Span, CRUNCH, and Beyond: Working Memory Capacity
and the Aging Brain

Nils J. Schneider-Garces, Brian A. Gordon, Carrie R. Brumback-Peltz,
Eunsam Shin, Yukyung Lee, Bradley P. Sutton, Edoardo L. Maclin,
Gabriele Gratton, and Monica Fabiani

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Astratto

■ Neuroimaging data emphasize that older adults often show
greater extent of brain activation than younger adults for similar
objective levels of difficulty. A possible interpretation of this find-
ing is that older adults need to recruit neuronal resources at lower
loads than younger adults, leaving no resources for higher loads,
and thus leading to performance decrements [Compensation-
Related Utilization of Neural Circuits Hypothesis; per esempio., Reuter-
Lorenz, P. A., & Cappell, K. UN. Neurocognitive aging and the
compensation hypothesis. Current Directions in Psychological
Scienza, 17, 177–182, 2008]. The Compensation-Related Utiliza-
tion of Neural Circuits Hypothesis leads to the prediction that acti-
vation differences between younger and older adults should
disappear when task difficulty is made subjectively comparable.
In a Sternberg memory search task, this can be achieved by as-

sessing brain activity as a function of load relative to the indi-
vidualʼs memory span, which declines with age. Specifically, we
hypothesized a nonlinear relationship between load and both
performance and brain activity and predicted that asymptotes in
the brain activation function should correlate with performance
asymptotes (corresponding to working memory span). The re-
sults suggest that age differences in brain activation can be
largely attributed to individual variations in working memory span.
È interessante notare, the brain activation data show a sigmoid relation-
ship with load. Results are discussed in terms of Cowanʼs [Cowan,
N. The magical number 4 in short-term memory: A reconsidera-
tion of mental storage capacity. Behavioral and Brain Sciences,
24, 87–114, 2001] model of working memory and theories of im-
paired inhibitory processes in aging. ■

INTRODUCTION

Working memory (WM) is a system that allows us to store
and manipulate small amounts of information for a short
time (Baddeley, 1986; Baddeley & Hitch, 1974). One of
the most intriguing findings in cognitive psychology is that
the capacity of WM is in fact very limited, although there is
some debate as to exactly how many items can be main-
tained and manipulated. Mugnaio (1956), in a classic article,
proposed that the capacity of WM is 7 ± 2 items. Howev-
er, in a more recent review of a large number of studies,
Cowan (2001) proposed that the core of the WM system
can only hold 4 ± 1 items and that additional processes
such as “chunking” are required for more items. An im-
portant aspect of Cowanʼs model is that WM is seen as a
part of a more extended memory system, in which a small
number of items are activated out of a much larger pool,
so as to be readily available for the performance of a par-
ticular task. The limitation, Perciò, is not really in mem-
ory capacity per se but in how many items can be kept
into the focus of attention at any point in time. Così, Questo

University of Illinois at Urbana-Champaign

“activation capacity” is assumed to be dependent on atten-
tion deployment, and WM is assumed to be limited by at-
tention span. A similar view has been proposed by Engle
and Kane (2004), Kane, Bleckley, Conway, and Engle
(2001), and Kane and Engle (2000). A related question is
how WM capacity is linked to brain activations during WM
compiti. To address this question, this study aims at examin-
ing in detail the changes in brain activity that are observed
when WM capacity limits are reached.

Although the majority of studies of WM capacity have
been carried out in young adults, in the last several de-
cades researchers have also investigated how WM changes
with age (per esempio., Craik & Byrd, 1982; Craik, 1968). Several
studies have shown that, similarly to other cognitive func-
zioni, WM performance declines with increasing age (per esempio.,
Bopp & Verhaeghen, 2005; Park et al., 2002; Verhaeghen
& Salthouse, 1997). Several theories have been developed
to explain this decline. Per esempio, Salthouse (1996)
proposed that aging leads to reduced speed of process-
ing, rendering it more difficult to maintain many items
in memory at a time. Tuttavia, it is also possible to link
age-related WM decline to a reduced ability to maintain
an appropriate/stable attention focus. Infatti, there is sub-
stantial evidence of reduced inhibition of the processing

© 2009 Istituto di Tecnologia del Massachussetts

Journal of Cognitive Neuroscience 22:4, pag. 655–669

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of distracting or irrelevant information in older adults,
which may support such a scenario (Hasher, Lustig, &
Zacks, 2008; Hasher & Zacks, 1988). In our own research,
we have also found evidence supporting the claim that
older adults may have problems inhibiting the processing
of irrelevant information present within the experimental
context (Fabiani, Basso, Wee, Sable, & Gratton, 2006; Fabiani,
Friedman, & Cheng, 1998; Fabiani & Friedman, 1995). An
inappropriate focus on irrelevant/distracting information,
stemming from age-related difficulties in attention control,
may effectively reduce the WM capacity that is available
for the task at hand and lead to decreased performance.

È interessante notare, this view of WM decline in aging does
not necessarily imply that older adults should show down-
regulation of their brain activity during WM tasks compared
with younger adults. Infatti, functional neuroimaging data
provide numerous examples of an increased number of
areas showing up-regulation during the performance of
several cognitive tasks, including WM tasks (per esempio., Riecker
et al., 2006; Park et al., 2003; Reuter-Lorenz, Stanczak, &
Mugnaio, 1999; Grady et al., 1994; for a review, see Reuter-
Lorenz & Lustig, 2005). In many cases, the data indicate
the occurrence of bilateral activations in older adults when
younger adults only show unilateral activity (Hemispheric
Asymmetries Are Reduced in OLD, HAROLD); Cabeza et al.,
2004; Cabeza, 2002; see also Reuter-Lorenz et al., 1999). In
other studies, this up-regulation has involved areas within
the same hemisphere (per esempio., Payer et al., 2006).

These age-related increases in brain activity are consis-
tent with the concept of dedifferentiation (Lindenberger
& Baltes, 1997; see also Spearman, 1927): Older adults
may not be able to activate networks as selectively or as
efficiently as younger adults, therefore activating networks
in both hemispheres or involving additional areas. Così,
such data could be interpreted as indicators of neuronal
dysfunction (cioè., the inability to suppress inappropriate
processing leading to conflict or reduced availability of
resources; Per esempio, Zarahn, Rakitin, Abela, Flynn, &
Stern, 2007; Rypma, Berger, & DʼEsposito, 2002) or as
compensatory activity for impaired functioning (cioè., a vi-
carious processing route may be used when the appro-
priate processing units are not as readily available; for
esempio, Cabeza, 2002; Reuter-Lorenz, Marshuetz, Jonides,
& Smith, 2001; Rypma & DʼEsposito, 2001; McIntosh et al.,
1999).

Recent studies show that increasing task loads may in-
duce not only older adults but even younger adults to up-
regulate activity in some cortical regions (per esempio., Mattay
et al., 2006). To account for both the age-related deficits
and these load effects, Reuter-Lorenz and Cappell (2008)
and Reuter-Lorenz and Lustig (2005) proposed that, In
general, people will activate more cortical regions as task
load increases (Compensation-Related Utilization of Neu-
ral Circuits Hypothesis; CRUNCH). Tuttavia, because of
less efficient processing, it may be necessary for older
adults to recruit these regions at lower load levels than
younger adults. This hypothesis thus argues that older

adults might recruit cognitive resources at lower loads to
compensate for cognitive decline. Therefore, one would
expect to see a sharper increase in fMRI signal for low load
levels in older adults than in younger adults.

How would this hypothesis interact with the capacity
limits of WM? We hypothesize that, as WM load increases,
brain activity should increase up to where the memory
capacity limit is reached. After that, brain activity should
stop increasing, either because there are no further re-
sources available or because there is no performance
advantage in deploying brain resources any further. How-
ever, this limit should be reached earlier in older adults
than in younger adults, resulting in a ceiling effect for
both the fMRI signal and performance. If load is varied
parametrically across several levels from low to high,
older adultsʼ fMRI activation should follow a nonlinear
pattern, with a sharp increase at the beginning and a flat-
tening at higher loads. The predicted fMRI asymptote
at higher loads corresponds to the performance pat-
tern predicted by the Cowanʼs (2001) theory; CRUNCH
adds the prediction of a sharper increase in fMRI signal
for low loads in older adults. Observation of both pat-
terns requires data from several levels at both low and
high loads.

The Sternberg (1966) memory search task appears to
be a particularly useful tool to examine the relationship
between memory load and brain activation in younger
and older adults. This paradigm allows for parametric var-
iations of memory load by using different memory set
sizes. It has also been extensively studied using fMRI
( Veltman, Rombouts, & Dolan, 2003; Bunge, Ochsner,
Desmond, Glover, & Gabrieli, 2001; DʼEsposito, Postle,
& Rypma, 2000; Henson, Burgess, & Frith, 2000), including
experiments comparing the results obtained in younger
and older adults (per esempio., Zarahn et al., 2007; Rypma et al.,
2002). These data generally show increasing activations
in medial and lateral pFC as well as in parietal cortex, con
increasing memory loads. Results have also indicated the
existence of age-related changes, but the interpretation
of these effects has been complicated by the presence
of large individual differences (Rypma, Berger, Genova,
Rebbechi, & DʼEsposito, 2005), which have been attributed
to variations in strategies. These strategic differences may
have been in part due to the use of a slow event-related
progetto, with very long intervals (>10 sec) between the
presentation of the memory set and of the probe stimulus,
which may have encouraged participants to use elaborative
rehearsal. In the current study, we parametrically varied
memory set size from 2 A 6 and chose a much shorter
interval (4 sec), which should reduce strategic differences,
but still leave enough time for both younger and older
adults to encode the memory set stimuli.

When considered in all its implications, the CRUNCH
model explains overrecruitment and underrecruitment
of brain areas in older adults in terms of the relative ac-
tivation necessary to cope with the task and to compen-
sate for deficits. Taken in this light, Perciò, CRUNCH

656

Journal of Cognitive Neuroscience

Volume 22, Numero 4

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leads to a very strong prediction: The difference in brain
activation level between younger and older subjects
should disappear once the difficulty of the task is equated
between the two groups. This should occur when mem-
ory load is not considered in absolute terms but relative
to WM span. In turn, this requires the assessment of
memory capacity/span in each individual, so that a curve
of brain activation by subjective memory load can be
computed on a subject-by-subject basis and then exam-
ined across groups. There are in fact several procedures
that are commonly used to assess memory span. Of
these, two of the most frequent are backward digit span
(Wechsler, 1981) and operation span (O-SPAN; Engle &
Kane, 2004; Kane et al., 2001; Kane & Engle, 2000). Al-
though very useful, these measures are limited because
they do not directly estimate WM capacity within the
same task used to assess brain activity (in this case, IL
Sternberg task). It is therefore difficult to exactly scale
the scores obtained by each individual subject in these
span tests so that they are made consistent with the mem-
ory loads used in the Sternberg task.

To address this problem, we derived a measure of
WM span directly from the performance obtained within
the Sternberg task. We followed suggestions by Cowan
(2001) to estimate the amount of information that is trans-
mitted during the memory task (which we refer to here
as “throughput”). We then used this measure to estimate
the memory span of each individual within the Sternberg
task, providing estimates that are expressed in the same
unit (memory load as a function of the number of items
in the memory set) used to classify the brain activation
function. This allowed us to measure brain activity as
a function of how large the memory load was with re-
spect to memory span for each individual (for a simi-
lar approach in young adults see Todd & Marois, 2005).
Using these data, we could then evaluate whether simi-
lar activation by subjective load functions were found in
younger and older adults—or, in other words, whether
age-related differences in these functions disappeared
when difficulty was normalized by WM capacity, as pre-
dicted by CRUNCH.

METHODS

Participants

This study was part of a more extended project aimed at
examining changes in neurovascular coupling as a function
of aging and physical fitness. For this reason, the older
group was larger than the younger group. The original
sample included 17 younger adults recruited from the Uni-
versity of Illinoisʼ student population and 33 older adults
recruited through ads in local newspapers, campus-wide
e-mailings, and postings at area gyms, retirement homes,
and community centers. For the purposes of the current
study, Tuttavia, the relevant measures were only available
from a smaller set of 42 subjects (behavioral measures
were not available in 4 subjects, E 4 additional subjects
were discarded because of significant movement artifacts
in fMRI recordings). Così, the younger sample included
12 subjects (age range = 18–27 years, mean age = 23.8 years,
6 women); the older sample included 30 subjects (age
range = 65–80, mean age = 70.9 years, 13 women). Youn-
ger and older adults did not differ in years of education
or scores in the Vocabulary subtest of the Wechsler Adult
Intelligence Scale-Revised ( Wechsler, 1981). They were
significantly different on the modified Mini-Mental Status
examination (Mayeux, Stern, Rosen, & Leventhal, 1981)
and on the O-SPAN (La Pointe & Engle, 1990). The demo-
graphic characteristics of the participants are summarized
in Table 1.

Screening Procedures

Participants were screened based on a number of health
and cognitive criteria. Prospective subjects were excluded
from the study if they regularly took medications that are
known to affect the CNS (per esempio., beta blockers, CNS stimu-
lants, antidepressants, antipsychotics, sedating antihista-
mines, or migraine medications). Subjects with serious
or chronic medical conditions were also excluded. Addi-
tionally, subjects had to score at least 51 on the modified
Mini-Mental Status examination, show no signs of depres-
sion on Beckʼs Depression Scale (Beck & Steer, 1996), E

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Tavolo 1. Mean (with Estimated Standard Error in Parentheses) Demographic Characteristics for Younger and Older Adults

Measure

Age (years)

Education (years)

Modified Mini-Mental Status examination

Vocabulary subscore of Wechsler Adult Intelligence Scale-Revised

O-SPAN

t tests between groups (two tailed): df = 40.

**P < .05. ***p < .01. Young (n = 12) Old (n = 30) t Test 23.8 (0.7) 16.4 (0.7) 56.7 (0.2) 13.0 (1.0) 25.0 (4.1) 70.9 (0.8) 16.1 (0.6) 55.5 (0.3) 13.3 (0.4) 13.9 (1.5) 0.23 2.37** 0.36 3.33*** Schneider-Garces et al. 657 score above or within one standard deviation of the aver- age score for their age group on the Vocabulary subtest of the Wechsler Adult Intelligence Scale-Revised (Wechsler, 1981). All participants were right-handed (as assessed by the Edinburgh Handedness Inventory; Oldfield, 1971) and had normal or corrected-to-normal vision. Memory Paradigm and Procedures We used a modified version of Sternbergʼs memory search task (Sternberg, 1966), with memory set sizes two through six (see Figure 1). The stimuli to be encoded were up- percase letters (B, D, F, G, H, J, M, R, and T). To prevent a direct visual match, their corresponding lowercase letters were used as probes (see Bunge et al., 2001). The letters were selected because of their different shapes when pre- sented in upper and lower case. Each letter subtended approximately 1.4° of visual angle in the diagonal and was presented using a Resonance Technologies goggles system (Resonance Technologies, Northridge, CA). Each trial was initiated by the presentation of a mem- ory set comprising two to six uppercase letters presented simultaneously for 3 sec, followed by a screen containing only a fixation cross presented for 1 sec. After that, the probe was presented for 500 msec, followed by another fixation cross presented for 1.5 sec. During this 2-sec interval, participants had to indicate whether the probe was part of the preceding memory set by pressing the right or the left button on a response box with the cor- responding hand. The response-hand assignments were counterbalanced across subjects. Each memory set was composed of letters chosen randomly from the set of let- ters listed above, with the proviso that no identical letters were allowed within the same memory set. The probe was part of the memory set on 50% of the trials. Five mem- ory set size conditions (2, 3, 4, 5, and 6) were used in a blocked fashion and were presented in either ascending (2–6) or descending (6–2) order, counterbalanced across subjects. Set Sizes 2–6 were chosen because they encom- pass the memory span predicted by Cowan (2001). For each set size condition, a run consisted of four blocks of eight trials each, with a 20-sec fixation period before the first block and between each block. This yielded a total of four task blocks (32 trials) and four rest blocks per set size condition. All subjects underwent a training session with 128 trials using Set Sizes 4–6 before being tested inside the MRI scanner. Further, a short training block with approximately 32 trials using Set Size 4 was administered just before the fMRI recording began to ensure that participants remem- bered the task instructions. Data Acquisition and Preprocessing The MRI data were recorded with a Siemens Allegra 3-T head-only scanner. The fMRI data were recorded with a fast echo-planar protocol (repetition time = 2 sec, echo time = 25 msec, flip angle = 80°). Thirty-eight slices (3-mm D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D h o t w t n p o : a / d / e m d i f t r o p m r c h . s p i l d v i e r e r c c t . h m a i r e . d u c o o m c / n j a o r c t i n c / e a - p r d t i 2 c 2 l 4 e - 6 p 5 d 5 f 1 / 9 2 3 2 8 / 7 4 1 / 4 6 o 5 c 5 n / 1 2 0 7 0 6 9 9 4 2 4 1 0 2 3 / 0 j o p c d n . b 2 y 0 g 0 u 9 e . s t 2 o 1 n 2 3 0 0 8 . S p e d p f e m b y b e g r u 2 e 0 2 s 3 t / j . . f . / t o n 1 8 M a y 2 0 2 1 Figure 1. Procedures and time line. Top: example trials for Set Sizes 2 and 4. Bottom: order of trials for one subject. 658 Journal of Cognitive Neuroscience Volume 22, Number 4 thickness, 3-mm in-plane resolution, 0.3-mm gap) were collected interleaved and parallel to the anterior and poste- rior commissures. A high-resolution T1-weighted MPRAGE (192 slices, 1 × 1 × 1 mm) was also recorded to enable ac- curate anatomical coregistration. Finally, a fast T2-weighted image was also collected for coregistering the T2* image used for fMRI with the T1 image used for anatomical analysis. The neuroimaging data were preprocessed and analyzed using FSL version 3.1 (http://www.fmrib.ox.ac.uk/fsl/ ). Struc- tural images were processed with SUSAN (part of FSL) to improve the signal-to-noise ratio and BET (part of FSL) was used to perform skull stripping. BET was also used on the functional images. In addition, the functional images were slice-time corrected, motion corrected using MCFLIRT, temporally filtered with a Gaussian high-pass cutoff of 70 sec, and spatially smoothed with a 6-mm FWHM three- dimensional Gaussian kernel. Functional and structural images were coregistered and transformed into the Mon- treal Neurological Institute coordinates before group analy- ses were carried out. Data Analysis Behavior The behavioral data (RT and accuracy) were analyzed with mixed-design ANOVAs with one between-subjects factor (Age) and one within-subjects factor (Set Size). For the ANOVA, the accuracy data were first transformed using the Fisher logit approximation to avoid ceiling effects. Note that, due to the use of a blocked fMRI design, we collapsed across probe items requiring yes or no responses for all these analyses. Further, no significant differences were found between descending and ascending set size presen- tation orders, so the data were combined for all behavioral and neuroimaging analyses. In addition, we also estimated the amount of informa- tion transmitted (throughput), given the number of items in the memory set. Throughput is derived according to the following formula, which is mathematically identical to the k formula introduced by Cowan (2001; see also Cowan et al., 2005): Throughput ¼ ACC − 0:5 0:5 (cid:1) N items; where the chance level of 0.5 is subtracted from the un- corrected overall accuracy (ACC), then range corrected by dividing by 0.5 (as above-chance accuracy can only vary between 0.5 and 1) and finally multiplied by the number of items included in the memory set for that condition. This formula corrects for chance level and takes into ac- count that more information is available at higher load levels. Note that if accuracy is 1 (perfect), the throughput is equal to the number of items in the memory set, which would indicate that all information available is processed (ideal function in Figure 3). By measuring throughput across increasing set sizes, we will be able to estimate WM capacity as the maximum amount of information transmit- ted across set sizes. fMRI The statistical analysis of fMRI data was carried out using FEAT (fMRI Expert Analysis Tool) Version 5.63, part of FSL (FMRIBʼs Software Library, www.fmrib.ox.ac.uk/fsl). Group- level analyses were carried out using FLAME (FMRIBʼs Local Analysis of Mixed Effects) Stage 1 only (i.e., without the final MCMC-based stage; Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004; Beckmann, Jenkinson, & Smith, 2003). The overall mean of each group was thresholded using clusters determined by Z > 5.0 and a (corrected)
cluster significance threshold of p = .05 (Worsley, Evans,
Marrett, & Neelin, 1992).1 A linear trend analysis was ap-
plied to the group-level analysis, separately for Set Sizes
2–4 and 4–6. Resulting Z (Gaussianized T/F) statistic images
were thresholded using clusters determined by Z > 3.1
and a (corrected) cluster significance threshold of p = .05
(Worsley et al., 1992).

To determine differences in MR activity by set size slopes
between younger and older adults, a peak voxel analysis
was performed.2 The voxel showing the largest Z-score
within each of a series of ROIs was selected for each
subject and condition. The ROIs were drawn according
to Brodmannʼs areas (BA) as implemented in the WFU
Pickatlas (http://www.fmri.wfubmc.edu; Maldjian, Laurienti,
Kraft, & Burdette, 2003). Specifically, we used the follow-
ing ROIs, each separately for the left and right hemisphere:
BA 18/19, BA 7, BA 6, BA 24/32, BA 44/45/47, and BA 10.
Because the voxel with the largest slope was selected, UN
bias toward higher values was introduced. Tuttavia, Questo
bias should operate equally for each set size condition
and for younger and older adults. Therefore, although
the actual values should not be considered as meaningful,
the comparisons of slopes for Set Sizes 2–4 and 4–6 and the
comparisons between younger and older adults are legiti-
mate. This procedure was selected over alternatives (per esempio.,
using a fixed voxel per subject or per group) because these
alternative analyses may bias the results against the older
group due to increased anatomical variability with age,
which must be taken into consideration given the numer-
ous findings of brain matter loss with age (Gordon et al.,
2008; Rettmann, Kraut, Prince, & Resnick, 2006; Resnick
et al., 2000; Raz et al., 1997). Further, because the fMRI data
were spatially filtered, this procedure is analogous to con-
sidering the weighted average of the largest adjacent voxels
within each ROI.

The resulting peak-voxel data for each ROI were first
tested for a significant overall slope from Set Sizes 2–6,
collapsed across groups and set size conditions. Those
showing a significant slope were then further analyzed to
determine the presence of significant slopes within the
younger and the older groups separately for both lower
(2–4) and higher (4–6) set sizes. We also compared how

Schneider-Garces et al.

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Figura 2. Mean RT (left) E
Fisher-transformed accuracy
(right) across set sizes for
younger and older adults with
estimated standard errors of
the means.

the two slopes (2–4 and 4–6) differed within each group
(younger and older adults).

RESULTS

Behavioral Results

The mean RTs and Fisher-corrected accuracy values are
presented in Figure 2, separately for each age group, ses-
sion, and set size condition. Results from the mixed-design
ANOVA for RT revealed a significant main effect of set size,
F(4,156) = 55.97, P < .01,3 and a significant main effect of age, F(1,39) = 7.16, p < .05, indicating that both younger and older adults were slower with increasing memory load and that older adults, overall, were significantly slower than younger adults. The Set Size × Age interaction was not significant, F(4,156) = 0.56, ns, indicating that the in- crease in RT with increasing memory load was not signif- icantly different for younger and older adults. To keep behavioral analyses in line with those of fMRI data, we also performed two-tailed t tests, directly comparing the slopes for the younger and the older adults for low (2– 4) and high (4–6) set sizes. We also compared the slopes for low and high set sizes separately for younger and older adults. These comparisons revealed no significant effects (for details, see Table 2). Figure 3. WM capacity measured by throughput as a function of set size, separately for younger and older adults, with estimated standard errors of the means. The ideal function (accuracy = 1) is provided for reference purposes. The accuracy analysis also showed a main effect of set size, F(4,156) = 11.47, p < .01, and a main effect of age, F(1,39) = 12.08, p < .01, indicating that both younger and older adults were less accurate for higher memory loads and that the older adults were less accurate compared with the younger adults, respectively. The Set Size × Age inter- action was marginally significant, F(4,156) = 2.05, p < .10. The throughput data are shown in Figure 3. The younger adultsʼ function approached the ideal function, but with a shallower slope. In other words, younger adults were able, on average, to increase throughput up to 4.98 items, oc- curring at Set Size 6, whereas older adults only showed an increase up to 3.46 items, occurring at Set Size 5 with no additional information throughput for Set Size 6. The sepa- rate planned t tests revealed significantly different slopes between groups for low and high set sizes, with the older adults showing a smaller increase for low set sizes and nearly no increase for high set sizes (see details in Table 2). These data indicate that older adults may be unable to maintain, on average, more than four items in WM because additional items beyond Set Size 4 did not significantly increase their throughput measurement. Younger adults, on the other hand, may retain significantly more information. fMRI Results Overall mean contrasts for each age group are presented in Figure 4 and show a number of regions being active during the task. Younger adults showed foci of activation in bilateral occipital, left parietal, left premotor, and left medial frontal cortex. Older adults showed bilateral foci of activation in occipital, parietal, premotor, and medial frontal cortex. These results replicate findings reported in other studies, indicating that WM tasks induce the activation of a dorsal fronto-parietal network (Champod & Petrides, 2007; Cabeza et al., 2004; Cabeza, Dolcos, Graham, & Nyberg, 2002; Cornette, Dupont, Salmon, & Orban, 2001; Jonides et al., 1997), which is left lateralized in the younger adults and bilaterally activated in the older adults. The presence of bilateral activity in older adults in a task showing unilateral activity in younger adults is a common observation in brain imaging studies, as sum- marized by the HAROLD model (Cabeza, 2002). In ad- dition, bilateral activation of occipital areas was found in 660 Journal of Cognitive Neuroscience Volume 22, Number 4 D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D h o t w t n p o : a / d / e m d i f t r o p m r c h . s p i l d v i e r e r c c t . h m a i r e . d u c o o m c / n j a o r c t i n c / e a - p r d t i 2 c 2 l 4 e - 6 p 5 d 5 f 1 / 9 2 3 2 8 / 7 4 1 / 4 6 o 5 c 5 n / 1 2 0 7 0 6 9 9 4 2 4 1 0 2 3 / 0 j o p c d n . b 2 y 0 g 0 u 9 e . s t 2 o 1 n 2 3 0 0 8 . S p e d p f e m b y b e g r u 2 e 0 2 s 3 t / j / . . t . f o n 1 8 M a y 2 0 2 1 Table 2. Slopes for RT and Accuracy by Set Size, Separately for Low (2–4) and High (4–6) Set Sizes and Younger and Older Adults RT ACC Throughput Young 43.79 −0.17 0.98 Slopes 2–4 Old 62.69 −0.21 t Test Young 1.38 0.41 64.10 −0.16 0.75 0.72 2.16** t tests between groups (two tailed): df = 40. *p < .1. **p < .05. ***p < .01. both younger and older adults. This may reflect the visual nature of the task. To examine load effects, we conducted linear trend analyses, separately for low (2–4) and high (4–6) set sizes. These analyses revealed a clear differentiation between the two age groups, with younger adults showing no sig- nificant (i.e., subthreshold) linear trends for low set sizes but pronounced linear increases in several areas for high set sizes and older adults showing significant effects at low set sizes but no further significant increases at high set sizes (see Figure 5).4 For the younger adults, low set sizes were associated with foci of linear increase only in left occipital cortex, whereas high set sizes were associated with linear increases in bilateral parietal and frontal cortex in addition to the left occipital cortex (see Table 3). The older adults showed foci of linear increase in left occipital, bilateral parietal, bilateral premotor, bilateral inferior front- al, and medial frontal cortex at low set sizes but not at high set sizes. These data show both overrecruitment (at low set sizes) and underrecruitment (at high set sizes) in older adults, as postulated by the CRUNCH model. Interestingly, how- Figure 4. Statistical brain maps (axial surface projection) of the task minus rest contrast for younger and older adults, collapsed across set sizes. LF = left front. Slopes 4–6 Old 48.92 −0.27 t Test 0.87 1.04 0.02 3.95*** Slopes Difference Young 20.31 0.01 −0.23 Old t Test −13.77 −0.05 −0.7 1.42 0.36 1.93* D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D h o t w t n p o : a / d / e m d i f t r o p m r c h . s p i l d v i e r e r c c t . h m a i r e . d u c o o m c / n j a o r c t i n c / e a - p r d t i 2 c 2 l 4 e - 6 p 5 d 5 f 1 / 9 2 3 2 8 / 7 4 1 / 4 6 o 5 c 5 n / 1 2 0 7 0 6 9 9 4 2 4 1 0 2 3 / 0 j o p c d n . b 2 y 0 g 0 u 9 e . s t 2 o 1 n 2 3 0 0 8 . S p e d p f e m b y b e g r u 2 e 0 2 s 3 t / j . . / . f t o n 1 8 M a y 2 0 2 1 ever, they also show some bilateral recruitment in younger adults at high loads, suggesting that the recruitment of ad- ditional areas may be a common mechanism to deal with increasing task difficulty or load rather than a mechanism geared at compensating for loss in neuronal efficiency that is specific to aging. To further examine these load effects, we focused our statistical analyses on the peak voxels in the ROIs showing large changes as a function of memory load, which includ- ed BA 18/19, BA 7, BA 6, BA 24/32, and BA 44/45/47. For each of these regions, the voxel corresponding to the peak response was identified, separately for each subject, set size condition, and hemisphere. Because the interest of this study is to evaluate differences in brain activation as a function of memory load, it is important to first minimize the impact of individual (or group) differences on the overall magnitude of the brain oxygen-level dependent (BOLD) response. Therefore, we scaled the peak values ob- served for each subject, hemisphere, and memory set size by the amplitude of the largest response observed across set sizes for each individual subject. These relative am- plitude values were then used for all following analyses. The brain-activation-by-memory-load functions for each ROI peak voxel and for the average across ROIs are pre- sented in Figure 6 (panels B and A, respectively). Results of t test analyses are presented in Table 4. These data in- dicate that, although an increase in brain activity as a func- tion of memory load was observed for most areas in both hemispheres, the pattern was quite different for younger and older subjects: Whereas the younger adults showed most of the increase between Load 4 and Load 6, the older adults showed most of the increase between Load 2 and Load 4. Relationship between Relative fMRI Activation, Memory Load, and WM Span One of the most important predictions of the CRUNCH model is that age-related differences in brain activity are a reflection of the more limited processing capacity of the older adults. As a consequence, older adults require a Schneider-Garces et al. 661 Figure 5. Statistical brain maps (axial surface projection) of linear trend analyses for Set Sizes 2–4 and 4–6 for younger and older adults. LF = left frontal. D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D h o t w t n p o : a / d / e m d i f t r o p m r c h . s p i l d v i e r e r c c t . h m a i r e . d u c o o m c / n j a o r c t i n c / e a - p r d t i 2 c 2 l 4 e - 6 p 5 d 5 f 1 / 9 2 3 2 8 / 7 4 1 / 4 6 o 5 c 5 n / 1 2 0 7 0 6 9 9 4 2 4 1 0 2 3 / 0 j o p c d n . b 2 y 0 g 0 u 9 e . s t 2 o 1 n 2 3 0 0 8 . S p e d p f e m b y b e g r u 2 e 0 2 s 3 t / j t / . . . f o n 1 8 M a y 2 0 2 1 Table 3. Regions of Significant Linear Trends in Younger and Older Participants, Separately for Low (2–4) and High (4–6) Loads Lobe Region of Activation/ Linear Trend Younger adults, linear trend for Set Sizes 2–4 Hemisphere/BA x y z Z Max Cluster Size ( Voxel) Occipital Medial/inferior L 18/19 −16 −100 −6 5.7 Younger adults, linear trend for Set Sizes 4–6 Frontal Middle/premotor Superior/middle/premotor L 6/8 R 6/8 Medial R/L 8, 32/24 Parietal Inferior/superior Inferior/superior Occipital Medial/inferior Cerebellum Older adults, linear trend Set Sizes 2–4 Frontal Premotor/inferior Inferior Medial Medial Inferior Inferior Parietal Occipital Medial/inferior L 7/40 R 7/40 L 18/19 R L 44/6/43 R 45/46 L 6 R 32/24 L 7 R 7 L 18/19 −24 34 6 −28 32 −44 32 −40 52 −4 6 −26 30 −28 0 0 14 −46 −50 −86 −66 0 34 2 14 −76 −58 −96 50 42 46 46 44 −6 −24 28 18 60 44 38 30 2 4.38 3.77 4.25 4.86 4.02 4.79 4.03 4.93 5.04 5.77 4.7 4.66 3.93 4.83 558 601 426 966 1947 674 2232 344 813 254 277 217 397 426 1237 662 Journal of Cognitive Neuroscience Volume 22, Number 4 D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D h o t w t n p o : a / d / e m d i f t r o p m r c h . s p i l d v i e r e r c c t . h m a i r e . d u c o o m c / n j a o r c t i n c / e a - p r d t i 2 c 2 l 4 e - 6 p 5 d 5 f 1 / 9 2 3 2 8 / 7 4 1 / 4 6 o 5 c 5 n / 1 2 0 7 0 6 9 9 4 2 4 1 0 2 3 / 0 j o p c d n . b 2 y 0 g 0 u 9 e . s t 2 o 1 n 2 3 0 0 8 . S p e d p f e m b y b e g r u 2 e 0 2 s 3 t / j . f t . / . o n 1 8 M a y 2 0 2 1 Figure 6. (A) Relative signal change of BOLD response as a function of set size, averaged across ROIs (peak values) in younger and older adults. (B) Relative signal change of BOLD response as a function of set size, separately for each ROI (peak values). (C) Relative signal change of BOLD response as a function of set size, averaged across ROIs (peak values), when WM load is adjusted as a function of each participantʼs span. greater amount of brain activation to handle relative low loads. This implies that differences between younger and older adults should disappear once the load is computed relative to each individualʼs WM span rather than in ab- solute terms. The throughput measure that we have de- scribed earlier can provide a tool for quantifying WM span within the context of the Sternberg memory search task. Specifically, we expect that, when WM span is reached, the throughput measure should reach an asymptote—that is, it should not increase with further increases in memory load. The asymptotic (maximum) value can then be con- sidered as an estimate of WM span, which can be assessed individually for each participant in the study. Because we were interested in using this measure to estimate the brain activation levels relative to memory span, we approximated this value to the nearest integer. The resulting estimates of WM span (based on each personʼs throughput asymp- tote) correlated significantly with O-SPAN measures (r = 0.38, p < .01, one tailed). This indicates that this measure is a valid estimate of WM span. Importantly, it is also a mea- sure obtained during the same task in which the fMRI was recorded. Interestingly, this measure differed significantly between younger (mean = 5.08 items) and older adults (mean = 3.80 items), t(40) = 5.06, p < .0001, although for both groups the estimates were relatively close to the memory span ranges given by Cowan (2001). Another useful characteristic of this measure is that it allows us to evaluate, for each individual subject, what amount of brain activity is required as a function of the relationship between memory load and WM span. The results of this analysis, averaged across all the ROIs that showed significant increases of BOLD response as a func- tion of relative set size and across all subjects in the study, are presented in Figure 6C. The data presented in this figure indicate that the BOLD response increased in a sigmoid (rather than linear) fashion. For very low memory loads (relative to WM span), the curve was essentially flat. However, when memory loads were Schneider-Garces et al. 663 Table 4. Two-tailed t Tests for Slopes of Activation as a Function of Set Size Slope 2–6 Slope 2–4 Slope 4–6 Slope 4–6 Minus 2–4 t Test Overall Young Old t Test between Young Old t Test between Young Old t Test between Overall 4.83*** 1.84* 4.30*** BA 18/19 left BA 18/19 right BA 7 left BA 7 right BA 6 left BA 6 right 4.37*** 3.73*** 4.05*** 1.52 1.03 0.63 3.58*** 4.28*** 3.96*** 4.17*** 1.86* 3.65*** 5.74*** 2.60** 1.59 1.58 4.27*** 1.76* BA 24/32 left 5.78*** 2.58** 4.76*** BA 24/32 right 4.06*** 0.97 3.01*** BA 44/45/47 left 4.14*** 3.91*** 2.87** BA 44/45/47 right 2.53** 1.30 2.64** −1.17 −1.35 −1.66 −1.44 −0.95 −1.02 −0.23 −0.34 −0.71 −0.17 −0.25 2.87** −0.48 2.68** −0.69 2.32** 2.17** 1.09 −0.67 1.27 2.53** −0.55 2.27** 0.80 −2.39** 2.10** 0.83 −2.21** 0.13 −2.47** 0.99 −2.25** 1.01 −0.48 1.06 −0.25 −1.98* 3.96*** −0.47 2.79** 1.11 −2.37** 1.59 −0.22 1.36 2.92** −0.23 2.87** −0.25 2.46** 0.39 0.68 −1.50 2.66** 2.61** 1.44 1.46 0.16 −1.06 0.54 −2.57** 1.22 −1.73 0.10 −1.22 −0.31 −2.26** 2.01* 1.59 2.16** 1.11 2.32** 0.77 1.93* 2.06** 0.90 0.98 Overall, df = 41; young, df = 11; old, df = 29; young versus old, df = 40. *p < .1. **p < .05. ***p < .01. closer to span, the curve rose steeply but flattened once WM span was attained, reaching an asymptote related to the performance (throughput) asymptote. To confirm this visual impression, a set of t tests were performed for consecutive steps of increasing memory load, corrected for multiple comparisons using the Bonferroni procedure. The step between span −1 and span showed a signifi- cant increase in BOLD response, t(41) = 2.98, p < .05 (Bonferroni corrected). None of the other steps reached statistical significance. Such sigmoid function was evident for both younger and older adults, and in both groups of subjects it reached its asymptote when the load was equal to the WM span. No point in the curve showed a significant difference between younger and older adults (all tʼs < 1). Thus, the difference between the fMRI load functions in younger and older subjects (presented in the upper portion of Figure 6A) disappears when individ- ual subjectsʼ WM spans are taken into consideration. This finding is clearly consistent with CRUNCH. In addition, how- ever, it also underscores the fact that a common mech- anism may come into play as subjective load increases, regardless of age (i.e., once individual differences in span are taken into account). To provide further evidence of the relationship between the increase in BOLD activity with load and WM span, we considered the relationship between the throughput mea- sure representing the behavioral span and the load at which the BOLD response reached its asymptote. This was estimated as the first memory load condition at which the fMRI activation reached 80% of its maximum value. This point was established for each subject across regions. The mean value of the fMRI asymptotic point was larger for the younger (mean load = 4.67 items) than for the older adults (mean load = 3.80 items), t(40) = 2.05, p < .05, paralleling the difference in span size between the two groups. In fact, the difference between the point of asymp- tote in the fMRI load function and the span size was similar across groups (younger adults = 0.42 items; older adults = 0.00 items; t = 1.01, ns). The relationship between these two measures for individual subjects is presented in Figure 7. o n 1 8 M a y 2 0 2 1 Figure 7. Three-dimensional scatter plot illustrating the relationship between the point of asymptote in the fMRI activation function and the WM span (maximum value in the throughput function). z-Axis = number of subjects; x-axis = behavioral asymptote; y-axis = fMRI asymptote. 664 Journal of Cognitive Neuroscience Volume 22, Number 4 D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D h o t w t n p o : a / d / e m d i f t r o p m r c h . s p i l d v i e r e r c c t . h m a i r e . d u c o o m c / n j a o r c t i n c / e a - p r d t i 2 c 2 l 4 e - 6 p 5 d 5 f 1 / 9 2 3 2 8 / 7 4 1 / 4 6 o 5 c 5 n / 1 2 0 7 0 6 9 9 4 2 4 1 0 2 3 / 0 j o p c d n . b 2 y 0 g 0 u 9 e . s t 2 o 1 n 2 3 0 0 8 . S p e d p f e m b y b e g r u 2 e 0 2 s 3 t / j . / t . . f This figure indicates a significant correlation between these two measures (r = .44, p < .002, one tailed). DISCUSSION Taken together, the results of this study provide strong, quantitative support for the utilization of neural networks proposed by CRUNCH (Reuter-Lorenz & Cappell, 2008). They further indicate that brain activity is nonlinearly related to WM load. Both of these findings have impli- cations: the first for theories of aging and the second for theories of WM. The results of this study are largely consistent with pre- vious brain imaging data obtained with the Sternberg task (Bunge et al., 2001; DʼEsposito et al., 2000; Henson et al., 2000; Rypma & DʼEsposito, 1999). As in previous work, a large number of areas were activated during this task, in- cluding occipital, prefrontal, parietal, and medial areas.5 When all memory load conditions were combined togeth- er and compared with the rest period (Figure 4), prepon- derantly left hemisphere activations were observed in younger adults, whereas more bilateral activations were observed in older adults. Although this observation is con- sistent with previous aging data (see HAROLD; Cabeza et al., 2004; Cabeza, 2002; see also Reuter-Lorenz et al., 1999), this group difference in lateralization was not evi- dent when the memory-load conditions were contrasted with each other, as bilateral differences in activation were evident at high loads even for younger adults (Figure 5; for a similar finding, see also Bunge et al., 2001). It is possible that the age-related differences in lateralization regardless of load may not be related to WM function per se (which should vary with memory load), but to other aspects of the task, such as perceptual and motor function, which may be common to all memory load conditions. The main purpose of this study was to quantitatively investigate predictions made by CRUNCH (Reuter-Lorenz & Cappell, 2008). CRUNCH is centered on the idea that differences in overrecruitment and underrecruitment of brain areas commonly observed between younger and older adults may reflect age-related differences in process- ing capacity (or ability). Older adults, for reasons that are yet to be completely understood, require more resources than younger adults for processing equivalent amounts of information. For this reason, they require additional re- cruitment of brain activity at lower task loads than younger adults. In our study, we manipulated task load in a parametric fashion, using memory set sizes varying between 2 and 6. The behavioral data indicated that older adults, on aver- age, had significantly more problems with the task than younger adults. This was particularly true at high (>4)
memory set sizes. Infatti, in these conditions, their accu-
racy declined, and the “throughput” analysis revealed
that they reached a ceiling in their capacity to transmit
informazione (cioè., to correctly identify, above chance, dif-
ferent targets in the presence of increasing information)

at about four items. This number was significantly smaller
than that for younger adults (>5). In other words, IL
two age groups differed by more than one full item in
terms of their memory span.

This asymptotic performance level in the older adults
was associated with a clear asymptotic level in brain acti-
vation, as measured with fMRI. The fMRI data indicated
that a number of regions, namely, occipital cortex, pre-
frontal regions, dorsal parietal cortex, and cingulate cor-
tex, showed significant bilateral increases of activity with
set size, presumably reflecting the greater load that a
large memory set imposes on the information processing
system. In all these areas, the activation-by-set-size func-
tions suggested, for the older adults, a large increase be-
tween Set Size 2 and Set Size 4 and a small-to-negligible
further increase at larger set sizes (Guarda la figura 6). Questo
pattern contrasts quite obviously with the data obtained
in the younger group. Although set size effects were ob-
served in similar areas in younger and older adults, IL
younger group showed smaller growth in brain activation
as a function of set size until at least Set Size 4. A pro-
nounced increase in brain activity in the younger group
was observed at higher set sizes.

If the fMRI data were observed in isolation, various hy-
potheses could be entertained about the significance of
these effects. For instance, it could be argued that the
neurovascular system is limited in its capacity to provide
additional oxygenated blood (and therefore flush out
deoxyhemoglobin leading to the BOLD signal) and that
this limit is reached at lower loads in older than younger
adults. This would explain the earlier asymptote in the
older (occurring at Set Size 4) than in the younger sub-
jects (occurring at Set Size 6 or beyond). Although a pos-
sible role of an impaired neurovascular system on brain
function cannot be ruled out by the present study, IL
behavioral data clearly indicate that the older adults reach
a performance asymptote at about Set Size 4—a value
that represents a real limit in processing capacity rather
than a mere artifact of the measuring system.

Così, the data indicate that, whatever the reason, older
adults reach an asymptote in both behavior and brain
activation at lower levels than younger adults. L'interno-
dividualized span analysis provides an even stronger
quantitative support for CRUNCH. It shows that the dif-
ferences in the brain-activation-by-memory-load function
between younger and older adults can be entirely ac-
counted for by differences in span across individuals, Rif-
gardless of their age. When these differences are taken
into account, the curves for younger and older adults
are virtually identical. Così, no special mechanism is re-
quired to account for the different pattern of brain activ-
ity in older adults with respect to younger adults, as this
difference is explained by relative task difficulty. Although
some extant data (per esempio., Stern et al., 2005) are consistent
with the premises of CRUNCH (explicated in Figure 2 Di
Reuter-Lorenz & Cappell, 2008), the current data dem-
onstrate for the first time that relative task difficulty alone

Schneider-Garces et al.

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is sufficient to account for all of the age-related brain ac-
tivation differences, and no other mechanism is required.
This is made possible by the use of an individualized
measure of task-related ability, the throughput measure.
The activation-by-task-load functions are also useful to
highlight another important observation. Namely, Essi
are clearly nonlinear, showing two asymptotes: a floor at
low set sizes and a ceiling at high set sizes, separated by
a region of rapid growth when memory load is approach-
ing span. The presence of a ceiling can be easily explained:
At some level, the brain is not capable of producing fur-
ther activation (at least as measured with fMRI; see also
Reuter-Lorenz & Cappell, 2008). This is associated with a
ceiling in performance (when memory span is reached).
Although causality cannot typically be inferred with brain
imaging data alone, it is very tempting to hypothesize that
a common mechanism leads to both activation and per-
formance asymptotes. This common mechanism may be
a limited capacity of the WM system (a “cognitive” expla-
nation) or a limited capacity of the neurovascular supply to
the brain (an “energetic” explanation). The current data
are insufficient to tease apart these hypotheses, and in fact
there may be no need to do that, as one could argue that
the neurovascular system is built to satisfy functional re-
quirements, and therefore it can be expected that the
two limits should coincide. Further, the current study can-
not be used to determine which specific process of the
many involved during the Sternberg task is the one re-
sponsible for the limitations in performance and brain
activation. The blocked design and fast pace of the study
do not allow us to use the relatively slow hemodynamic
data provided by fMRI to determine whether the effects
are due to processes occurring during encoding, mainte-
nance, or retrieval of information from WM. Other studies
based on event-related fMRI designs, often with longer
and variable delays (per esempio., Grady, Yu, & Alain, 2008; Rypma
et al., 2005; Rypma & DʼEsposito, 2000), or other brain im-
aging methods with higher temporal resolution such as
ERPs or event-related optical signal (Fabiani & Gratton,
2005; Gratton & Fabiani, 2001) can be more useful for this
purpose.

The presence of a floor effect in the brain imaging
data is not predicted by or related to CRUNCH and re-
quires some further consideration. There are three pos-
sible explanations for this phenomenon. Primo, the floor
effect may be an artifact due to the insensitivity of the
hemodynamic measures to lower levels of brain activa-
zione, especially when thresholding is used for statistical
analyses. This possibility is difficult to rule out completely,
although the effect sizes present in this study are in line
with many other published reports. A more interesting
explanation for the floor effect is that it is related to the
presence of a real floor in brain activation. There are
two possible interpretations for this. The first, che è
embedded in the model proposed by Cowan (2001), È
that WM can itself be partitioned into an easily accessible
core of highly activated nodes (whose use requires little

effort) and a “halo” of less highly activated nodes (whose
use requires more substantial effort). In the present case,
when the set size is small, only the core of WM needs to be
accessed and very little effort (and brain activity) is re-
quired. Tuttavia, when the set size is large, the halo comes
into play, with the consequence that a much greater effort
(and brain activity) is involved.

The second interpretation is that the brain activation
observed in this task is, to a great extent, related to the
necessity of maintaining independent chunks of informa-
tion active in WM. In other words, the difficulty is not in
maintaining the information in an active form but in
maintaining the different pieces of information as distinct
and eliminating cross talk. In questo caso, the amount of ac-
tivity should be related to the number of negative cross-
links that need to be established between the different
chunks, which should grow in a combinatory (or expo-
nential) fashion with the number of active chunks. Ac-
cording to this interpretation, brain activation should
grow exponentially until a ceiling is reached (due to lack
of sufficient resources for keeping so many different
representations distinct from each other). Hence, IL
brain-activation-by-set-size function should be a sigmoid,
with the largest growth occurring around span. A depic-
tion of this theoretical view is presented in Figure 8.

Within this framework, the main difference between
younger and older adults is that the sigmoid function
relating memory load to brain activity and performance
is shifted to the left in older adults (which is consistent
with the model proposed by Rypma, Eldreth, & Rebbechi,
2007). For either of the “cognitive” accounts described
above, we would need to determine why older adults
Avere, on average, a smaller span than younger adults, al-
though their respective functions are very similar when
scaled by the individual spans. With respect to the first
hypothesis, this would suggest that the difference between
younger and older adults is in the size of the core area,
which could reflect the ability to maintain focus on par-
ticular memory nodes that carry task-relevant information.

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Figura 8. Schematic depiction of the relationship between subjective
WM load and related brain activation. The average function of older
adults is expected to be shifted toward the left, that of younger
adults toward the right.

666

Journal of Cognitive Neuroscience

Volume 22, Numero 4

This may in turn be seen as an attention control function,
as in fact proposed by both Engle and Kane (2004) E
Cowan (2001). In questo caso, older adults may have a smaller
WM span because they have poorer control of attention—
in other words, the WM core may be less stable regardless
of its size. There is in fact substantial evidence that older
adults may have problems controlling attention and be
more distractible than younger adults (Hasher et al., 2008;
Hasher & Zacks, 1988). This in itself may reflect difficulties
in suppressing the processing of irrelevant information
and be related to a general deficit in inhibitory processes
(per esempio., Fabiani et al., 1998, 2006; Gazzaley, Cooney, Rissman,
& DʼEsposito, 2005; Fabiani & Friedman, 1995).

È interessante notare, inhibitory processes may also be very im-
portant for maintaining separate memory representations—
the crucial element in our second hypothesis. The inhib-
itory deficits commonly observed in older adults may then
account for the difficulty in maintaining separate memory
representations: The increased brain activity observed at
lower loads in older adults may then reflect the require-
ment to overcome the deficit in reciprocal inhibition be-
tween different memory representations. Infatti, the two
hypotheses described above may be two sides of the
same coin: In entrambi i casi, deficits in inhibitory processes
may lead to an inability to maintain attentional focus and
to keep different memory representations active and dis-
tinct and in turn limit the effective size of WM span.

In conclusion, the results of this study provide strong
quantitative support for CRUNCH and in particular for
the relative utilization of neural networks depending on
performance. They are less conclusive, Tuttavia, regarding
the idea of compensation also embedded in CRUNCH.
The difference in brain activation as a function of load that
exists between younger and older adults can be entirely
accounted for by the difference in their WM capacity. In
other words, the data indicate that, given equal objective
memory loads, individuals with lower memory abilities
are deploying more brain activation than those with higher
memory abilities, regardless of age. To the extent that “com-
pensation” is intended as the amount of effort needed to
reach a given level of performance, the data are consistent
with the predictions of the compensation hypothesis. How-
ever, because of their correlational nature, the data do not
provide conclusive information about the causal direction
of the relationship between brain activity and behavior:
We cannot say whether the increased brain activity is used
to improve performance or whether the lack of some gen-
eral ability causes both the increased brain activity and the
decrement in performance. The data also show that the
brain-activation-by-memory-load functions are nonlinear,
displaying both a ceiling and a floor effect. The ceiling effect
is associated with a limit in WM span. The floor effect may
be a reflection of the large difference in the mental effort
required to maintain an increasing number of items active
and/or distinct within WM. This mental effort is likely to
be largely related to inhibitory processes, which may be
impaired in older adults.

Ringraziamenti
This work was supported in part by NIA grant #AG21887 to
Monica Fabiani. We wish to thank Kirk Erikson and Paige Scalf
for advice regarding the fMRI analysis and Nelson Cowan and
Kathy Low for helpful comments on an earlier version of this
manuscript.

Reprint requests should be sent to Monica Fabiani, Beckman
Institute, University of Illinois, 405 North Mathews Avenue,
Urbana, IL 61801, or via e-mail: mfabiani@illinois.edu.

Notes

1. A high Z threshold was used in this analysis (which com-
pares the activity during the task with the activity during fixa-
zione) to prevent merging all the brain activations into a single,
large undifferentiated cluster, given the very large level of activ-
ity in this task. More standard and less conservative thresholds
were used for all other analyses.
2. Given the large size of each ROI, and the fact that in such
cases function within each ROI may not be unitary, we felt that
using the more standard practice of averaging the activity with-
in each ROI would not be appropriate. Tuttavia, because sin-
gle voxel analyses may be unreliable, we also repeated the ROI
analysis using the average of a “box” encompassing 27 contiguous
voxels surrounding the peak as a method for providing a more
stable estimate of activity for each subject and condition. The re-
sults were virtually identical to those obtained with the single voxel
analysis. All of the effects that were significant at a p < .05 level with the single voxel analysis were also significant with the 27- voxel analysis, and those that were not still remained nonsignifi- cant. The 27-voxel analysis is available as supplementary material. 3. The degrees of freedom in the behavioral analyses are re- duced due to one subjectʼs missing the RT value for Set Size 2 because of response box malfunction. 4. Note that a lower threshold was used for the younger adults to adjust for the smaller number of subjects in that group. 5. The ROIs used in the present study were selected to include BA areas for which a significant load effect (independent of age) was observed. No BA area in dorsolateral prefrontal cortex (DLPFC) made this cut. However, we did observe activation in DLPFC (e.g., in BA 10) during the task, but this activity did not significantly differ as a function of load. It is possible that the apparent lack of load effects in DLPFC is due to the relatively short duration of the maintenance period in the Sternberg task used in this study when compared with that of previous fMRI studies using the same paradigm (e.g., Rypma & DʼEsposito, 1999) or to the continuous maintenance required by the n-back task used by Mattay et al. (2006). REFERENCES Baddeley, A. D. (1986). Working memory. New York: Oxford University Press. Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. Bower (Ed.), Recent advances in learning and motivation ( Vol. 8, pp. 47–90). New York: Academic Press. Beck, A. T., & Steer, R. A. (1996). Manual for the Beck Depression Inventory. San Antonio, TX: Psychological Corporation. Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003). General multilevel linear modeling for group analysis in fMRI. Neuroimage, 20, 1052–1063. Bopp, K. L., & Verhaeghen, P. (2005). Aging and verbal memory span: A meta-analysis. 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