge et al. (2007) showed thatthe COMT enzyme activity and protein expression in the PFCincreases from neonate to adulthood, with a tendency of decreasingagain in old age. The age-dependent COMT enzyme activityduring child development may also contribute to the protractedpostnatal maturation of the PFC’s DA system.

Another DA-relevant gene of interest is the DRD2/ANKK-Taq-A1 polymorphism, which has been linked to DRD2 receptordensity in the striatum (Holmboe et al., 2010). A1 allele carriersshow a 30%–40% reduction in DRD2 receptor density relative toA2 homozygotes (Ritchie & Noble, 2003). Another SNP of theDRD2 gene, the DRD2 C957T (rs6277), has also been related tochanges in striatal and extrastriatal DRD2 receptor density. Spe-cifically, C/C carriers of this SNP have shown a higher synapticDA level in the striatum and higher extrastriatal DRD2 densitycompared with T/T carriers (Hirvonen et al., 2005, 2009). Xu et al.(2007) compared C/C carriers with T/T carriers in their perfor-mance in the Word Serial Position Test, which requires the reten-tion of the serial position of six words over a period of 9 s. C/Ccarriers performed at a lower range than did the T/T carriers. Thisfinding contradicts the genetic effects at the molecular level,indicating that potential interactions between genes need to be

881NEUROMODULATION, ATTENTION, AND WORKING MEMORY

considered (Xu et al., 2007). A recent study provided evidence forgene– gene interactions between the COMT and the DRD2/ANKK-Taq-A1 polymorphism affecting subprocesses of WM.Stelzel et al. (2009) tested their participants on a battery of WMtasks that tap into three subprocesses of WM: (a) the ability tostore contents in WM (WM maintenance), (b) the ability to rear-range WM contents according to a given rule (WM manipulation),and (c) the ability to suppress irrelevant information that interfereswith WM maintenance (WM inhibition). Performance in WMmanipulation was influenced by an interactive effect of the COMTand the DRD2 polymorphism, whereas performance in WM main-tenance and WM inhibition was not (Stelzel et al., 2009). In detail,Met/Met homozygotes outperformed Val carriers only when D2receptor density could be considered high (i.e., when the A1 alleleof the DRD2/ANKK-Taq-A1 polymorphism was absent). Theepistasis suggests that an optimal balance between prefrontal DAavailability and striatal DRD2 receptor density is crucial for effi-cient WM manipulation.

Direct evidence for links between individual differences incholinergic candidate genes and individual differences in WMperformance is still relatively scarce. However, recent studies havereported genetic epistasis between the cholinergic and dopaminer-gic system in affecting WM performance (Markett, Montag, &Reuter, 2010; Markett, Montag, Walter, & Reuter, 2011), suggest-ing that both neurotransmitter systems interact in affecting WM, asdiscussed in more detail later.

Interplay Between Attention, Working Memory, andGenotypes

As described earlier, attention and WM processes are closelyrelated at the cognitive level, and they share common brain net-works as well. This raises the question of how the genotypesrelevant for the cholinergic and dopaminergic system may interactin affecting attention and WM. Whereas the cholinergic system hasmostly been associated with processes of attention (i.e., orienting,detecting, and discriminating stimuli), the dopaminergic systemhas been shown to be important for WM processes (i.e., updatingand stabilizing representations). However, this distinction is notentirely clear. Some studies have revealed that genes coding for thedopaminergic system also play a role in attention tasks (e.g.,Bellgrove et al., 2007), and the cholinergic system has beenreported to be relevant for WM processes (Green et al., 2005).These effects possibly arise from the functional overlaps of the twoprocesses at the cognitive, cortical, and neurochemical levels.Moreover, processes of development (Posner, Rothbart, Sheese, &Voelker, 2012; Rueda et al., 2005; Wahlstrom, White, Luciana,2010) or aging (Lindenberger et al., 2008; Nagel et al., 2008) arelikely to affect neuromodulatory and genetic effects on cognitivefunctions. Nevertheless, it is difficult to fully disentangle WMprocesses from attentional processes. Whereas attentional orient-ing tasks are more independent from WM, many WM tasks taxattentional orienting processes too. This relation is most obvious inthe context of visual-spatial WM, in which the maintenance orupdating of stimulus locations is most likely accomplished by anattention-based rehearsal mechanism (Awh et al., 2006; Repovs &Baddeley, 2006; Smyth & Scholey, 1994). Consequently, it re-mains difficult to completely delineate which neurotransmittersystem might be solely responsible for one or the other process.

Recent studies have addressed this issue directly by trying todisentangle attentional and WM processes in the same task. Onestudy used a spatial WM task and manipulated attention by pre-cuing the memory display with a scaled cue (Greenwood, Sunda-rarajan, et al., 2009). WM performance increased when the focusof attention decreased (i.e., when the cue was small), indicatingthat the attentional manipulation influenced WM performance.This effect was largest for those individuals who possessed two Calleles of the cholinergic CHRNA4 gene but also for G allelecarriers of the dopaminergic beta-hydroxylase gene. Furthermore,cholinergic and dopaminergic neurotransmitter systems have beenshown to interact during visual-spatial and phonological WM tasks(Markett et al., 2010, 2011). In more detail, Markett et al. (2010)found a significant interaction between a gene coding forCHRNA4 (rs1044396) and a haplotype block covering three D2receptor polymorphisms (rs1800497, rs6277, rs2283265) onhigher levels of visual-spatial WM load, suggesting that visual-spatial WM performance is modulated by cholinergic and dopa-minergic neurotransmission. Furthermore, using a phonologicalWM task, a similar interaction between CHRNA4 (rs1044396) anda D2 receptor polymorphism (rs6277) was identified (Markett etal., 2011). These results suggest that genetic epistasis between thedopaminergic and the cholinergic systems is modality-independentand that both neurotransmitter systems work hand in hand, ratherthan independently. Depending on which processes of the twosystems are tapped more—such as orienting, resisting interference,or updating—the relative influences of the neurotransmitter sys-tems may differ.

Aging Influences Cholinergic and DopaminergicNeurotransmission

The efficiencies of the dopaminergic and cholinergic systemschange across the lifespan. During childhood and adolescence,when dopaminergic modulation is still maturing and not fullydeveloped (for a review, see Benes, 2001), the immature DAsystem constraints WM performance (e.g., Diamond, 1996; Liotti,Pliszka, Perez, Kothmann, & Woldorff, 2005; Tunbridge et al.,2007). On the other end of the lifespan, namely in old age,dopaminergic modulation declines markedly, also leading to im-pairments in WM functioning (Backman et al., 2006; Li et al.,2010). In more detail, aging affects pre- and postsynaptical dopa-minergic neurotransmission in the striatum and the PFC. Postmor-tem studies as well as receptor imaging studies have indicatedmarked age-related losses of DAT and D1/D2 receptor densities inthe striatum and various extrastriatal regions including the PFC.The average decline is estimated to be 5%–10% per decade fromearly to late adulthood (Backman et al., 2006). A study simulatingneurocognitive aging by administrating a DA D1 receptor antag-onist showed decreased spatial WM performance as well as re-duced BOLD signal in frontoparietal regions (Fischer et al., 2010).There is also evidence for clear age-related loss of D2 receptorbinding not only in the PFC and striatal regions but also in thetemporal, the parietal, and the occipital cortex, as well as in thehippocampus, the amygdala, and the thalamus (Backman, et al.,2006). The inverted -shaped function of DA signaling (Arnsten,1998; Goldman-Rakic et al., 2000; Mattay et al., 2003) is wellsuited to model the link between changes in WM performanceacross the lifespan and age-related changes in DA signaling. Ac-

882 STORMER, PASSOW, BIESENACK, AND LI

cording to this function, both deficient and excessive DA modu-lations lead to less distinct neural representations of stored mem-ory items and consequently limited WM capacity (Li & Sikström,2002).

Given that the function relating brain resources and performanceis nonlinear (i.e., the inverted -shaped function relating DA andWM performance; e.g., Arnsten, 1998; Li & Sikström, 2002;Mattay et al., 2003), the resource limit hypothesis expects thatgenetic effects on cognition will be larger in individuals with moreconstrained neuroanatomical or neurochemical resources (Linden-berger et al., 2008). Specifically, genetic differences are expectedto be magnified as the DA signaling level falls off the peak of thefunction, as in adolescence due to overactivity of DA signaling(Wahlstrom et al., 2010), as in early childhood due to underactivitybecause the DA system is still maturing (e.g., Diamond, 1996;Tunbridge et al., 2007; for a review, see Benes, 2001), and duringold age due to underactivity because DA signaling is depleted (Li,Lindenberger, Backman, 2010; Lindenberger et al., 2008). Nagelet al. (2008) provided direct empirical support for this predictionand showed that allelic variations in the DA-relevant gene COMThad a greater influence on individual WM differences in olderrelative to younger adults. The theoretically (Li, Lindenberger, &Sikström, 2001) and empirically (for an early review, see Arnsten,1998) based inverted -shaped function relating DA to WM pro-vides a good framework for the relation between DA signaling,cognitive functioning, and aging. As yet, there is no clear evidencefor the shape of the function that relates cholinergic functioningand cognition in general. There is, however, evidence that duringthe course of healthy aging, cholinergic functions decline throughdendritic, synaptic, and axonal degenerations as well as a decreasein trophic support and actual loss of cholinergic neurons (Schliebs& Arendt, 2006, 2011). These age-related changes in the cholin-ergic system have been associated with age-related declines incognitive abilities (Bartus, 2000). In light of interactions betweenthe cholinergic and dopaminergic systems (e.g., Reinvang, Lun-dervold, Rootwelt, Wehling, & Espeseth, 2009), it may be specu-lated that the efficiency of cholinergic modulation becomes in-creasingly important with progressing DA reduction withadvancing age and that genetic effects relevant for the cholinergicsystem may increase in old age.

An Empirical Example: Adult Age Differences inCOMT Effects on Spatial Working Memory Updating

In this section we report one empirical finding exemplifyingadult age differences in genetic effects on visual-spatial updatingperformance. Specifically, we focus on the effects of the DA

COMT gene and the cholinergic CHRNA4 gene on a spatial WMupdating task and an multiple-object tracking (MOT) task. Thetasks require the maintenance of target representations over timewhile concurrently updating the locations of these representations.Similar brain regions are involved in MOT and spatial updating,namely a network of frontal, parietal, occipito-temporal, and stri-atal regions (Culham, Cavanagh, Kanwisher, 2001; Leung et al.,2007). We expected younger adults to perform better than olderadults (Babcock & Salthouse, 1990; Sekuler, McLaughlin, & Yot-sumoto, 2008). As COMT mostly affects dopaminergic neu-rotransmission in prefrontal cortex that is also recruited in tasksinvolving spatial updating operations (Culham et al., 2001; Leunget al., 2007), we hypothesized that the COMT gene would have aneffect on spatial updating performance, with Met/Met homozy-gotes performing better than Val carriers (Goldberg et al., 2003).Furthermore, based on previous findings (Nagel et al., 2008) andthe resource limit hypothesis (Lindenberger et al., 2008), weexpected the COMT effect to be stronger in older adults. Anotherquestion of interest was whether variations in the cholinergicCHRNA4 genotype would also have an effect, given that spatialupdating processes are likely to tap into attentional orientingprocesses.

Sample and Tasks

The data reported here was collected in the covariate sessionand, in part, in the experimental session of two other studies onage-related decline in visual and auditory attention (Passow, et al.,in press; Störmer, Li, Heekeren, & Lindenberger, 2011). Fifty-three younger adults (26 female; 22–32 years of age; M � 26[�3]) and 54 older adults (27 female; 63–79 years; M � 70[�3.8]) were included after giving informed consent. The EthicsCommittee of the Max Planck Institute for Human Developmentapproved the study. Each participant was assessed on marker testsof crystallized intelligence (spot-a-word; Lehrl, 1977) and percep-tual speed (identical pictures; Thurstone & Thurstone, 1941;Wechsler, 1958). As expected, older adults attained lower scoreson perceptual speed relative to younger adults and higher scores inverbal knowledge (see Table 1 for details). Each participant com-pleted a spatial WM updating task and an MOT task. In the spatialupdating task, grids containing a three-by-three array were pre-sented to the participants. The number of grids varied between twoand three as a manipulation of memory load. At the beginning ofeach trial, a black dot was presented in one field of each grid for2,000 ms. Participants had to memorize the location of the dot foreach grid. After the dot disappeared, arrows underneath the gridspointed to different directions (i.e., up, down, left, right), indicat-

Table 1Sample Description and Behavioral Results

Variable Younger adults Older adults

Mean age in years (SD) 26 (�3) 70 (�3.8)Identical pictures test: perceptual speed score (1 SE of the mean) 0.75 (0.02) 0.45 (0.01)Spot-a-word test: verbal knowledge score (1 SE of the mean) 0.56 (0.02) 0.71 (0.02)2-grid spatial updating task: % correct (1 SE of the mean) 90 (2) 58 (3)3-grid spatial updating task: % correct (1 SE of the mean) 65 (3) 36 (2)Estimated tracking capacity m (1 SE of the mean) 1.66 (0.07) 0.96 (0.06)

883NEUROMODULATION, ATTENTION, AND WORKING MEMORY

ing spatial operations. The participant was instructed to update thelocations of the dots according to the direction of the arrow. Forboth the two-grid and three-grid conditions a trial required asequence of six updating operations. At the end of the trial,participants used the computer mouse to indicate the field of thefinal location of the dot within the grid (i.e., free recall; chancelevel is 25% for the two-grid condition and 11% for the three-gridcondition). Participants performed six trials for each of the twogrid sizes (cf. Schmiedek, Hildebrandt, Lövden, Wilhelm, & Lin-denberger, 2009). In the MOT task, participants were confrontedwith eight identical disks in each hemifield that moved randomlyacross the visual hemifield for 7 s. They were instructed to track acertain number of target objects among distractor objects in onehemifield at a time (left or right) while other task-irrelevant objectswere presented concurrently in the other hemifield. At the end ofthe trial, one of the objects was marked as a test probe, andparticipants had to indicate whether the test probe was one of thetarget objects. On half of the trials the test probe was one of thetargets, and on the other half it was one of the nontargets. To varymemory load, the number of to-be-tracked objects was manipu-lated parametrically (two, three, or four objects for younger adults;two or three objects for older adults). Further, motion speed of theobjects (slow, fast) and feature similarity between objects in theattended and unattended hemifield (low, high) was varied. Forthe present study, the main interest was the overall tracking ca-pacity collapsed over all conditions, which provides a measure ofvisual-spatial updating performance. Details of the experimentaldesign and results of the experimental manipulations are reportedelsewhere (Störmer et al., 2011).

Genotyping

DNA was extracted from saliva using standard methods. Thenonsynonymous mutation of the SNP rs4680 of the COMTVal158Met polymorphism and the SNP CHRNA4 rs1044396 ofthe CHRNA4 polymorphism were genotyped. The frequencies ofthe two COMT genotypes were 28% for Met/Met and 72% for anyVal in younger adults and 35% for Met/Met and 65% for any Valin older adults. The frequencies for the two CHRNA4 genotypeswere 21% for C/C and 79% for any T in younger adults and 26%for C/C and 74% for any T in older adults (see Table 2). Thegenotype distributions of both SNPs did not deviate significantlyfrom the Hardy-Weinberg equilibrium (p � .07 for the COMTSNP, and p � .71 for the CHRNA4 SNP).

Analysis and Results

In the first step of analysis, accuracy was examined for each taskseparately. For the WM updating task, mean accuracy (% correct)was measured for each grid separately. As can be seen in Table 1,younger adults performed better than older adults in both gridconditions, and accuracy dropped for both age groups for threegrids compared with two grids. A repeated-measures analysis ofvariance with age group as a between-subjects factor and gridnumber as within-subjects factor supported this, revealing a maineffect of age group, F(1, 105) � 84.74, p � .0001, �2 � .31, anda main effect of grid number, F(1, 105) � 165.65, p � .0001, �2 �.18. No interactions were observed. Younger adults performedapproximately at the same level in the three-grid condition (65%)as older adults did in the two-grid condition (58%, p � .05). Toadjust for performance differences between the two age groups, weused the three-grid condition for younger adults and the two-gridcondition for older participants in the subsequent analysis. For theMOT task, effective tracking capacity was estimated for eachindividual and each condition separately with the formula m’ �n[2(p – 1)], where n is the number of targets (2, 3) and p is thepercent correct (Scholl, Pylyshyn, & Feldman, 2001). Then, effec-tive tracking capacity m was averaged over all conditions for eachindividual. On average, younger adults were able to track 1.66(�0.07; 1 SE of the mean) objects, and older adults were able totrack 0.96 (�0.06; 1 SE of the mean) objects. Statistical resultsconfirmed that there was a main effect of age group, F(1, 92) �52.07, p � .0001, �2 � .36. To obtain a composite score of spatialupdating, in a second step of analysis for each individual a sum-mary score containing performance measures of each of the twotasks was calculated.1 To ascertain that performance in both taskswas related, we calculated correlations between the two perfor-mance scores for each age group. Moderate but reliable associa-tions were observed (younger adults: r � .44, older adults: r � .34;all ps � .05), in support of the idea that a common process (i.e.,spatial updating) underlies both tasks. This enabled us to gain asummary measure for spatial updating performance based on themean of the T scores derived from both tasks. (StandardizedCronbach’s alpha for the composite score was .61 for youngeradults and .49 for older adults. Although these values were lowerthan the common cutoffs of .7 for standard psychometrical scales,we computed the composite score mainly to have a summarymeasure of performance in both tasks).

To investigate the effect of COMT and CHRNA4 on spatialupdating performance, we grouped the participants based on ageand genotype. COMT genotype affected performance in the groupof older adults but did not exhibit an effect in the group of youngeradults. In particular, older adults who were Met/Met carriersshowed better spatial updating performance than did any Valcarriers (see Figure 3). Statistical analysis revealed a main effect ofCOMT genotype, F(3, 103) � 5.32, p � .05, �2 � .04; a maineffect of age group, F(3, 103) � 27.16, p � .0001, �2 � .13; andan age group by COMT genotype interaction, F(3, 103) � 4.50,

1 From the whole sample only 48 of the younger adults and 46 of theolder adults took part in the session of the MOT task. Thus, for the MOTtask, data from six younger (11%) and seven older (13%) participants werelacking. Performance values for those participants were estimated from thespatial WM updating task only.

Table 2COMT and CHRNA4 Genotype Distributions in Younger andOlder Adults

COMT

CHRNA4

Younger adults Older adults

C/C Any T C/C Any T

Met/Met 3 12 6 13Any Val 8 30 8 27

Note. COMT � catecholamine-O-methyltransferase; CHRNA4 � cho-linergic nicotinic receptor gene; Met/Met � Met homozygotes.

884 STORMER, PASSOW, BIESENACK, AND LI

p � .05, �2 � .04. Follow-up analysis for each age group sepa-rately showed that the effect of genotype was present in only thegroup of older adults, F(1, 52) � 11.69, p � .05, �2 � .18. Nomain effect of the cholinergic CHRNA4 genotype or a CHRNA4genotype by age group interaction was observed (all ps � .05).The relatively small sample size of these data resulted in verysmall cell sizes for the gene–gene combinations (see Table 2) andprecluded further analyses on COMT � CHRNA4 interactions.

Discussion

This sample finding illustrates how variations in COMT geno-type can affect performance in spatial updating tasks. In particular,the older COMT Met homozygotes performed better comparedwith older Val carriers, whereas this relation was absent in youngeradults. The direction of the gene effect in older adults is consistentwith the fact that Met homozygotes (Met/Met) have higher avail-ability of DA at prefrontal receptors, replicating results fromprevious studies. For instance, Nagel and colleagues (2008) re-ported faster reaction times in a spatial WM task for Met carriersrelative to Val/Val carriers in older adults. The gene by ageinteraction parallels the inverted -shaped function of DA signal-ing (Arnsten, 1998; Li & Sikström, 2002) and implies that healthyaging may amplify dopaminergic gene effects. The lack of thecholinergic gene effect might indicate that rapid attentional pro-cesses such as transient orienting played less of a role for perform-ing well in the present tasks. Rather, sustained processes of main-tenance and updating visual-spatial information that mostlyinvolve frontal and parietal brain regions (Culham et al., 2001;Leung et al., 2007) that were affected by individual differences inCOMT genotype in our data were more important in these tasks. Itshould be noted that the size of the present sample is small, andthus the statistical power is rather limited. Even though the resultsare consistent with the age magnification hypothesis and illustrateCOMT genotype effects on visual-spatial WM updating, theyshould be interpreted with caution. Future studies with larger

samples that also take gene–gene interactions into considerationare necessary to further verify these findings.

Conclusion

In sum, recent advances in the investigation of variation ingenotypes that are associated with cholinergic and dopaminergicneuromodulation have provided important insights for understand-ing individual and age-related differences in visual-spatial atten-tion and WM processes. There is evidence for a pivotal role ofcholinergic neuromodulation in visual-spatial attention processes,whereas dopaminergic neuromodulation seems to be crucial forWM processes. Recent findings have suggested that gene variantsassociated with attention may also be of relevance to WM pro-cesses, or the other way around. The overlap and interactionsbetween genotypes coding for the neurotransmitter systems likelydepend on the specific subprocesses the tasks tap into. Precisely,most studies using tasks that require orienting processes, such asspatial cuing tasks, reported effects of the cholinergic systems.Tasks involving the maintenance or updating of information, onthe contrary, have mostly been found to depend on dopaminergicneurotransmission and in some cases on the interaction of thecholinergic and the dopaminergic system. The partial overlap isconsistent with the overlap in brain networks activated by visual-spatial attention and visual-spatial WM, as well as their crossoveron the cognitive level. To reveal a more complete picture of theunderlying neurotransmitter systems it seems to be of importanceto not only examine single gene effects on one performancemeasure, but rather to include gene– gene interactions andmultiple attention and WM tasks that tap into different and morespecific processes. There is evidence for changes in the efficiencyof both neurotransmitter systems across the lifespan that in partunderlie age differences in attention and WM performance. Theseage-related changes in neuromodulation imply that individual dif-ferences in genotypes relevant for cholinergic and dopaminergicneurotransmission may affect WM and attentional functions todifferent extents at different time points across the lifespan, assuggested by the resource limit hypothesis (Lindenberger et al.,2008). The molecular genetics approach may shed further light onthe mechanisms underlying cognitive development in childhoodand adolescence as well as cognitive decline in old age. Cross-sectional and in particular longitudinal studies could help to gaininsights on the influence of neurotransmitter systems and relatedgenes on cognition across the life span.

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Figure 3. T scores of spatial updating performance for younger andolder adults as a function of catecholamine-O-methyltransferase(COMT) genotype. � p � .05.

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Received April 1, 2011Revision received August 25, 2011

Accepted September 26, 2011 �

889NEUROMODULATION, ATTENTION, AND WORKING MEMORY