Working Memory and Cognitive Aging
Summary and Keywords
Working memory as a temporary buffer for cognitive processing is an essential part of the cognitive system. Its capacity and select aspects of its functioning are age sensitive, more so for spatial than verbal material. Assumed causes for this decline include a decline in cognitive resources (such as speed of processing), and/or a breakdown in basic control processes (resistance to interference, task coordination, memory updating, binding, and/or top-down control as inferred from neuroimaging data). Meta-analyses suggest that a decline in cognitive resources explains much more of the age-related variance in true working memory tasks than a breakdown in basic control processes, although the latter is highly implicated in tasks of passive storage. The age-related decline in working memory capacity has downstream effects on more complex aspects of cognition (episodic memory, spatial cognition, and reasoning ability). Working memory remains plastic in old age, and training in working memory and cognitive control processes yields near transfer effects, but little evidence for strong far transfer.
Working memory is a crucial component of the cognitive system. It consists of a temporary buffer, lasting for a few seconds at most, used to both passively store and actively manipulate information (for an overview of views and theories, see Miyake & Shah, 1999). The consensus model of working memory distinguishes an attentional core responsible for processing of information (Baddeley & Hitch, 1974 call this the “central executive”; Cowan  labels it “the focus of attention”; the latter term is used here throughout), surrounded by a set of domain-specific stores (i.e., a visual store, a verbal store, and so on) that passively maintain information in the service of the task at hand.
A central aspect of this system and its functioning is that it is severely limited in both time and capacity: Information held passively, without rehearsal, typically has a half-life of about 8 s in younger adults (Verhaeghen, 2014); depending on the task, working memory’s capacity is between 4 and 7 items. The upper limit is for passive storage tasks, that is, tasks that require no concurrent processing, such as digit span or letter span (where participants simply repeat a sequence of digits or letters). This passive storage aspect of working memory is often referred to as short-term memory or primary memory. The lower limit is for tasks that interleave bouts of processing and storage, such as the reading span task or the operation span task; in the latter task (Engle, 2002), participants solve a series of simple equations, at the end of each of which a word is presented, and at the end of the trial, they recall all words in order. These tasks yield lower capacity estimates than passive storage tasks because the ongoing activity occupies the focus of attention with processing, thereby effectively wiping out its storage capacity, leaving only the capacity of the passive buffers.
When the number of items to be retained in working memory is smaller than or equal to the capacity of the focus of attention, the items will be contained within the focus. There they will be immediately retrievable, and access times will be fast. When the number of items to be retained exceeds the capacity of the focus, the excess items will be stored outside the focus of attention, in the passive buffers. In that case, accessing items for processing will necessitate a retrieval operation; this will slow down response time. This process is referred to as focus switching—the process of shunting items in and out of the focus of attention when the focus gets overloaded (Oberauer, 2003).
An effective working memory system is essential for high-level performance in a wide variety of cognitive tasks. This is either because an effective working memory system depends on the efficient implementation of a host of cognitive control operations that are fundamentally involved in all aspects of the cognitive system, or because a higher working memory capacity allows for a more efficient or less error-prone implementation of such operations, or both. To illustrate, there are sizeable correlations between fluid intelligence and working memory capacity, and between spatial and language abilities and working memory (e.g., Conway et al., 2005; Engle, 2002; Kemper, Herman, & Lian, 2003; Kyllonen, 1996; Salthouse & Pink, 2008). In a large-scale meta-analysis, Ackerman, Beier, and Boyle (2005) conclude that the average correlation between working memory capacity and markers of general fluid ability is .36 (.48 after correcting for unreliability).
Working Memory Performance Declines With Advancing Age
The brunt of the research shows that working memory capacity for verbal material declines with advancing adult age (for a meta-analysis, see Bopp & Verhaeghen, 2005). Small age differences are already found in short-term memory tasks that rely primarily on passive storage of information: Older adults’ capacity in such tasks is on average 92% that of the capacity of younger adults (e.g., while younger adults had an average digit span score of 7.6, older adults’ average digit span score was 7.1). These differences are magnified in true working memory tasks, where performance of older adults on average reaches only 74% that of younger adults (e.g., younger adults had average reading span scores of 3.5 compared to 3.0 for older adults). In a meta-analysis focusing on studies that used the full adult age range (with mostly, but not exclusively, verbal tasks), Verhaeghen (2014) found an age-ability correlation of −.12 for short-term memory (25 studies), and −.42 for working memory (17 studies), revealing the same dissociation between mere storage tasks and true working memory tasks. The age-working memory correlation is comparable in size to the correlations between age and episodic memory (−.38), reasoning (−.45), or spatial ability (−.41) in the same meta-analysis. The half-life of working memory is about 70% shorter in older adults compared to younger adults (7.7 s vs. 5.9 s; Verhaeghen, 2014).
Also noteworthy is that there is a systematic relationship between working memory capacity measures of younger and older adults: When capacity estimates for older adults are plotted as a function of capacity estimates for younger adults (with each point representing the estimate from a single study), the data are well described (R2 = .98) by a single line going through the origin (Bopp & Verhaeghen, 2005). One conclusion is that capacity of older adults is a simple fraction of that of younger adults. Another conclusion is that the age-related difference in capacity is quantitative rather than qualitative, that is, whatever task manipulation or difference in stimulus set produces a difference in younger adults’ capacity has the same effect on older adults’ capacity.
One aspect of the system that may be spared in old age is the focus of attention, as exemplified by age-related differences in N-back tasks. In N-back tasks, participants are presented with a running series of stimuli and decide whether the current stimulus is the same as the item presented n positions back. In a 1-back task, the item to be compared with the item on screen can be held in the focus of attention, and therefore response times are fast and identification is highly accurate (McElree, 2001). However, when n > 1, the capacity limit of the focus of attention is exceeded, and additional items must be held in the activated portion of long-term memory, necessitating a focus switch. The consistent finding is that age-related differences in N-back accuracy are small or nonexistent on 1-back tasks, but large and significant when n > 1 (e.g., Verhaeghen & Basak, 2005). This suggests a bifurcation where age-related differences only appear as soon as the task involves a focus switch, that is, when the memoranda need to be stored outside the focus of attention.
While the decline in verbal working memory performance is sizeable, the decline in spatial working memory (where the memoranda are stimuli such as line segments, crosses in a grid, or dots in a quasi-random configuration) is even larger. In the largest relevant adult lifespan study (n = 388), Hale and colleagues (2011) observed that performance on spatial short-term and working memory tasks declined twice as fast as performance on verbal short-term and working memory tasks: 0.026 z-units per year for spatial tasks versus 0.013 z-units per year for verbal tasks.
What Are the Causes of Age-Related Decline in Working Memory Performance?
At least two types of causes for age-related decline in working memory performance have been posited. These are not mutually exclusive; like anything in cognitive aging, the aging of working memory likely has multiple antecedents.
Cognitive Resources Declines
The first potential cause concerns age-related declines in task-general, across-the-board cognitive resources. One such resource is speed of processing as indexed by reaction time tasks or, more often, tasks of perceptual speed such as digit symbol substitution tests. Older adults show age-related slowing in such tasks; the correlation between adult age and speed of processing is strong, r = −.53 in a meta-analysis of 73 studies using the full adult age range (Verhaeghen, 2014). In this meta-analysis, the decrease in processing speed explained more than 90% of the age-related variance in short-term memory performance and about 60% of the age-related variance in working memory. Other resources include the efficiency and efficacy of sensory processing (e.g., Schneider & Pichora-Fuller, 2000).
One possible explanation for this relationship is that it is directly causal, that is, that age-related slowing is an indicator of a slowing down of the general clock speed of the mind. Such mental slowing should have consequences: Rehearsal time is likely to suffer, and rehearsal time is directly related to short-term memory capacity (Baddeley, Thomson, & Buchanan, 1975). Additionally, when less time is available for processing, performance might suffer either directly because external deadlines for processing expire (Salthouse, 1996 calls this the limited-time mechanism) or indirectly because the intermediate results of processing, stored in working memory, might have decayed by the time they are needed (the simultaneity mechanism Salthouse, 1996). Models of this kind imply a cascade (Fry & Hale, 1996, 2000): The idea is that age-related deficits in basic aspects of processing are interrelated, with a flow of directionality of effects (and thus a hierarchy) within those basic aspects. In the model proposed by Fry and Hale, age-related changes in processing speed influence working memory performance, which in turn affects performance on more complex tasks of fluid ability.
Another possible explanation is that the age-related decline in basic resources has an indirect causal effect on working memory. The clearest instantiation of this idea within the field of working memory is the effortfulness hypothesis, which claims that deficits in sensory processing cause age-related differences in cognitive functioning through mechanisms such as resource overlap, resource competition, and resource trade-offs (e.g., Pichora-Fuller et al., 2016; Wingfield, Tun, & McCoy, 2005). The assumption is that both sensory processing and cognitive processing require the deployment of shared mental resources. If older adults invest more effort in the initial, perceptual stages of processing, this would come at the cost of processing resources that would otherwise be available for downstream operations, including effective working memory encoding, maintenance, and retrieval. Such costs would potentially be exacerbated if the perceptual stage gets successfully resolved because a lot of the available resources would then be expended at the perceptual stage. The effortfulness hypothesis has so far been tested by adding auditory noise to span tasks for younger adults, who then show the same type of working memory deficits as older adults do (Baldwin & Ash, 2011; Rabbitt, 1968), or by statistically controlling for individual differences in hearing (e.g., Wingfield et al., 2005) or visual acuity (Porto et al., 2016), which then leads to a decrease or disappearance of age-related difference in measures of working memory functioning.
A third possibility is that age-related slowing is not a true causal mechanism, but simply a biomarker or proxy par excellence. That is, perceptual speed might be the most sensitive and earliest behavioral indicator that a more general, low-level underlying suboptimality is creeping into the substrate (see Anstey, 2008 for a review on biomarkers of aging). Speed then acts as the canary, so to speak, in the coalmine of the aging mind. Consistent with this hypothesis, age-related slowing has been associated with a loss of brain connectivity (e.g., Cerella & Hale, 1994), with changes in neurotransmitter systems (e.g., Bäckman et al., 2000), with changes in brain glucose metabolic rate or intracellular pH levels (e.g., Hoyer, 2002, and with the degree of neural demyelinization (e.g., Anderson & Reid, 2005). High cognitive speed can then be considered to be an indicator of a well-functioning substrate at the apex of its integrity; decreases in speed are indicative of insults to the system. Under this scenario, measures of speed might mediate age-related variance simply because measures of speed are closer indicators of such a general mechanism than more specific cognitive measures.
A related, final proposal is that the measures used to tap processing speed are, in fact, indicative of a yet deeper underlying set of basic abilities that are shared with more complex tasks, such as tasks of working memory capacity. For instance, Nettelbeck (2001) has argued that even simple tasks of response time measure “focused attentional capacities to detect organization and change under severe time constraints [as well as] decision processes [. . .] that monitor responding” (p. 459). It is these attentional capacities and decision-making processes that even very basic tasks have in common with working memory tasks. In support of this conjecture, researchers modeling response times using the diffusion model (Ratcliff, 1978) have found that drift rate, a parameter indicative of the general speed of information accumulation in the system, is a strong predictor not just of psychometric speed, but also of working memory, and of reasoning ability as well (Schmiedek, Oberauer, Wilhelm, Süß, & Wittman, 2007). Under this interpretation, speed is not causal; fast response times are simply an indicator of a system with an excellent drift rate, which also leads to high performance on other tasks. Because simple speeded tasks are determined to a larger extent by this basic parameter than more complex tasks are, they are more sensitive to an age-related decline in the speed of general information accumulation, and thus might give the appearance of being causal variables.
Higher-Resolution Process Deficits
A second category of explanations aims for higher resolution than task-general accounts. Theories in this category postulate age-related deficits that are specific to particular basic control processes that operate in working memory, considered to go over and beyond the effects of general age-related slowing.
Resistance to Interference
Three types of control processes have been researched extensively in the field of cognitive aging (see Miyake et al., 2000 for a classification of control processes). First, resistance to interference, also known as inhibitory control, has been a central explanatory construct in aging theories since the late 1980s (e.g., Hasher & Zacks, 1988). Effective working memory functioning depends on efficient resistance to distractors (internal or external); a breakdown in inhibition leads to mental clutter in working memory, thereby limiting its functional capacity and perhaps also its speed of operation. It has been demonstrated that, contrary to younger adults, older adults do show evidence of less efficient inhibitory processing. For instance, they do process items clearly marked as irrelevant (e.g., Gazzaley et al., 2008), they have more difficulties in removing no-longer-needed stimuli from both working memory (e.g., Schmiedek, Li, & Lindenberger, 2009) and episodic memory (Titz & Verhaeghen, 2010), and they are likely to hyperbind, that is, remember associations between items that were only incidentally paired (Campbell, Hasher, & Thomas, 2010). The average correlation between adult age and different measures of resistance to interference is −.45 (Verhaeghen, 2014).
There is, however, controversy over whether these data should really be interpreted in terms of a control deficit. For instance, age-related differences in brain activation indicative of inhibitory functioning are often observed in the presence of a group difference in performance. This raises the possibility that the differences observed in the dependent variable of choice may be due not to the aging process per se, but to group differences in mean difficulty levels. Research on younger adults has suggested that when capacity limits are exceeded, individuals engage selection processes that are not normally a determinant of working memory capacity (Bengson & Luck, 2015; Cowan et al., 2010; Cusack et al., 2009). In line with this conjecture, review papers by Fabiani (2012) and Reuter-Lorenz and Cappell (2008) have pointed out that when task difficulty is equated, age differences in neural activity often (but not always) disappear.
A second issue is that executive control is often assessed using difference scores (e.g., the Stroop effect is the difference in response time between color naming in the incongruent condition and the color naming in either a neutral baseline or a congruent condition). Difference scores can be psychometrically problematic when groups differ in baseline speed, as younger and older adults do. Hence, group differences in baseline speed should be partialed out of the difference scores to provide an assessment of inhibition performance over and beyond the effects of general age-related slowing. When this is done, executive control in tasks of selective attention (including resistance to interference) does not show a specific age-related deficit (Verhaeghen, 2011).
A second control process that has been posited to be age sensitive is the ability to maintain and coordinate distinct tasks or distinct processing streams simultaneously or sequentially (Braver & West, 2008). Some of this literature pertains to dual-task performance (Verhaeghen et al., 2003), some of it to flexible switching between tasks (Mayr, Spieler, & Kliegl, 2001); the concept has also been investigated using more directly working-memory-inspired tasks (Fisk & Sharp, 2004). The one extant meta-analysis on dual-task effects and aging (Verhaeghen, Steitz, Sliwinski, & Cerella, 2003) reports that a requirement to maintain and coordinate two or more tasks leads to a 20% increase of age-related slowing compared to the slowing present in the tasks performed in isolation. The one meta-analysis on aging and task switching (Wasylyshyn, Verhaeghen, & Sliwinski, 2011) reports that older adults do have specific problems with loading two or more task sets into working memory, as measured by the difference between response times in task-switching blocks compared with blocks in which only a single task was performed (this increased age-related slowing, on average, by 58%). In contrast, older adults do not have a deficit in flexibly switching between sets, as indexed by the difference between switch trials and nonswitch trials in switching blocks. These results suggest that the locus of the age-related deficit is the maintenance of multiple task sets or goal sets (perhaps as a direct consequence of a decline in working memory capacity) rather than the need for coordination per se. The correlation between adult age and task switching is −.35 (Verhaeghen, 2014). Even when the effects of age-related baseline slowing are removed, coordinative ability and the addition of a task-switching requirement do show specific age-related deficits (Verhaeghen, 2011).
Updating and Focus Switching
A third purported age-sensitive control process is memory updating or focus switching—the ability to flexibly remove items from working memory as needed and replace them with new content. The task most often used to measure updating is the aforementioned N-Back task, where reliable age differences in accuracy emerge, especially when N is equal to or larger than 2 (see Verhaeghen  for a review). The origin of this deficit is unclear; it may be sui generis. For instance, it is neither reducible to age-related differences in source monitoring-context binding (Bopp & Verhaeghen, 2007), nor to specific age differences in dealing with the dual-task situation inherent in the kind of memory updating required in these tasks (Verhaeghen & Zhang, 2012); nor is it to specific age differences in resistance to interference originating from intervening stimuli (Verhaeghen & Zhang, 2012). The problem is likely not one of true capacity, at least when measured in terms of available slots: In the N-back task, older adults are able to access at least 5 slots as efficiently as younger adults do (which implicates that they possess these slots; Verhaeghen, 2012). This in turn suggests that the problem might be one of memory resolution (i.e., how detailed and discriminable the representation within a slot is), but this conjecture awaits further research.
Some researchers (e.g., Chen & Naveh-Benjamin, 2012; Peterson, & Naveh-Benjamin, 2016; Sander, Lindenberger, & Werkle-Bergner, 2012) posit a basic breakdown in low-level, that is, perceptual-attentional, binding processes that integrate features into objects and associate these objects with each other (e.g., Zimmer, Mecklinger, & Lindenberger, 2006). This conjecture is typically tested by comparing memory for unbound features with that for features bound into objects, or by comparing memory for objects with memory for associations between objects. Some recent work, however, has sometimes found no such deficits (e.g., Rhodes, Parra, Cowan, & Logie, 2016), suggesting, at the very least, that the deficit is not universal.
Finally, neuroscientists have argued that age-related differences in working memory are marked at least in part by a breakdown in (some, but likely not all, given the discussion cited about deficits in inhibitory processes, flexible task switching, and binding) tasks of top-down control (e.g., Gazzaley, 2012; see Sander, Lindenberger, & Werkle-Bergner, 2012 for a review). Top-down control refers to the enhancement of task-relevant information and processes and the suppression of task-irrelevant information and processes. According to this position, older adults may fail in either or both of these types of control. The neural architecture of working memory (for reviews, see Reuter-Lorenz & Sylvester, 2005; Sander et al., 2012) includes contributions of the prefrontal cortex (especially the dorsolateral prefrontal cortex [DLPFC], Brodmann area 46, and area 9), which implements top-down control by biasing information processing in posterior regions. Especially the DLPFC is known to be vulnerable to age, with clear shrinking of gray matter; the white matter tracts associated with top-down control also undergo considerable deterioration. These structural changes are accompanied by functional changes. From their review of 16 studies, Reuter-Lorenz and Sylvester conclude that, regardless of whether performance is matched in the two age groups, older adults tend to activate different brain regions compared to younger adults. Notably, older adults are more likely than younger adults to recruit prefrontal areas and are also more likely to present bilateral activation where younger adults show clear lateralization. Importantly, within groups of older adults, these patterns of overactivation tend to correlate positively with performance, suggesting that their role is compensatory. This has led Reuter-Lorenz to posit the CRUNCH hypothesis (compensation-related utilization of neural circuits hypothesis), which states that older adults will utilize compensatory strategies earlier in the difficulty continuum than younger adults do. Specifically, older adults exert more cognitive control in tasks of mere storage than younger adults do, a result that possibly explains why age differences in behavioral performance on these tasks are minimal. Note that this conclusion fits well with the effortfulness hypothesis.
While it appears to be the case that some explanations in terms of control processes fare relatively well, one crucial question concerns the extent to which explanations based on control processes explain more of the age-related variance in working memory than simple resource explanations (such as age-related slowing) do. It appears this might not be the case: In his meta-analysis, Verhaeghen (2014) found that resistance to interference explained 41% of the age-related variance in working memory capacity, and task switching 28%; both numbers are markedly lower than the 62% of the age-related variance in working memory explained by speed-of-processing. (There were not enough studies on updating to warrant a meta-analysis.) Interestingly, control processes were implicated to an extremely large extent in passive storage (as testified by the finding that resistance to interference explained 91% of the age-related variance in short-term memory, and that task switching explained 94% of the variance in short-term memory)—effect sizes comparable to those of speed-of-processing, which explained 93% of the variance. The control processes themselves were associated to a large degree with speed-of-processing: Speed-of-processing explained 79% of the age-related differences in resistance to interference, and 70% in task switching.
The picture that emerges from the literature, then, is that control processes do govern age differences in passive working memory tasks, but that—surprisingly perhaps—true working memory tasks are relatively impervious to age-related decline in the two control processes for which we have sufficient data for a meta-analysis. What this suggests is that the deficit in working memory might be more closely tied to broad resource-type mechanisms than to a breakdown in specific control processes. The portion of the age-related variance that cannot be explained by the basic resource-tied mechanisms is likely due to specific memory processes, such as the updating or focus-switching process, or deficits in attentional binding.
Downstream Effects of Age-Related Differences in Working Memory Performance
Given that working memory is a crucial system within the cognitive architecture, one would expect that the age-related declines in its working and capacity would have downstream effects on more complex aspects of cognition. This is reflected in the theoretical positions described in the section “What Are the Causes of Age-Related Decline in Working Memory Performance?”. The cascade model, for one, maintains that age-related differences in basic resources, such as speed and working memory, limit the effective deployment of higher-order cognitive processing; declines in working memory capacity would then suppress performance on fluid intelligence, reasoning, and the like. Likewise, researchers within the cognitive-control tradition (e.g., Heitz et al., 2006) have posited that efficient control is the link between working memory capacity and fluid aspects of cognition. More particularly, recent individual-differences studies in the area have shown that the updating component of working memory is quite strongly related to fluid intelligence (e.g., Schmiedek, Hildebrandt, Lövdén, Wilhelm, & Lindenberger, 2009; Shelton, Elliott, Matthews, Hill, & Gouvier, 2010; Unsworth & Engle, 2008). In line with this proposal, updating explains quite a bit of age-related variance in at least some aspects of higher-order cognition (Chen & Li, 2007).
In general, working memory measures indeed explain a substantial proportion of the age-related deficits observed in higher-order aspects of cognition. In his meta-analysis, Verhaeghen (2014) estimated the amount of age-related variance in each of three criterion variables (episodic memory, reasoning ability, and spatial ability) accounted for by processing speed, short-term memory capacity, and working memory capacity. Depending on the criterion variable, working memory capacity explained between 50% and 70% of age-related variance; short-term memory capacity explained considerably less—less than 10% of age-related variance; processing speed’s explanatory power was on par with that of working memory—55% to 70%. Importantly, working memory capacity reliably explained age-related variance over and above the variance already explained by processing speed—between 5% and 10%. This suggests that something more than a simple cascade is happening, and that there might be something specific about working memory that measures of capacity and measures of fluid intelligence have in common. Combined, speed and working memory account for 60% to 80% of age-related differences in higher-order cognition. Working memory has also been implicated in age-related changes in linguistic processing, including both the comprehension and production side of language (e.g., Kemper, 2012).
Plasticity in Working Memory Performance in Old Age
Are the age-related declines in working memory capacity and functioning, and the possibly concomitant changes in more complex aspects of cognition, irreversible?
Numerous studies investigating the effects of cognitive interventions in general have shown that cognitive plasticity (i.e., the potential modifiability of a person’s cognitive abilities and brain activity) is substantial up to very old age (e.g., Gross et al., 2012; Karbach & Schubert, 2013). Working memory is typically trained using process-based training, where participants are trained in a particular working memory task or a specific control process implicated in working memory performance; often this takes the form of extended but unguided practice with the task. In a recent meta-analysis of such extended-practice working memory training studies in older adults, it was found that working memory training leads to significant and large improvements in the trained tasks (Karbach & Verhaeghen, 2014). The gain is about 1 SD; net gain, after subtracting the effects of control treatment, is about 0.80 SD.
Transfer effects from this type of intervention have inspired heated debates in the literature on younger adults and children (e.g., Melby-Lervag & Hulme, 2013; Redick et al., 2013; Shipstead, Redick, & Engle, 2012), with the emerging consensus that working memory training transfers to other working memory tasks, but not to more complex aspects of cognition, such as fluid intelligence. Karbach and Verhaeghen concluded, in line with these analyses, that working memory training results in clear and quite large transfer effects to other working memory tasks, with a gain of about 0.60 SD; net gain, after subtracting the effects of control treatment, is about 0.40 SD; net treatment effect at posttest is about 0.55 SD. Unlike the findings in younger adults and children, however, older adults also show transfer effects to far-transfer tasks, that is, tasks that measure a different cognitive construct than the task trained. The gain is about 0.50 SD; net gain, after subtracting the effects of control treatment, is about 0.30 SD. In an update of this meta-analysis, removing outliers and adding two more recent studies and taking pretest differences between groups into account, Melby-Lervag and Hulme (2016) concluded that the effects were small but reliable for comparison with passive control (0.15 SD), but essentially zero (0.02 SD) for comparison with active control. The end conclusion is that far transfer, even compared to passive control, is likely to be too small to make a meaningful difference in older adults’ daily life.
Conclusions and Future Directions
Working memory is clearly sensitive to age. True working memory tasks are more age-sensitive than short-term memory tasks, and spatial tasks show larger age-related effects than verbal tasks. Somewhat counter-intuitively, it appears that control processes might explain more of the age-related variance in short-term memory tasks than in working memory tasks; conversely, age-related differences in working memory appear to be better explained by a decline in more general processing resources. Age-related differences in working memory cascade down to effects in episodic memory, reasoning, and other aspects of fluid cognition. Although working memory remains plastic in old age, working memory training appears to be a doubtful route for ameliorating age effects in other aspects of cognition.
One clear issue for further research would be a better integration between behavioral studies and neuroscience paragraphs, especially with regard to operationalizations of the concept of resources. Another challenge concerns the disentangling of the effects of aging from those of task difficulty in neuroimaging studies. Finally, a working memory intervention that would indeed yield benefits on other aspects of cognition and daily functioning would be the holy grail of aging working memory research.
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