This study is a part of the larger Mind in Motion study (Clark et al., 2019), which focuses on understanding the neural control of mobility function in older adults. For the data set included here, participants attended two separate visits scheduled within 30 days or less of each other. During the first visit, participants underwent anthropometric measurements, cognitive screening, and mobility function assessments. They also completed a terrain walking speed task, which included four levels of difficulty: flat, low, medium, and high terrain unevenness. In the second visit, participants performed the n-back task, which had four levels of difficulty: 0-back, 1-back, 2-back, and 3-back. Additionally, fNIRS was used to assess changes in brain activity during the n-back task. Further details are provided in the following sections.

Data from 68 participants were analyzed: 24 healthy younger adults (13 females, 11 males; mean age ± SD = 22.8 ± 3.3 years) and 44 community-dwelling older adults (27 females, 17 males; mean age ± SD = 74.0 ± 5.6 years). Briefly, inclusion criteria comprised age ranges of 20–40 years for younger adults and ≥65 years for older adults. Exclusion criteria involved the presence of mild cognitive impairment [i.e., Montreal Cognitive Assessment (MoCA) score < 26; Nasreddine et al., 2005], walking disability (i.e. 400-m walk test in ≥15 min without assistance; Vestergaard et al., 2009), severe obesity [body mass index (BMI) ≥35], or the presence of serious or unstable medical conditions or historical health issues that would prevent the participant from fully engaging in the cognitive and walking assessments. In the 400-m walk, a 15-min cut-off at usual gait speed has been recommended as an objective measure to screen for and identify mobility limitations (Rolland et al., 2004). Full inclusion and exclusion criteria have been described previously (Clark et al., 2019). All participants provided written consent, and the protocol followed ethical guidelines and was approved by the Institutional Review Board at the University of Florida.

Older adults were categorized into their respective physical function groups based on their SPPB scores (Pavasini et al., 2016), with SPPB scores ≥10 indicating high physical function (n = 29) and scores < 10 indicating low physical function (n = 15). The SPPB test is an assessment of global physical function, which includes balance, strength, and gait measurements (Guralnik et al., 1994). This test involves a 4-meter usual pace walk, time to complete five unassisted chair stands, and three standing balance tasks with feet together and in semi- and full-tandem foot positions.

Four walking surface conditions (flat, low, medium, and high) were designed, each presenting distinct levels of uneven terrain (Clark et al., 2019; Downey et al., 2022). Uneven terrain was created using rigid foam disks of various heights (non-compressible, each 12.7 cm in diameter; Blockwire Manufacturing LLC, Goshen, AL, USA), which were attached to a 3.5 meter mat. The low uneven terrain consisted entirely of 1.3 cm tall disks painted in yellow. The medium uneven terrain consisted of 50% 1.3-cm tall and 50% 2.5-cm tall disks painted in orange. The high uneven terrain included three different height disks painted in red: 50% at 3.8 cm, 20% at 2.5 cm, and 30% at 1.3 cm. For the flat terrain, there were no disks on the walking surface, but green circles were painted on the mat to ensure that the visual aspect of the flat condition was similar to the other terrain conditions. Participants completed multiple practice trials for each terrain condition before testing and were then instructed to walk at a natural, comfortable pace over each terrain condition three times, with the order of conditions randomized. The time to complete the middle 3-m portion of the mat was measured with a stopwatch (Figure 1A).

To assess visuospatial working memory performance, we employed the spatial n-back task as shown in Figure 1B. Participants were seated comfortably in front of a computer monitor. During the n-back task, a blue square appeared on a computer screen in one of nine possible locations. The location of the square was refreshed every 500 ms for short inter-stimulus interval trials (“short ISI”) or 1,500 ms for long inter-stimulus interval trials (“long ISI”). Each time the square re-appeared, participants were instructed to indicate whether the current location matched the location presented n-back by pressing a designated key. For each n-back test, the location of the square was refreshed 16 times. For the 0-back test, participants were instructed to press the key only when the square appeared in the center position. For the 1-back test, participants were instructed to press the key only when the square appeared in the same position on two consecutive appearances. For the 2-back test, participants were instructed to press the key only when the square appeared in the same position as two appearances prior (regardless of the position at the most recent appearance). Likewise, the 3-back test required the participants to press the key when the square appeared in the same position as three appearances prior (regardless of the position at the two most recent appearances). If the positions did not match, participants were instructed to refrain from responding. Participants completed two runs of the n-back task. In the first run, the sequence was as follows: short ISI 0-back, long ISI 2-back, short ISI 3-back, long ISI 1-back, short ISI 1-back, short ISI 2-back, long ISI 0-back, and long ISI 3-back. Each n-back test was separated by a short rest period. The second run followed this sequence: short ISI 3-back, short ISI 1-back, long ISI 0-back, short ISI 2-back, short ISI 0-back, long ISI 2-back, long ISI 1-back, and long ISI 3-back. Each run lasted approximately 10 min, resulting in a total duration of approximately 20 min for the entire task.

For data analysis, we averaged one task from the first run and the corresponding task from the second run for each n-back level. We used custom R scripts (available at https://github.com/tfettrow/Crunch_Nback_Analysis) specifically to read and analyze n-back task data. The primary outcome, d-prime (d′), provides a net summary score of n-back performance by accounting for both successes and failures. The formula used for calculating d′ = Z_H – Z_FA (Macmillan and Creelman, 1990), where H and FA represent the “Hit” (correct) and “False Alarm” (false positive) rates, and Z signifies the Z-transformation. Hit rates denote the proportion of hits when a signal is present (hits/(hits + misses)), and False Alarm rates represent the proportion of false alarms when a signal is absent (false alarms/(false alarms + correct negative)) (Haatveit et al., 2010). When participants excel at maximizing hits (minimizing misses) and minimizing false alarms (maximizing correct rejections), their sensitivity in distinguishing between target and non-target stimuli during the task increases (Haatveit et al., 2010). Therefore, a high d-prime score indicates greater discriminability in signal/target detection, reflecting better overall performance on the n-back task (Figure 1B). We calculated d-prime with the R package Psycho (https://cran.r-project.org/web/packages/psycho/psycho.pdf). Additionally, reaction time (measured in ms) was recorded only for correct responses, reflecting the time participants took to respond to each stimulus during the task.

Baseline characteristics, including age, sex, MoCA, and 400-m assessments, underwent one-way analysis of variance for continuous data and chi-square (χ²) tests for categorical data. To investigate the impact of aging and mobility function on walking speed and n-back performance, a linear mixed model with subject-specific random intercepts was used to analyze the effects of group (younger adults, older adults with high physical function, and older adults with low physical function) as a between-subject factor, terrain condition (flat, low, medium, and high) or n-back level (0-back, 1-back, 2-back, and 3-back) as a repeated within-subject factor, and the interaction between group and terrain or n-back level on walking speed (m/s) and working memory performance (d-prime score and reaction time), respectively. Additionally, a linear mixed model was utilized to examine prefrontal ΔO₂Hb (μM) as a dependent variable across n-back levels among groups. Before conducting statistical inference, we examined linearity through Q-Q plots and histograms and confirmed the normality of residual data using the Shapiro-Wilk test. If the group by terrain interaction achieved statistical significance, pairwise post hoc tests were conducted between groups for each terrain separately or between terrains for each group separately. However, in the absence of a significant interaction, the main effects of terrain and group were assessed independently for all combinations of terrains and groups. Pairwise comparisons were adjusted using the false discovery rate (FDR) correction method. Partial Eta squared (ηp2) was obtained for effect size, with the following criteria applied: ηp2 ≥ 0.01, ηp2 ≥ 0.06, and ηp2 ≥ 0.14 represent small, medium, and large effect sizes, respectively (Lakens, 2013).

A multivariate correlation analysis was conducted to assess the relationship between the percentage change in d-prime or reaction time from 0-back to 3-back and the percentage change in walking speed from flat to high terrain within each group for each n-back task by ISI condition. N-back performance, including d-prime scores and reaction time, as well as walking speed, was converted into percentage changes (%) to account for differences in measurement scales normalize the data across participants, and allow comparison relative to a baseline (Kelly et al., 2010). This approach allows changes across modalities to be interpreted on a common scale, rather than using non-comparable raw scores. The correlation coefficient (r) was used to quantify the strength of the relationship between changes in n-back performance (d-prime and reaction time) and uneven terrain walking speed. P-values were adjusted using the FDR correction method within each group, with corrections applied separately for the two outcome measures in the short and long ISI n-back tasks. The following definitions for effect sizes were employed (Cohen, 1992): r ≥ 0.10, r ≥ 0.30, and r ≥ 0.50 represent small, medium, and large effect sizes, respectively. Additionally, to compare correlation coefficients between groups, Fisher's r-to-z transformation was applied (Silver and Dunlap, 1987). Correlation coefficients were first converted into Fisher's z-scores, and the difference between the transformed values was computed. The standard error of the difference was estimated based on the sample sizes of each group. The z-difference score was then calculated and used to determine statistical significance with a two-tailed P-value. N-back data from two younger adults, two older adults with high physical function, and one older adult with low physical function were excluded due to technical issues (e.g., missing data or negative values at one or more n-back levels). Accordingly, these five participants were also excluded from both the correlation and fNIRS analyses. All statistical analyses were conducted using JMP software (JMP^® 15.0, SAS Institute Inc., Cary, NC, USA). The significance level (α) was set at 0.05.

As shown in Table 1, SPPB scores were significantly higher in the high physical function group compared to the low physical function group (P < 0.001), as expected due to group assignment based on this score. Age did not differ between the two physical function groups, and MoCA scores did not differ across all three groups. Additionally, the low physical function group had a significantly higher BMI and took longer to complete the 400-meter walk test compared to the high physical function group (BMI: P = 0.015, 400-meter walk test: P = 0.049) and the younger group (BMI: P < 0.001, 400-meter walk test: P = 0.005). However, both BMI and 400-meter walk times in the low physical function group remained within the established inclusion criteria. Sex distribution did not differ significantly between groups (χ² = 3.59, P = 0.166), and therefore, sex was not included as a covariate in subsequent analyses.

A group × task interaction was not significant (F_{6, 264} = 0.11, P = 0.995, ηp2 = 0.01), indicating that the effect of terrain unevenness on walking speed was similar across all three groups. The main effect of terrain (with data from all pooled groups) yielded significant results (F_{3, 264} = 10.58, P < 0.001, ηp2 = 0.11), indicating a significant decrease in walking speed for low (_FDRadj. P = 0.014), medium (_FDRadj. P < 0.001), and high (_FDRadj. P < 0.001) terrains compared with flat terrain, as well as for high terrain (_FDRadj. P = 0.015) compared to low terrain. Additionally, the main effect of group (with data from all pooled terrains) yielded significant results (F_{2, 264} = 56.37, P < 0.001, ηp2 = 0.31), indicating that overall walking speed was significantly slower in the low physical function group (_FDRadj. P < 0.001) and the high physical function group (_FDRadj. P < 0.001) compared to the younger group. Additionally, the low physical function group exhibited significantly slower walking speed compared to the high physical function group (_FDRadj. P < 0.001; Figure 2). Descriptive data for terrain walking speed are available in the Supplementary Table S1.

For the long ISI (1,500 ms) n-back task, the low physical function group showed a significant positive correlation between the percentage change in d-prime from 0-back to 3-back and the percentage change in uneven terrain walking speed from flat to high terrain (r = 0.63, P = 0.021; Figure 4A), indicating a large effect size. This association remained significant after FDR correction (adjusted P = 0.032). No significant correlations were observed in the high physical function group (r = 0.11, P = 0.601) and the younger group (r = −0.02, P = 0.902). For the short ISI (500 ms) n-back task, the percentage change in d-prime was not significantly correlated with the percentage change in walking speed in any group (all P > 0.05; Figure 4B). Similarly, no significant associations were found between percentage changes in reaction time and walking speed for either ISI condition in any group (all P > 0.05; Figures 4C, D).

To compare correlation strengths across groups, Fisher's r-to-z transformation was applied for the significant long ISI correlation. The low physical function group showed a trend toward a stronger correlation compared to the high physical function group (z = 1.81, P = 0.070) and a significant stronger correlation compared to the younger group (z = 2.13, P = 0.033).

As hypothesized, the present findings confirmed that walking speed gradually decreased as terrain became more uneven. This pronounced decline is likely attributable to a more cautious and attention-demanding gait pattern due to inconsistent biomechanical and sensory feedback when walking on an uneven surface (Downey et al., 2022; Shah et al., 2025). For example, participants likely choose to alter their typical foot placement locations relative to the discs, leading to step-to-step differences in step length and width, and different position of the foot and ankle joint (e.g., dorsi/plantar flexion and in/eversion). Collectively, these contribute to reduced walking speed (Downey et al., 2022; Hwang et al., 2024; Liu et al., 2024; Shah et al., 2025). We had expected a more rapid decline in walking speed in the low physical function group as terrain unevenness increased, but this was not observed, likely due to the group starting with significantly slower walking speed on flat terrain compared to the other groups.

N-back data showed a significant decline in d-prime scores—the primary performance measure—as task difficulty increased. This decline was notably more pronounced in the older groups compared to the younger group. In the short ISI n-back task, a significant main effect of the n-back level indicates that performance worsened as the n-back level increased across all pooled groups. This confirms that the task became more challenging as n-back level increased. Although there was a non-significant interaction effect, the overall decline in performance during the short ISI n-back task was greater in the older groups compared to the younger group across all pooled n-back levels, suggesting a detrimental effect of aging on the performance. This trend was more evident in the long ISI n-back task revealing a significant interaction effect. Younger adults exhibited a gradual decline in performance beginning at the 2-back level, whereas older adults, particularly those with low physical function, experienced a more rapid deterioration. Among older adults, those with low physical function showed performance declines beginning at the 1-back level, whereas those with high physical function began to decline at the 2-back level, progressing toward the most challenging 3-back level. The decline in d-prime scores indicates a diminished working memory capacity in terms of discriminability, particularly as task difficulty increases across all groups. The younger group exhibited a stronger ability to adapt to escalating cognitive demands, as evidenced by a slower decline in n-back performance compared to the older groups, who struggled more noticeably with increasing task difficulty. This finding highlights the increased difficulty of the task for older adults and underscores age-related deficits in working memory performance, linking age-related brain changes to alterations in memory and cognition (Nyberg et al., 2012). Additionally, a significant main effect of the group for the long ISI n-back task indicates that older adults with low physical function showed a substantially greater decline across all pooled n-back levels compared to their high physical function counterparts. This suggests that deficits in mobility function and cognitive function might be affected by the same underlying neurological impairments associated with aging.

Consistent with our findings, previous studies comparing age groups have reported a similar pattern. For instance, Gajewski et al. (2018) reported a significant interaction between age groups and n-back levels (0-back vs. 2-back) using a 1,500 ms inter-stimulus interval in the n-back task. Across the combined datasets, this study found that older participants (n = 194) had a higher proportion of missed targets at the 2-back level compared to younger participants (n = 157), while no significant group differences were observed at the 0-back level. Additionally, Hepdarcan and Can (2025) used a longer 2,000 ms inter-stimulus interval in the n-back task, which included 0-, 1-, 2-, and 3-back levels, and found that younger participants (n = 30) had better d-prime score than older participants (n = 25), although no significant interaction between age groups and n-back levels was found. However, Zajac-Lamparska et al. (2024) using a randomized inter-stimulus interval ranging from 1,800 to 2,500 ms inter-stimulus interval in the n-back task, which included 1-, 2-, and 3-back levels, found that both older participants (n = 50) and younger participants (n = 60) exhibited significantly lower d-prime scores at each difficulty level, including the easiest level (i.e., 1-back), following a similar pattern. Unlike previous studies comparing younger and older adults, our study's inclusion of older adults with low physical function may have contributed to the significant interaction between group and n-back level in the 1,500-ms n-back task. Study-specific differences in n-back task types should also be considered as a potential reason for inconsistent findings.

Additionally, n-back data revealed a significant increase in reaction time with increased n-back levels in both the long and short ISI conditions, with the effect particularly pronounced under the short ISI condition across all groups. This suggests that participants had greater difficulty responding to each stimulus as task demands increased, particularly when stimuli were refreshed every 500 ms in the short ISI condition compared to 1,500 ms in the long ISI condition. In the group comparison, the older groups consistently exhibited longer reaction times across all pooled n-back levels in both the long ISI (1,500 ms) and short ISI (500 ms) n-back tasks, compared to the younger group. Furthermore, significantly longer reaction times across all pooled n-back levels were observed in the low physical function group compared to the high physical function group during the long ISI n-back task. This observation aligns with previous studies showing that older adults typically exhibit slower reaction times in n-back task compared to younger individuals (Gajewski et al., 2018; Pergher et al., 2019; Hepdarcan and Can, 2025). This well-documented finding is attributed to age-related changes in brain function, specifically impacting processing speed in working memory, leading to a slower ability to identify and respond to target stimuli (Verhaeghen and Cerella, 2002). Essentially, the older brain takes longer to process information and make decisions (Eckert et al., 2010).

As hypothesized, a novel finding in this study is the significant association, observed exclusively in the low physical function group, between slower uneven terrain walking speed and poorer n-back performance. This finding contrasts with the absence of a corresponding association in older adults with high physical function and younger adults. Particularly in the long ISI n-back task, a significant relationship was observed for older adults with a larger effect size evident in the low physical function group. Specifically, older adults with low physical function showed a significant association between the decline in walking speed from flat to high and the decline in n-back performance from 0-back 3-back. However, this relationship was not observed in older adults with high physical function or younger adults. An additional analysis examining whether the strength of the correlation differed between groups revealed that it was significantly stronger in the low physical function group compared to the younger group, and marginally stronger compared to the high physical function group. This finding suggests that declines in uneven terrain walking speed are more closely linked to declines in n-back working memory performance among older adults with low physical function. In the short ISI n-back task, declines in uneven terrain walking speed and in n-back performance were not significantly related in any of the groups. The long ISI condition likely imposes greater demands on working memory by requiring sustained rehearsal and increasing vulnerability to interference over a longer period of time (Vergauwe and Langerock, 2017). However, particularly in older groups, poorer performance in the short ISI condition suggests that the tasks may differ in factors beyond memory demands, such as perceptual or encoding challenges, or heightened attentional demands within a shorter timeframe (Buonomano et al., 2009). This may be due to the short ISI task being overly demanding, as shorter retention intervals can disrupt attentional refreshing in working memory, potentially leading to poorer performance (Camos et al., 2018), which could have weakened any observed relationship with uneven terrain walking speed.

The significant association in the long ISI n-back task performance with uneven terrain walking speed could be attributed to underlying neurophysiological impairments that affects both tasks, especially in the lower physical function group. Aging- or neurologically-related brain atrophy may play a role, as the motor and cognitive systems rely on shared neural resources, particularly in the prefrontal cortex and basal ganglia (Seidler et al., 2010). Neuroimaging studies employing cross-sectional and longitudinal approaches have shown age-related brain atrophy in cognitively normal individuals, characterized by a decline in volume and an accelerated atrophy rate across various brain structures (Scahill et al., 2003; Fujita et al., 2023). Notably, this neural loss is not equally distributed across the brain, with gray matter in the lateral prefrontal cortex being particularly susceptible to aging-related decline (Raz et al., 2004; Lemaitre et al., 2012). As aging or neurological conditions advance, the brain may prioritize either motor or cognitive tasks, leading to slower walking speed when cognitive demands increase, such as when navigating complex environments (Beurskens and Bock, 2012). One interpretation, therefore, is that brain impairments contribute to the decline in performance on both tasks. Furthermore, impairments in working memory, as assessed by the n-back task, may directly influence the use of working memory during uneven terrain walking. Walking on uneven terrain demands working memory for continuous updating, retention, attention, and adaptation, as individuals must constantly assess and adjust their movements to navigate complex environments such as uneven surfaces (Menant et al., 2014). This process involves simultaneously managing multiple pieces of information, such as the location of the discs on the uneven terrain walkway, the condition of the terrain, and the precise foot placement, to ensure stability while walking (Downey et al., 2022).

The fNIRS results did not support the hypothesis that greater task demands in the n-back task would lead to a proportional increase in prefrontal cortical activity in either the short- or long-ISI condition nor did they reveal any significant group differences. Specifically, prefrontal cortical activity remained stable as n-back levels increased in the long ISI condition, while in the short ISI condition, activity was sustained through the 2-back level but declined significantly at the more demanding 3-back level across all pooled groups. The absence of a consistent increase in prefrontal cortical activity may suggest reduced task engagement or disengagement in response to increasing task difficulty, leading to lower cognitive effort as n-back levels increase. When task demands exceed participants' mental capacity, they may disengage and allocate fewer cognitive resources, resulting in lower brain activity—a downward trend that becomes more pronounced in more challenging tasks (Boere et al., 2024). This finding aligns with a previous fMRI study (Mattay et al., 2006) which demonstrated a similar distribution of prefrontal cortical activity between younger and older adults across all n-back task levels (1-, 2-, and 3-back levels), without a proportional increase in activation in either age group. Additionally, at higher task loads (2- and 3-back levels), older adults, who performed worse than younger adults in accuracy, exhibited relatively reduced prefrontal activity. Similarly, a recent fNIRS study using an n-back task with 1-, 2-, and 3-back levels (Zhu et al., 2024) found that older adults, who performed worse than younger adults only at the 2-back for accuracy, showed a trend of decreasing prefrontal cortical activity as task difficulty increased across the n-back levels; however, younger adults exhibited significant increases in prefrontal cortical activity at the 3-back relative to the 1-back. Consistent with our findings, older adults, in particular, showed no proportional increase in brain activation as working memory demands increased likely reflecting a decline in cognitive engagement and a corresponding reduction in cognitive effort in both long and short ISI conditions. The n-back task, particularly during the short ISI condition, may impose greater attentional demands, as reflected by longer mean reaction times compared to the 500 ms stimulus refresh interval, especially at higher n-back levels across all participant groups. This may reflect increased reliance on posterior brain regions due to temporal overlap (Eriksson et al., 2015), potentially limiting the interpretability of fNIRS measurements focused exclusively on the prefrontal cortex.

Additionally, the fNIRS results did not align with the hypothesis proposed by Reuter-Lorenz and Cappell (2008), which suggests that older adults engage in over-recruitment (greater brain activation than younger adults) at lower cognitive demands to sustain performance. Moreover, our findings do not support the notion that increasing task difficulty depletes neural resources in older adults, resulting in underactivation relative to younger adults. This is consistent with a recent electroencephalogram study (Zajac-Lamparska et al., 2024) analyzing theta and alpha wave power in the frontal-midline region during performance (d-prime) on an n-back task at three difficulty levels (1-, 2-, and 3-back levels), which did not support the hypothesis of compensatory brain activity in older adults compared to younger adults. The results indicated a reduced capacity to utilize neuronal resources relevant to the task in older adults, rather than showing compensatory activity. However, it is conceivable that age-related changes in brain areas involved in the n-back task vary across the adult lifespan. A meta-analysis of fMRI studies have shown that prefrontal cortex engagement during n-back task performance remains consistent in young adults, less so in middle-aged adults, and absent in older adults, suggesting a gradual decline in prefrontal cortex engagement with aging (Yaple et al., 2019). This could reflect a shift in resource allocation, with reduced reliance on the prefrontal cortex and increased involvement of other brain regions, such as the parietal cortex, dorsal cingulate cortex, insula, and cerebellum (Yaple et al., 2019), which may support compensatory activity in older adults (Cabeza et al., 2002; Reuter-Lorenz and Cappell, 2008). Furthermore, sustained engagement under higher cognitive demands may lead to mental fatigue, particularly in regions associated with cognitive effort, such as the prefrontal cortex, during tasks that require significant working memory and attentional resources (Yan et al., 2025). Therefore, the variability in prefrontal cortical recruitment in older adults may be influenced by a trade-off between task difficulty/modality, mental fatigue, and individual cognitive capacity.

This cross-sectional study is limited in its ability to establish cause-and-effect relationships or track changes in variables over time. Consequently, longitudinal studies are needed to provide more comprehensive and definitive evidence regarding the relationship under investigation. Additionally, the relatively small sample size per group may reduce the reliability of the linear mixed model and correlation estimates and hinder the interpretation of hemodynamic responses measured by fNIRS during the n-back task, thereby limiting the generalizability of the findings. Other limitations include fNIRS's low spatial resolution and limited depth penetration, along with variability in probe placement across individuals, which affect detection of brain activity. Since we measured only prefrontal cortex activity, we cannot rule out contributions from other brain regions involved in working memory (Owen et al., 2005; Yaple et al., 2019). Furthermore, with regard to age-related compensatory mechanisms (Cabeza et al., 2018), our study design did not fully support detecting cognitive load-dependent differences in brain activity between older and younger adults. While cognitive performance is often related to brain activity, this relationship is not always straightforward and can be influenced by factors such as task modality, task complexity, individual differences, and external factors like stress, fatigue, and environmental conditions (Cabeza and Nyberg, 2000). Therefore, larger and more rigorously controlled studies are crucially required to validate age-related compensatory mechanisms.