Dahlin Et All (2009) REVIEW Article About His 2 Studies and Jaeggi

Restorative Neurology and Neuroscience 27 (2009) 405–419 405DOI 10.3233/RNN-2009-0492IOS Press

Training of the executive component ofworking memory: Subcortical areas mediatetransfer effects

Erika Dahlina,b,e,∗, Lars Backmanc, Anna Stigsdotter Neelyd and Lars Nyberga,b,e

aDepartment of Integrative Medical Biology, Umeå University, Umeå, SwedenbDepartment of Radiation Sciences, Umeå University, Umeå, SwedencAging Research Center, Karolinska Institutet, Stockholm, SwedendDepartment of Psychology, Umeå University, Umeå, SwedeneUmeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden

Abstract. Purpose: Several recent studies show that training can improve working memory (WM) performance. In this review,many issues related to WM training, such as neural basis, transfer effects, and age-related changes are addressed.Method: We focus on our own studies investigating training on tasks taxing the executive updating function and discuss ourfindings in relation to results from other studies investigating training of the executive component of WM.Results: The review confirms positive behavioral effects of training on working memory. The most common neural patternfollowing training is fronto-parietal activity decreases. Increases in sub-cortical areas are also frequently reported after training,and we suggest that such increases indicate changes in the underlying skill following training. Transfer effects are in generaldifficult to demonstrate. Some studies show that older adults increase their performance after WM training. However, transfereffects are small or nonexistent in old age.Conclusions: The main finding in this review is that sub-cortical areas seem to have a critical role in mediating transfer effectsto untrained tasks after at least some forms of working memory training (such as updating).

Keywords: Fronto-parietal changes, subcortical changes, executive training, fMRI, transfer effects, aging

1. Background

Previous studies have found training-related changesafter teaching strategies (Nyberg et al., 2003), coachingprograms (Van Puyenbroeck and Maes, 2006), and edu-cation in compensatory strategies (Boman et al., 2004).People with limited cognitive capacity are often affect-ed in their everyday life due to problems with executivefunctions (Mateer, 1999) and therefore, it is importantto increase our knowledge about the modifiability ofexecutive functions and related working memory (WM)

∗Corresponding author: Erika Dahlin, IMB, Physiology Section,Umeå University, SE-901 87 Umeå, Sweden. Tel.: +46 90 786 5186 connection 13; Fax: +46 90 786 66 83; E-mail: [email protected].

functions through guided interventions. We discusswhat is known about specific functions and neural cor-relates underlying increased performance after trainingof executive functions and WM. The discussion willlead to some conclusions about how underlying neuralmechanisms mediate effects to untrained tasks.

WM refers to the ability to hold and monitor infor-mation during a short delay and involves simultaneousstorage and manipulation (Baddeley, 1996). Accordingto Baddeley’s classical model, WM consists of threemain components. One is specialized for maintenanceof speech-based information (the phonological loop),and one is specialized for maintenance of visual andspatial information (the visuospatial sketchpad). In ad-dition to these two “slave” systems, the model also in-cludes a control structure termed the central executive,

0922-6028/09/$17.00 2009 – IOS Press and the authors. All rights reserved

406 E. Dahlin et al. / Training of the executive component of working memory

which is considered responsible for the regulation ofcognitive processes (i.e., executive functions). Miyakeet al., (2000) provided evidence for a division of ex-ecutive functioning into three separate, but moderatelycorrelated, functions: updating, shifting and inhibition.Miyake and colleagues defined updating as a functionthat update and monitor working memory representa-tions, shifting as a function responsible for attention ortask switching, and inhibition as the ability to deliber-ately inhibit dominant, automatic, or prepotent respons-es. The central executive component of WM is almostcertainly the most important component in terms of itsgeneral impact on cognition (Baddeley, 1996; Eysenck,2000; Repovs and Baddeley, 2006), but neverthelessremains the least studied component.

In the spirit of the lab-review format, we focus on ourown studies investigating training on tasks taxing theexecutive updating function (see Fig. 1 for descriptionof training tasks) and discuss our findings in relation toresults from other studies investigating training of theexecutive component of WM. Four of the training tasksincluded in our studies had a similar structure. In thesetasks, single items (letters, numbers, spatial locations,or colors) were presented and the participants were in-structed to monitor and update the four last presenteditems during list presentation. In the remaining train-ing task (which was performed twice at each session),words from various semantic categories were present-ed. The participants were instructed to mentally placethe words into categories (animals, clothes, countries,relatives, sports, professions) and continuously updatethe content and remember the last presented word ineach category (see Dahlin et al., 2008 for a detaileddescription of the training program). Here we describetraining-related performance changes, neural changes,and effects on untrained tasks (transfer). With the ex-ception of the last section, where data from elderly par-ticipants are discussed, we only include studies involv-ing young healthy individuals.

2. Training effects

In this section, we discuss previous WM trainingstudies and compare different interventions in terms oftraining-related changes. A training-related change canbe indicated either by a change in accuracy, change inreaction time, or a change in error rate and is definedinconsistently in previous studies. Participants’ healthstatus, gender, and life style as well as format differ-ences between training programs may be important fac-

Fig. 1. Training tasks included in each training session in the updat-ing training described by Dahlin, Nyberg et al., (2008). In four prac-ticed tasks participants were instructed to recall the last four itemsin correct order (letters, numbers, spatial locations, or colors). Theremaining training task was performed twice each session. Partici-pants were instructed to mentally place words into categories (indi-cated by boxes at the bottom of the screen), continuously update thecontent, and remember the last presented word in each category. Inthis example; first box/category ANIMALS, second SPORTS, andthird CLOTHES.

tors for training-related changes. Nonetheless, factorslike these have not attracted much interest in previ-ous WM training studies. We suspect that especially

E. Dahlin et al. / Training of the executive component of working memory 407

the amount of training is a critical factor for training-related changes, and will highlight this factor. Sever-al studies have revealed that it is possible to increaseperformance (Brehmer et al., 2008; Jaeggi et al., 2008;Olesen et al., 2004; Westerberg and Klingberg, 2007),reduce reaction times (Karbach and Kray, in press), anddecrease error rate (Hempel et al., 2004) on WM tasksafter longer (> 1 week) training periods. It has alsobeen found that it is possible to reduce reaction times(Bherer et al., 2005; Garavan et al., 2000; Jansma et al.,2001) and error rate (Landau et al., 2004) after shorter(< 1 week) periods of WM training. In all studies list-ed above, performance continued to change throughoutthe training period, which raises questions about theoptimal length of the training period. At present, lit-tle is known about optimal lengths of training periods,number of training sessions, and number of trainingtasks. The fact that different dependent measures areused in various studies does not facilitate comparisons.

That said, we estimated the relation between lengthof training period and the size of training-relatedchanges (see Table 1). To do so, we divided 22 studiesinto three groups; 1 (Effect Size, ES � 1.5), 2 (ES >1.5 and ES < 3.0), and 3 (ES � 3.0), based on cal-culated effect sizes on the dependent measures ((posttraining – baseline)/standard deviation at baseline). Allwere current studies that investigated training of theexecutive component of WM to some extent. In studieswhere several dependent measures for a given task wereused, we calculated effect sizes on the measure whichrevealed the most pronounced effect. Further, if sever-al criterion tasks were included, the most executivelydemanding task was analyzed.

A comparison of the length of training periods instudies listed in groups 1 and 3 revealed that higher lev-els of training-related changes were reported in studieswhere participants received longer periods of trainingcompared to studies where participants received short-er periods of training. Hence, from the estimation ofES in Table 1 it seems as if the length of the trainingperiod is a critical factor for the magnitude of the train-ing gains. However, it is a simplification to only dis-cuss length of training period, as type of training tasksand number of training sessions are presumably alsoimportant factors. Not surprisingly, the general patternin Table 1 is that more sessions of training resulted inlarger effect sizes. The fact that almost all of the stud-ies in Table 1 that used long training periods also usedmany training sessions makes it difficult to draw strongconclusions about the relative importance of these twofactors. However, it is interesting to note that both

Kramer (1999) and Karbach and Kray (in press) used4 sessions of training on task switching (the trainedtasks were not identical, albeit similar), but the ES inKramer (1999) were < 1.5 whereas the ES in Karbachand Kray (in press) were � 3. The main differencebetween the studies was the length of the training pe-riod. In Kramer (1999) participants trained for oneweek, whereas in Karbach and Krays (in press) theytrained for four weeks. This is one example where thesame number of training sessions can produce differenttraining-related effects depending on the total lengthof the training period. Studies that investigate this is-sue in a systematic way are warranted, but a tentativeconclusion we can draw is that longer training periodsand more training sessions in yields larger effects ontrained tasks.

In line with the impression that higher levels of train-ing lead to larger training-related gains, a recent study(Jaeggi et al., 2008), where participants trained on an n-back task for 8, 12, 17, or 19 days, found that groups re-ceiving more training showed larger gains compared togroups receiving less training. Interestingly, the patternwas dose-dependent; performance increased graduallywith the number of days practiced. We found a similarpattern of dose-dependent effects when we calculatedeffect sizes across training weeks (Dahlin et al., 2008).The average performance during training session 1–3(week 1), session 4–6 (week 2), session 7–9 (week 3),session 10–12 (week 4), and session 13–15 (week 5)were subtracted from pre-test performance (baseline)and divided by the standard deviation at pre-test. Wefound that the ES for week 1 = 0.97; ES week 2 =1.81; ES week 3 = 2.25; ES week 4 = 2.51; and ESweek 5 = 2.76 (ES for post-training = 3.00). If thesepatterns reflect number of training sessions or a longertraining period remains unknown.

As previously mentioned, even brief periods of train-ing can lead to behavioral changes in the criterion task.In fact, the most common pattern when testing healthyparticipants on a task more than once is a change, i.e.a test-retest effect, on the repeated task (Levine et al.,2004). To control for such effects, it is imperative toinclude control groups in all training studies. There arehowever a lot of training studies not including controlgroups (Table 1) even if the importance of such groupshave been frequently discussed over the years. We in-cluded both trained and untrained participants in a re-cent study where we examined training-related changesafter five weeks (a total of 15 sessions) of updatingtraining (Dahlin et al., 2008; Dahlin, Stigsdotter Neelyet al., 2008). Even if the change in performance was


more pronounced for the trained participants, we founda clear change from pre- to post-test for both groups.By including a control group in this study, we avoidedto misinterpret at test-retest effect as a training-relatedeffect.

A further aspect of WM training is that the proficien-cy of the underlying cognitive skill differs, between in-dividuals and finding an optimal level of task difficultyfor each individual can be critical for successful train-ing. One study divided the participants based on im-provement scores and revealed increased performanceafter five days of training on an auditory WM task for astrong learning group but not for a weak learning group(Gaab et al., 2006). One way to regulate task demandsis to use adaptive training adjusted to individuals’ per-formance. Only six of the studies listed in Table 1 usedtraining tasks with adjusted difficulty levels depend-ing on the participants’ performance (Brehmer et al.,2008; Dahlin et al., 2008; Dahlin, Stigsdotter Neelyet al., 2008; Jaeggi et al., 2008; Olesen et al., 2004;Westerberg and Klingberg, 2007). In our estimationof training-related gains, all studies that used adaptivetraining procedures revealed medium to high levels ofimprovement (Table 1). Thus, our conclusions is thatit is important to ensure that the training has an optimaldegree of difficulty for each individual during the entiretraining period.

After finding training-related effects, it is of rele-vance to ensure the efficacy of training. One meansof doing so is to investigate long-term maintenance ofgains. Long-term follow-up assessments are often in-cluded in other types of cognitive interventions, suchas training of encoding strategies in episodic memory(Bottiroli et al., 2008; Stigsdotter Neely and Backman,1993) and training of fluid intelligence in relation toeveryday functional outcomes (Willis et al., 2006), butare rarely addressed in WM training studies (Table 1).

To investigate long-term effects after updating train-ing, we invited trained and control participants for afollow-up test session 18 months post training (Dahlinet al., 2008). Performance on the trained task wasdecreased at follow-up compared to the post-trainingsession, but still significantly higher than at baselinefor trained participants. By contrast, performance atfollow-up did not differ from that at baseline for con-trols. The maintenance effect after 18 months suggestsa rather robust influence of training. Another studylisted in Table 1 (Kramer et al., 1999) found long-termeffects 2 months post training. One goal in this reviewwas to delineate factors important for long-term main-tenance of gains. The fact that only two WM training

studies (Dahlin et al., 2008; Kramer, 1999), differingin several respects (number of training sessions, num-ber of months between post-test and follow-up test, thedependent measure used), have investigated long-termmaintenance makes it difficult to draw firm conclusions.In the next section, we will further elaborate on changesin underlying functions and networks when addressingneural correlates of training-related cognitive changes.

3. Training-related neural changes

Anatomically, Baddeley (1996) defined the centralexecutive as a system located in the frontal lobes. Imag-ing studies have also revealed that the executive com-ponent of WM draws on frontal as well as parietal cor-tex integrity (e.g., Cabeza and Nyberg, 2000a; Col-lette and Van der Linden, 2002; Prabhakaran et al.,2000). In addition, recent work indicates that WMfunctioning is also dependent on different subcorticalareas (e.g., Cools et al., 2008; Marklund et al., 2007;McNab and Klingberg, 2008). These patterns of ac-tivation in tasks tapping the executive component ofWM was also found in a recent study from our lab(Nyberg et al., 2009), where frontal, parietal, and sub-cortical brain areas exhibited a load-dependent activa-tion pattern during WM performance. In this study,we investigated brain activation patterns in young low-performers, young high-performers, and elderly per-sons during n-back performance. A task with three lev-els of loads (1/2/3) was used, and capacity-constraintsin working memory between load 1–2 for the elder-ly and between 2–3 for young low-performers relativeto young high-performers were found. Constraints inneural activity also followed this pattern by showinga monotonically increasing response in parietal cortexand thalamus for young high-performers, whereas ac-tivity leveled off at 1-back for the elderly and at 2-backfor young low-performers in these brain regions. Theresponse in frontal cortex followed a similar pattern ofcapacity-constraints, although the old showed greateractivation at the lowest level compared to the youngergroups (Fig. 2; Nyberg et al., 2009). Hence, training-related activation changes may not be located in iso-lated parts of the brain but rather seem to be widelydistributed. In the following sections, we discuss keyregions in which altered patterns of brain activity posttraining have been documented.


Fig. 2. Load-sensitive activation patterns in fronto-paroetal andsub-cortical areas. (a) All groups showed a decrease in numberof correct responses as a function of load. The pattern suggeststhat capacity was heavily taxed at 1-back for the elderly adults andbetween 2-back and 3-back for young low-performers. Error barsrepresent standard errors around the means. (b) The old adultsshowed greater activation at the lowest load level compared to bothyounger groups, and activity decreases with increasing load in a leftfrontal area. A capacity-constrained response was seen for younglow-performers between 2- and 3-back in the same region. (c) A ca-pacity-constrained response was seen for old between 1- and 2-backand for young low-performers between 2- and 3-back in a right infe-rior parietal region. (d) A capacity-constrained response was foundbetween 1- to 2-back for the old and between 2- and 3-back for younglow-performers in thalamus.

3.1. Fronto-parietal training-related changes

Both fronto-parietal increases and decreases afterWM training have been reported (Table 1), and it is not

clear how to interpret such divergent training-relatedbrain changes with respect to neural efficiency. Increas-es in activation may reflect recruitment of additionalcortical units with practice. Alternatively, increasesmay stem from a strengthening of the BOLD responsewithin a particular region (Poldrack, 2000). Increas-es in fronto-parietal areas have been found after bothlonger (> 1 week) visuo-spatial WM training (Olesenet al., 2004; Westerberg and Klingberg, 2007) as wellas after shorter training on an n-back task (Hempel etal., 2004).

However, the most common fronto-parietal patternafter both repeated exposure to WM tasks (Chein andSchneider, 2005) and more intensive training (Kelly etal., 2006; Kelly and Garavan, 2005) is decreased acti-vation. Such a pattern of functional activation changesare reported after longer (> 1 week) periods of train-ing on a complex auditory task (Gaab et al., 2006),a visuospatial WM task (Brehmer et al., 2008), aswell as after shorter (< 1 day) periods of training ona visuospatial WM task (Garavan et al., 2000) andan n-back task (Hempel et al., 2004). We found de-creased fronto-parietal activation for the trained groupin our recent study (Dahlin et al., 2008), where training-related changes after five weeks of updating trainingwere investigated. The decreased training-related ac-tivations were located in right anterior prefrontal cor-tex, right somatosensory association cortex and rightsupramarginal gyrus. All these areas are important forhigher-order cognitive processing and attentional mod-ulation (Cabeza and Nyberg, 2000a). Especially theanterior prefrontal cortex is thought to be critical tostrategic processes involved in executive functions andmemory retrieval. The decreased fronto-parietal acti-vation following training may reflect that the task wasinitially difficult and required intense executive control.That is, participants may initially have generated taskstrategies that involved brain regions associated withattentional functions. Conceivably, with practice, thetask became less effortful and performance requiredless control. The reduction in executive control follow-ing training led to reductions of activation in areas im-portant for attention, particularly regions in the fronto-parietal cortex (Chein and Schneider, 2005; Poldrack,2000). In line with our results, a recent study (Brehmeret al., 2009) reported marked fronto-parietal decreasesafter five weeks of spatial and verbal WM training. Bycontrast, as noted above, Olesen (2004) reported in-creased fronto-parietal activation after an almost iden-tical intervention procedure. A possible reason for dis-crepant findings could be whether the training program


is adaptive or not. Adaptive training makes it neces-sary to implement new strategies during the trainingperiod, which could increase the executive demands,and hence result in greater fronto-parietal activation.However, both Olesen et al., (2004) and Brehmer et al.,(2008) used an adaptive training regimen, and the rea-son for inconsistent results must therefore be searchedfor elsewhere.

Another possible reason for inconsistency in brainactivation changes between studies is variations in theamount of training. In Hempel et al., (2004), a limit-ed amount of training on an n-back task led to fronto-parietal increases, whereas more extensive training ledto fronto-parietal decreases. Even though the amountof training in terms of number of days did not differ inthe studies discussed above, a more extensive programwas used in Brehmer et al. (2009; 7 training tasks)compared to Olesen et al., (2004; 3 training tasks).Consistent with this assertion, in Dahlin, StigsdotterNeely et al., (2008) participants practiced on 6 tasksduring each training session. It is reasonable to assumethat the probability of observing training-related de-creases is greater following more extensive training, al-though training-related fronto-parietal decreases havealso been reported after less extensive training (see Ta-ble 1). To further understand training-related brain ac-tivation changes, it is of interest to relate the fronto-parietal changes to subcortical changes in activationpatterns.

3.2. Sub-cortical training-related changes

Subcortical areas such as the basal ganglia are impor-tant in human cognition (for review, see Seger, 2006).Indeed, there are studies reporting subcortical activa-tion changes following WM training (Table 1). Themost common pattern in these studies is subcortical in-creases following training, and only two studies (Lan-dau et al., 2004; Tomasi et al., 2004) demonstrated de-creased subcortical activation following training. Lan-dau et al., (2004) showed activation decreases in pari-etal, occipital, and precentral gyrus as well as in anteri-or thalamus and left insula during encoding after prac-ticing a face WM task. Tomasi et al., (2004) showedactivation decreases in several regions of prefrontal cor-tex, cerebellum as well as in the pulvinar. Importantly,the amount of practice in both Landau et al., (2004)and Tomasi et al., (2004) was limited to the scanningsession. In studies where participants receive moretraining, increased subcortical activation changes aremore common. For example, training-related increas-

es in right striatum and left thalamus were found af-ter five weeks of WM training (Olesen et al., 2004),and increased bilateral activity in striatum followingfive weeks of updating training was found in our study(Dahlin et al., 2008). Hence, more training seems to re-sult in increased subcortical activity. One possibility isthat more training produces more substantial changes inthe underlying skill, which could be reflected in subcor-tical activity increases. Hence, the increased training-related activity we observed in striatum following up-dating training likely reflects strengthening of the up-dating skill (which was upheld 18 months after com-pletion of training). However, to more fully understandthe neural reorganization that takes place after train-ing, it is critical to draw attention to how cortical andsubcortical areas interact.

3.3. Neural networks involved in WM training

As noted, we found decreased activation in fronto-parietal areas after updating training, whereas activa-tion in subcortical areas increased post training. Theseresults support the views that training does not resultin a monotonic increase or decrease in neural activity(Kelly and Garavan, 2005; Landau et al., 2004), andthat training-related activation changes are not restrict-ed to an isolated part of the brain. To better understandthe neural reorganization that takes place after training,it is critical to identify neural networks underlying theseactivity changes. In a model describing a network crit-ical to cognitive functioning, Alexander et al., (1986)delineated the parallel organization of functionally seg-regated circuits linking basal ganglia and cortex. Anetwork that is strongly implicated in cognitive trainingis the dorsolateral prefrontal circuit, which consists ofat least two distinct basal ganglia-thalamocortical cir-cuits that selectively influence separate prefrontal ar-eas. According to this model, key regions include thedorsal prefrontal cortex (BA 10) and posterior parietalcortex (BA7). These areas are highly overlapping withthe fronto-parietal areas where we found decreased ac-tivation following training (Fig. 3; Dahlin, StigsdotterNeely et al., 2008). This circuit continues to the globuspallidus and the substantia nigra. In turn, these regionsproject to the thalamic nuclei, which project back toprefrontal cortex.

The connection between cortical areas and the basalganglia have also been described in several compu-tational models (Frank et al., 2001; O’Reilly, 2006).Frank et al., (2001) postulated that the frontal cortexis responsible for active maintenance of information,


Fig. 3. The network underlying cognitive performance described byAlexander et al., (1986) in relation to training-related changes ob-served by Dahlin Stigsdotter Neely et al., (2008). The brain map atthe top shows dorsal prefrontal cortex (BA 10) and posterior pari-etal cortex (BA7) exhibiting decreased activation following updat-ing training. According to Alexander et al., (1986) projections arisefrom these areas (blue dotted line from BA 7, green dotted line fromBA 10) to striatum. The brain map at the bottom (z = −4) showsassociative striatum, exhibiting increased activation following updat-ing training. The circuit then continues to the globus pallidus andsubstantia nigra, which projects to thalamus, which in turn projectsback to prefrontal cortex. The brain maps are thresholded at p <0.005 (cluster size > 10) n = 22.

whereas the basal ganglia serve a selective, dynamicgating function that enables frontal memory represen-tations to be rapidly updated. Analogously, in a re-cent review of biologically-based computational mod-els of high-level cognition, O’Reilly (2006) presentedevidence that rapid updating requires a mechanism forgating or switching between on and off states. The ex-tensive connectivity between PFC and the basal gangliais thought to be critical for this gating mechanism. Inrelation to the training-related fronto-parietal decreas-es and corresponding striatal increases that we foundfollowing updating training, these models suggest thatthe fronto-striatal network was involved. Even at the

end of our experiment, the frontal cortex maintainedsome degree of involvement, although much decreasedcompared to pre training. With practice, subcorticalbrain regions took an increasingly more prominent role.The basal ganglia gating mechanism may have be-come more efficient after training, and thus, the fronto-parietal cortex did not have to be involved to the sameextent. This notion is in line with a theory about rulelearning that argues that prefrontal cortex is responsiblefor initial rule or association induction (Packard andKnowlton, 2002). Subsequently, the striatum mediatesrule application during task performance. This theo-ry predicts that frontal activity should be high early intraining and decrease when rules are learned, where-as striatal activity should increase concomitantly withincreases in learning.

In addition, the pattern of fronto-parietal decreasesand subcortical increases after training have also beenfound after motor learning, which is more extensivelystudied compared to cognitive training. Less fronto-parietal activation and more subcortical activation is in-terpreted to reflect lower demands on cognitive control,that is, a more effective system in motor learning (Will-ingham, 1998; see also Poldrack et al., 2005). We in-terpret the decreased cortical activation as an indicationof more automatized task performance following train-ing, and the increased striatal activation as a change inthe underlying skill. A consequence of change in theunderlying skill may be that untrained tasks drawing onthe same skill also can be affected. Along these lines,we have argued that subcortical areas may have an im-portant role in mediating effects on untrained tasks fol-lowing training of updating WM (Dahlin, StigsdotterNeely et al., 2008). This topic will be elaborated on inthe next section.

4. Transfer effects

One goal after cognitive training is to demonstrateeffects on trained as well as untrained tasks. However,the typical pattern in studies investigating effects onuntrained tasks, i.e., transfer effects, is one of limit-ed generalizability, despite large improvement on thecriterion task (Table 1).

A frequently discussed factor in the context of trans-fer effects is the relation between trained and untrainedtasks. As early as in 1901 Thorndike and Woodworth(Thorndike and Woodworth, 1901) found that trainingin one task caused improvement in performing anotheruntrained task. The function trained was that of esti-

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mating areas of a specific shape from 10 to 100 cm2

and a clear improvement was seen in the trained task.Effects on untrained tasks were also found but the effectwas more pronounced when areas were similar in shapeto those in the training series compared to when newshapes were used. Hence, the gains could be attribut-ed to common elements in the trained and untrainedtasks, and the improvement differed depending on levelof similarity between the trained and untrained tasks.Following this seminal paper, the terms near- and far-transfer were introduced in the literature. Near-transfertasks are untrained tasks that are similar to the trainedtask, whereas far-transfer tasks are untrained tasks thathave less in common with the trained task.

To investigate whether shared processes in thetrained and untrained tasks were central for findingtransfer effects we built on the model of executive func-tions devised by Miyake et al., (2000). Our predictionwas that five weeks of training on a specific execu-tive function, updating, may result in transfer effectsto other untrained executive tasks taxing updating. Wetested one untrained task (n-back), which also requiredupdating, and one untrained task (Stroop) assumed todepend on the untrained executive function inhibition.We found transfer to n-back but not to Stroop (Dahlin,Stigsdotter Neely et al., 2008). Hence, our results sug-gest that the ability trained was improved, not only thetrained task. In another recent paper (Dahlin, Nyberget al., 2008), we tested trained and untrained partici-pants on a wider battery of untrained tasks before andafter five weeks of updating training. Here, we didnot find effects to other transfer tasks despite an ex-tensive battery of non-trained tasks assessing perceptu-al speed, reasoning, episodic memory, semantic mem-ory, and working memory. Based on these findings,our conclusion is that transfer effects are difficult todemonstrate and, when demonstrated, they reflect im-provement in a specific ability commonly underlyingtask performance.

In a study by Jaeggi et al., (2008) where participantstrained on n-back for 8, 12, 17, or 19 days, the degreeof gain in a fluid intelligence transfer task critically de-pended on the amount of training. Although all groupsshowed transfer of learning, the magnitude of improve-ment increased with more training in a dose-dependentfashion. Most likely, more training leads to more robustchanges in the underlying skill, which in turn shouldincrease the likelihood of obtaining transfer effects.

We have argued that a key factor for transfer effectsis shared underlying processes required for successfulperformance in trained and untrained tasks. In line with

this notion, the transfer effects from n-back training tofluid intelligence discussed above (Jaeggi et al., 2008)may reflect the fact that WM and fluid intelligence sharea common capacity constraint and attentional controlprocesses (Halford et al., 2007; Gray et al., 2003).Further evidence for our view on transfer is that the WMtraining studies documenting transfer effects (Table 1)mainly report effects to tasks similar to the trained task.

Knowledge about neural correlates of transfer mayshed further light on underlying mechanisms. How-ever, little is known about neural correlates of transfereffects. Following Thorndike and Woodworth (1901),it has been hypothesized that overlapping networksor brain areas for the trained and the untrained tasksare necessary for producing transfer effects (Jonides,2004). To our knowledge, only our recent study hasinvestigated neural correlates of transfer (Dahlin, Stigs-dotter Neely et al., 2008). We found overlappingfronto-parietal activation before training for the trainedupdating task (letter memory), the untrained updat-ing task (n-back), and the untrained inhibition task(Stroop). Further, we found overlapping activation instriatum before training in letter memory and n-back,but not in Stroop. This pattern is interesting to view inrelation to the behavioral results of transfer effects ton-back, and indicates that pre-training activity in stria-tum is important for finding transfer effects followingupdating training. When investigating training-relatedchanges we found overlapping training-related increas-es for both letter memory and n-back in the associa-tive striatum that was also activated in both tasks pretraining. Hence, the striatal complex was a key com-ponent mediating transfer effects after training on WMupdating. Previous studies have proposed that striatumis a gating structure selecting specific regions in frontalcortex to be updated or to maintain already existing in-formation (O’Reilly, 2006). We interpret the increasedstriatal activations as an indication that the updatingskill was more effective following training.

Another key aspect when discussing training andtransfer effects is differences between individuals, orgroups of individuals. Individual differences in cog-nitive ability have been found to influence the degreeof transfer after training on a visual comparison task(Sohn et al., 2006). If differences between individu-als exist within a group, there are most certainly al-so differences between groups with different cognitiveability such as younger versus older adults.


Fig. 4. Letter memory performance for training and control groups.Error bars are standard errors around the means. The dotted lineindicates baseline performance for young participants.

5. Working memory training in elderly adults

Cognitive interventions have revealed that it is pos-sible to increase performance in children with ADHD(Klingberg et al., 2002), patients with traumatic braininjury (Kim et al., 2008), and schizophrenic patients(Lopez-Luengo and Vazquez, 2003). However, themost studied group in cognitive interventions, apartfrom young adults, is healthy older adults. Episodicmemory, working memory, and executive functioningdecline in normal aging (e.g.,Craik and Jennings, 1992;Kliegl et al., 2001, Backman, 1999; Light, 1991), andseveral studies have tried to improve performance ontests of these functions in older adults by means of var-ious training procedures. Cognitive interventions in el-derly persons have often focused on the effects of teach-ing strategies (the method of loci) to enhance episodicmemory performance and found performance gains af-ter training (e.g., Nyberg et al., 2003; Verhaeghen andMarcoen, 1996; Verhaeghen et al., 1992). Training ofWM is not investigated to the same extent in elderlyadults, but healthy older persons can increase perfor-mance after more than one week of training in spatialand verbal WM tasks (Brehmer et al., 2008), switchingtasks (Karbach and Kray, in press; Kray and Epplinger,2006), as well as exercises aimed at improving bottom-up processing of auditory information (Mahncke et al.,2006). We investigated updating training in healthy el-derly adults between 65–70 years of age, using a train-ing program identical to the one described above foryoung adults. Results showed training-related gains inthe letter-memory updating task of about the same mag-nitude as observed for young adults (Dahlin, Nyberg etal., 2008; Dahlin, Stigsdotter Neely et al., 2008).

A key finding was that older persons performedabove the baseline level of young persons after fiveweeks of updating training (Fig. 4). Long-term mainte-nance of gains 18-months after training was also found;in fact, older adults performed above the baseline lev-el of young even one and a half year post training.Hence, the training effect was quite robust and fiveweeks of computer-based training made elderly adultsperform as they would be 40 years younger. At thesame time, the elderly persons performed below theiryounger counterparts both initially and after trainingand, unlike the young, they exhibited no transfer ef-fects. This pattern of (a) cognitive plasticity in old age;and (b) age-related constraints in plasticity character-izes much intervention research comparing individu-als across the adult life span (e.g., Verhaeghen et al.,1992; Baltes and Kliegl, 1992; Jones et al., 2006). Theage-related differences observed by Dahlin, Nyberg etal., (2008) likely reflect alterations in the fronto-striataldopaminergic system (for reviews, see Backman et al.,2006; Prull et al., 2000).

To investigate training-related brain activity changes,all elderly participants were fMRI scanned before andafter the training period. As for young adults, we foundfronto-parietal activation before the intervention duringthe trained letter-memory updating task. However, incontrast to young, we found increased activation fol-lowing training in fronto-parietal areas for older adults.This may reflect that task performance was not automa-tized to the same extent as for the young and that the taskwas executively demanding even after five weeks oftraining for older persons. Further, we found increasedstriatal activation post training in letter-memory for old-er participants, although the striatum was not activat-ed before training. Conceivably, these findings reflectage-related changes in the striatal dopamine system,which serves as a powerful mediator of the cognitivelosses that occur during the aging process (Backmanet al., 2000; Backman et al., 2006). In particular, thestriatal dopamine system plays a role in age-related dif-ferences in executive functioning (Erixon-Lindroth etal., 2005). Hence, age-related changes in the striataldopamine system may have constrained the training, sothat the underlying updating skill was not strengthenedto the same extent as for young persons. These changesmay also account for the lack of transfer effects in olderadults.

6. Directions for future research

The research discussed above clearly indicates thatit is possible to train WM functions. The overall data


pattern suggests that more training brings about larg-er training-related gains. However, it is complicatedto compare the amount of training between studies us-ing different training regimens and different dependentmeasures. A first direction for future research is there-fore to conduct more studies like that of Jaeggi et al.,(2008), varying the amount of training. Hence, studiesare warranted that compare training-related changes af-ter varying the number of sessions, spacing of sessions,duration of training, and number of tasks trained. Ofinterest is also whether the patterns of dose-responseeffects vary across the adult life span. To make it pos-sible to design optimal training regimens in the future,it is also important to investigate effects of differentlengths of training periods and number of training ses-sions in a systematic way. Today it is not known if fiveweeks of 15 training sessions is more or less effectivecompared to one week of 15 training sessions.

A second direction for future research is to conductmore studies investigating transfer effects systemati-cally. As noted, transfer effects are generally limitedand restricted to tasks similar to the criterion task. Thequestion about transfer effects is complex and sever-al issues remain unresolved. One important aspect inthis context is shared processes in the trained and un-trained tasks. As discussed above, we found transferto n-back after five weeks of training on other tasksrequiring updating. We interpret this as an effect on ageneral updating skill. An interesting avenue for futureresearch would be to test the generalizability of this as-sumption by examining whether n-back training resultsin improved letter-memory performance. Of interestis also to investigate the boundaries of transfer acrossthe three major components of executive functioningoutlined by Miyake et al., (2000): updating, switching,and inhibition. In particular, we need knowledge aboutwhether the limited degree of transfer from updatingtraining (e.g., null effects in the inhibitory Stroop task)holds for training in switching and other updating tasksas well.

A third direction for future research is that more stud-ies should investigate neural correlates of WM trainingand transfer. A particularly intriguing line of inquiryis to further investigate the striatal blood flow increas-es observed following training in both letter memo-ry and n-back in our recent study (Dahlin, Stigsdot-ter Neely et al., 2008). It is well known that thestriatum is a region in which the dopaminergic inner-vation is particularly dense (Alexander et al., 1986;Bjorklund and Dunnett, 2007), and pharmacologicalimaging in rodents suggests that striatal blood flow

is largely driven by dopamine agonism (Knutson andGibbs, 2007). Some recent studies have found evidencefor dopamine release in frontal cortex and hippocam-pus during working-memory performance (Aalto et al.,2005) and in striatum during both card-sorting (Monchiet al., 2006) and sequential learning (Badgaiyan et al.,2007). Also, Schott et al., (2008) recently showed asizable relationship between a PET-derived measure ofdopamine release and an fMRI-based measure of bloodflow in ventral striatum during reward-related learn-ing. To determine whether the training-related increas-es in striatal blood flow observed by Dahlin, Stigsdot-ter Neely et al., (2008) are driven by increased releaseof dopamine as a function of training, it would be ofgreat interest to employ a similar multimodal imagingapproach.

Acknowledgments

This work was funded by grants from the SwedishResearch Council and the Joint Committee for NordicResearch Councils in the Humanities and the SocialSciences for a Nordic Center of Excellence (NCoE) inCognitive Control to L.N., by grants from the SwedishResearch Council and Swedish Brain Power to L.B,and by grants from the Swedish Council for WorkingLife and Social Research to A.S-N.

References

Aalto, S., Bruck, A., Laine, M., Någren, K., & Rinne, J. O. (2005).Frontal and temporal dopamine release during working mem-ory ad attention tasks in healthy humans: a PET study usingthe high-affinity dopamine D2 receptor ligand [11C]FLB 457.J Neurosci, 25(10), 2471-2477.

Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallelorganization of functionally segregated circuits linking basalganglia and cortex. Annu Rev Neurosci, 9, 357-381.

Allen, R., & Reber, A. S. (1980). Very long term memory for tacitknowledge. Cognition, 8(2), 175-185.

Baddeley, A. (1996). Exploring the central executive. Q J ExpPsychol-A, 49(1), 5-28.

Badgaiyan, R. D., Fischman, A. J., & Alpert, N. M. (2007). Striataldopamine release in sequential learning. Neuroimage, 38(3),549-556.

Baltes, P. B., & Kliegl, R. (1992). Further testing of limits of cog-nitive plasticity: negative age differences in a mnemonic skillare robust. Dev Psychol, 28(1), 121-125.

Bherer, L., Kramer, A. F., Peterson, M. S., Colcombe, S., Erickson,K., & Becic, E. (2005). Training Effects on Dual-Task Per-formance: Are There Age-Related Differences in Plasticity ofAttentional Control? Psychol Aging, 20(4), 695-709.


Bherer, L., Kramer, A. F., Peterson, M. S., Colcombe, S., Erickson,K., & Becic, E. (2008). Transfer effects in task-set cost anddual-task cost after dual-task training in older and youngeradults: further evidence for cognitive plasticity in attentionalcontrol in late adulthood. Exp aging res, 34(3), 188-219.

Bjorklund, A., & Dunnett, S. B. (2007). Dopamine neuron systemsin the brain: an update. TRENDS Neurosci, 30(5), 194-202.

Boman, I. L., Lindstedt, M., Hemmingsson, H., & Bartfai, A. (2004).Cognitive training in home environment. Brain Inj, 18(10),985-995.

Bottiroli, S., Cavallini, E., & Vecchi, T. (2008). Long-term effectsof memory training in the elderly: A longitudinal study. ArchGerontol Geriat, 47(2), 277-289.

Brehmer, Y., Westerberg, H., Rieckmann, A., Fischer, H., &Backman, L. (2008, April). Neural correlated of workingmemory training in younger and older adults: an fMRI study.Poster presented at the Cognitive Aging Conference, Atlanta,GA.

Backman, L., Small, B. J., Wahlin, Ï., & Larsson, M. (1999). Cog-nitive functions in very old age. In F. I. M. Craik and T. A:Salthouse (Eds.), Handbook of aging and cognition. (2nd ed.,pp. 499-558). Mahwah, NJ: Lawrence Erlbaum.

Backman, L., Ginovart, N., Dixon, R. A., Wahlin, T.-B. R., Wahlin,Ï., Halldin, C., et al., (2000). Age-Related Cognitive DeficitsMediated by Changes in the Striatal Dopamine System. Am JPsychiatry, 157(4), 635-637.

Backman, L., Nyberg, L., Lindenberger, U., Li, S.-C., & Farde, L.(2006). The correlative triad among aging, dopamine, and cog-nition: current status and future prospects. Neurosci BiobehavR, 30(6), 791-807.

Cabeza, R., & Nyberg, L. (2000a). Imaging cognition II: An empiri-cal review of 275 PET and fMRI studies. J Cognitive Neurosci,12(1), 1-47.

Cabeza, R., & Nyberg, L. (2000b). Neural bases of learning andmemory: functional neuroimaging evidence. Curr Opin Neu-rol, 13(4), 415-421.

Chein, J. M., & Schneider, W. (2005). Neuroimaging studies ofpractice-related change: fMRI and meta-analytic evidence of adomain-general control network for learning. Cognitive BrainRes, 25(3), 607-623.

Chudasama, Y., & Robbins, T. W. (2006). Functions of frontostriatalsystems in cognition: Comparative neuropsychopharmacolog-ical studies in rats, monkeys and humans. Biol Psychol, 73(1),19-38.

Collette, F., & Van der Linden, M. (2002). Brain imaging of thecentral executive component of working memory. NeurosciBiobehav R, 26(2), 105-125.

Cools, R., Gibbs, S. E., Miyakawa, A., Jagust, W., & D’Esposito, M.(2008). Working memory capacity predicts dopamine synthe-sis capacity in the human striatum. J Neurosci, 28(5), 1208-1212.

Craik, F. I. M., & Jennings, J. M. (1992). Human Memory. In F. I.M. Craik and T. A. Salthouse (Eds.), Handbook of aging andcognition (pp. 51-110). Hillsdale, NJ: Erlbaum.

Dahlin, E., Nyberg, L., Backman, L., & Stigsdotter Neely, A. (2008).Plasticity of Executive Functioning in young and Old Adults:Immediate Training Gains, Transfer, and Long-Term Mainte-nance. Psychol Aging, 23(4), 720-730.

Dahlin, E., Stigsdotter Neely, A., Larsson, A., Backman, L., &Nyberg, L. (2008). Transfer of learning after updating trainingmediated by the striatum. Science, 320(5882), 1510-1512.

Erickson, K. I., Colcombe, S. J., Wadhwa, R., Bherer, L., Peterson,M. S., Scalf, P. E., et al., (2007). Training-induced functionalactivation changes in dual-task processing: An fMRI study.Cereb Cortex, 17(1), 192-204.

Erixon-Lindroth, N., Farde, L., Robins Wahlin, T.-B., Sovago, J.,Halldin, C., & Backman, L. (2005). The role of the stri-atal dopamine transporter in cognitive aging. Psychiat Res-Neuroim, 138(1), 1-12.

Eysenck, M. W. (2000). Psychology – A Student’s handbook. NewYork: Taylor & Francis Inc.

Frank, M. J., Loughry, B., & O’Reilly, R. C. (2001). Interactionsbetween frontal cortex and basal ganglia in working memory:A computational model. Cogn Affect Behav Neurosci, 1(2),137-160.

Gaab, N., Gaser, C., & Schlaug, G. (2006). Improvement-relatedfunctional plasticity following pitch memory training. Neu-roImage, 31(1), 255-263.

Garavan, H., HKelley, D., Rosen, A., Rao, S. M., & Stein, E. A.(2000). Practice-related functional activation changes in aworking memory task. Microsc Res Tech, 51(1), 54-63.

Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanismsof general fluid intelligence. Nat Neurosci, 6(3), 316-322.

Halford, G. S., Cowan, N., & Andrews, G. (2007).Separating cog-nitive capacity from knowledge: A new hypothesis. TrendsCognit Sci, 11(6), 236-242.

Hempel, A., Giesel, F. L., Garcia Caraballo, N. M., Amann, M.,Meyer, H., Wustenberg, T., et al., (2004). Plasticity of CorticalActivation Related to Working Memory During Training. AmJ Psychiatry, 161(4), 745-747.

Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008).Improving fluid intelligence with training on workning mem-ory. PNAS, 105(19), 6829-6833.

Jansma, J. M., Ramsey, N. F., Slagter, H. A., & Kahn, R. S. (2001).Functional anatomical correlates of controlled and automaticprocessing. J Cogn Neurosci, 13(6), 730-743.

Jones, S., Nyberg, L., Sandblom, J., Stigsdotter Neely, A., Ingvar,M., Petersson, K. M., & Backman, L. (2006). Cognitive andneural plasticity in aging: general and task-specific limitations.Neurosci Biobehav R, 30(6), 864-871.

Jonides, J. (2004). How does practice make perfect? Nat Neurosci.,7(1), 10-11.

Karbach, J., & Kray, J. (in press). How useful is executive controltraining? Age differences in near and far transfer of task-switching training. Developmental Science.

Kelly, C., Foxe, J. J., & Garavan, H. (2006). Pattern of normalhuman brain plasticity after practice and their implications forneurorehabilitation. Arch Phys Med Rehab, 87(2), 20-28.

Kelly, C., & Garavan, H. (2005). Human functional neuroimaging ofbrain changes associated with practice. Cereb cortex, 15(8),1089-1102.

Kim, Y. H., Yoo, W. K., Ko, M. H., Park, C. H., Kim, S. T., &Na, D. L. (2008). Plasticity of the Attentional Network AfterBrain Injury and Cognitive Rehabilitation. Neurorehab NeuralRepair, 23, 468-477.


Kliegl, R., Neil, J. S., & Paul, B. B. (2001). Memory and Aging,Cognitive Psychology of. In International Encyclopedia ofthe Social & Behavioral Sciences (pp. 9556-9560). Oxford:Pergamon.

Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training ofworking memory in children with ADHD. J Clin Exp Neu-ropsyc, 24(6), 781-791.

Knutson, B., & Gibbs, S. E. B. (2007). Linking nucleus accum-bens dopamine and blood oxygenation. Psychopharmacology,191(3) 813-822

Kramer, A. F., Hahn, S., & Gopher, D. (1999). Task coordinationand aging: explorations of executive control processes in thetask switching paradigm. Acta Psychol, 101(2-3), 339-378.

Kray, J., & Epplinger, B. (2006). Effects of associative learning onage differences in task-set swithing. Acta Psychol, 123(3),187-203.

Landau, S. M., Schumacher, E. H., Garavan, H., Druzgal, T. J., &D’Esposito, M. (2004). A functional MRI study of the influ-ence of practice on component processes of working memory.NeuroImage, 22(1), 211-221.

Levine, A. J., Miller, E. N., Becker, J. T., Selnes, O. A., & Cohen,B. A. (2004). Normative data for determining significanceof test-retest differences on eight common neuropsychologicalinstruments. Clin Neuropsychol, 18(3), 373-384.

Light, L. L. (1991). Memory and Aging: Four Hypotheses in Searchof Data. Annu Rev Psychol, 42, 333-376.

Lopez-Luengo, B. & Vazquez, C. (2003). Effects of Attention Pro-cess Training on cognitive functioning of schizophrenic pa-tients. Psychiat Res, 119(1-2), 41-53.

Mahncke, H. W., Connor, B. B., Appelman, J., Ahsanuddin, O. N.,Hardy, J. L., Wood, R. A., et al., (2006). Memory enhancementin healthy older adults using a brain plasticity-based trainingprogram: A randomized, controlled study. PNAS, 103(33),12523-12528.

Marklund, P., Fransson, P., Cabeza, R., Larsson, A., Ingvar, M., &Nyberg, L. (2007). Unity and diversity of tonic and phasic ex-ecutive control components in episodic and working memory.NeuroImage, 36(4), 1361-1373.

Mateer, C. A. (1999). The rehabilitation of executive disorders. InD. T. Stuss, G. Winocur, & I. H. Robertson (Eds.) CognitiveNeurorehabilitation. (pp. 314-332). Cambridge, England:Cambridge University Press.

McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basalganglia control access to working memory. Nat Neurosci,11(1), 103-107.

Milham, M. P., Banich, M. T., Claus, E. D., & Cohen, N. J. (2003).Practice-related effects demonstrate complementary roles ofanterior cingulated and prefrontal cortices in attention control.Neuroimage, 18(2), 483-493.

Minear, M. & Shah, P. (2008). Training and transfer effects in taskswitching. Mem Cognition, 36(8), 1470-1486.

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., How-erter, A., & Wager, T. D. (2000). The unity and diversity ofexecutive functions and their contributions to complex "frontallobe" tasks: A latent variable analysis. Cog Psychol, 41(1),49-100.

Monchi, O., Ko, J. H., & Strafella, A. P. (2006). Striatal dopaminerelease during performance of executive functions: A[11C]raclopide PET study. Neuroimage, 33(3), 907-912.

Nyberg, L., Dahlin, E., Stigsdotter Neely, A., & Backman, L. (2009).Neural correlates of variable working memory load acrossadult age and skill: Dissociative patterns within the fronto-parietal network. Scandinavian Journal of Psychology, 50,41-46.

Nyberg, L., Sandblom, J., Jones, S., Stigsdotter Neely, A., Petersson,K. M., Ingvar, M., et al., (2003). Neural correlates of training-related memory improvement in adulthood and aging. PNAS,100(23), 13728-13733.

O’Reilly, R. (2006). Biologically based computational models ofhigh-level cognition. Science, 314(5796), 91-94.

Olesen, P. J., Westerberg, H., & Klingberg, T. (2004). Increased pre-frontal and parietal activity post-training of working memory.Nat Neurosci, 7(1), 75-79.

Packard, M. G., & Knowlton, B. J. (2002). Learning and memoryfunctions of the basal ganglia. Annu Rev Neurosci, 25, 563-593.

Poldrack, R. A. (2000). Imaging brain plasticity: Conceptual andmethodological issues – a theoretical review. Neuroimage,12(1), 1-13.

Prabhakaran, V., Narayanan, K., Zhao, Z., & Gabrieli, J. D. E. (2000).Integration of diverse information in working memory withinthe frontal lobe. Nat Neurosci, 3(1), 85-90.

Prull, M. W., Gabrieli, J. D., & Bunge, S. A. (2000). Age-relatedchanges in memory: a cognitive neuroscience perspective. InF. I. M. Craik and T. A. Salthouse (Eds.), Handbook of agingand cognition (2nd ed., pp. 91-153). Mahwah, NJ: LawrenceErlbaum.

Repovs, G., & Baddeley, A. (2006). The multi-component modelof working memory: Explorations in experimental cognitivepsychology. Neuroscience, 139(1), 5-21.

Sayla, S., Sala, J., & Courtney, S. M. (2006). Increased neuralefficiency with repeated performance of a workning memorytask is information-type dependent. Cereb cortex, 16(5), 609-617.

Schott, B. H., Minuzzi, L., Krebs, R. M., Elmenhorst, D., Lang, M.,Winz, O. H., et al., (2009). Mesolimbic fMRI activations dur-ing reward anticipation correlate with reward-related ventralstriatal dopamine release. J Neurosci, 28(52), 14311-14319.

Seger, A. C. (2006). The basal ganglia in human learning. Neurosci-entist, 12(4), 285-290.

Sohn, Y. W., Doane, S. M., & Garrison, T. (2006). The impact ofindividual differences and learning context on strategic skillacquisiton and transfer. Learn Individ Differ, 16(1), 13-30.

Stigsdotter Neely, A., & Backman, L. (1993). Long-term main-tenance of gains from memory training in older adults: two31/2.year follow-up studies. J Geront, 48(5), 233-237.

Thorndike, E. L., & Woodworth, R. S. (1901). The influence ofimprovement in one mental function upon the efficiency ofother functions. Psychol Rev, 8(3), 247-261.

Tomasi, D., Ernst, T., Caparelli, E. C., & Chang, L. (2004). Practice-induced changes of brain function during visual attention: aparametric fMRI study at 4 Tesla. Neuroimage, 23(4), 1414-1421.

Van Puyenbroeck, J., & Maes, B. (2006). Program development ofreminiscence group work for ageing people with intellectualdisabilities. J Intellect Dev Disabil, 31(3), 139-147.


Verhaeghen, P., & Marcoen, A. (1996). On the mechanisms ofplasticity in young and older adults after instruction in themethod of loci: Evidence for an amplification model. PsycholAging, 11(1), 164-178.

Verhaeghen, P., Marcoen, A., & Goossens, L. (1992). Improvingmemory performance in the aged through mnemonic training:A meta-analytic study. Psychol Aging, 7(2), 242-251.

Weissman, D. H., Woldorff, M. G., Hazlett, C. J., & Mangun, G. R.(2002). Effects of practice on executive control investigatedwith fMRI. Cogn brain res, 15(1), 47-60.

Westerberg, H., & Klingberg, T. (2007). Changes in cortical activityafter training of working memory – a single-subject analysis.Physiol Behav, 92(1-2), 186-192.

Willingham, D. B. (1998). A neuropsychological theory of motorskill learning. Psychol Rev, 105(3), 558-584.

Willis, S. L., Tennstedt, S. L., Marsiske, M., Ball, K., Elias, J., Koep-ke, K. M., et al., (2006). Long-term effects of cognitive train-ing on everyday functional outcomes in older adults. JAMA,296(23), 2805-2814.

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