Abstract

With increasing age, people experience more difficulties with suppressing irrelevant information, which may have a major impact on cognitive functioning. The extent of decline of inhibitory functions with age is highly variable between individuals. In this study, we used ERPs and phase locking analyses to investigate neural correlates of this variability in inhibition between individuals. Older and younger participants performed a selective attention task in which relevant and irrelevant information was presented simultaneously. The participants were split into high and low performers based on their level of inhibition inefficiency, that is, the slowing of RTs induced by information that participants were instructed to ignore. P1 peak amplitudes were larger in low performers than in high performers, indicating that low performers were less able to suppress the processing of irrelevant stimuli. Phase locking analyses were used as a measure of functional connectivity. Efficient inhibition in both age groups was related to the increased functional connectivity in the alpha band between frontal and occipito-parietal ROIs in the prestimulus interval. In addition, increased power in the alpha band in occipito-parietal ROIs was related to better inhibition both before and after stimulus onset. Phase locking in the upper beta band before and during stimulus presentation between frontal and occipito-parietal ROIs was related to a better performance in older participants only, suggesting that this is an active compensation mechanism employed to maintain adequate performance. In addition, increased top–down modulation and increased power in the alpha band appears to be a general mechanism facilitating inhibition in both age groups.

INTRODUCTION

Studies investigating how aging affects visual selective attention and inhibition have shown that the elderly have problems suppressing irrelevant information, whereas there are no evident problems in their ability to process relevant information (de Fockert, Ramchurn, van Velzen, Bergström, & Bunce, 2009; Gazzaley et al., 2008; Mager et al., 2007; Gazzaley, Cooney, Rissman, & D'Esposito, 2005; but see Wild-Wall, Falkenstein, & Hohnsbein, 2008). According to the inhibitory deficit theory (Hasher & Zacks, 1988), this deficit in inhibition may have an influence on performance in a range of attention and memory tasks (for recent confirmation, see Gazzaley, Cooney, Rissman, & D'Esposito, 2005). However, there are studies showing that the deficits in inhibition processes with age are not unitary in nature and are not found in all tasks (Kramer, Humphrey, Larish, Logan, & Strayer, 1994).

Less efficient suppression of irrelevant information in the elderly has been linked to a deficit in top–down modulation of early visual processing stages (Gazzaley et al., 2008). Top–down modulation is crucial for our ability to voluntarily focus on relevant information and ignore irrelevant information in accordance with task instructions, expectations, or goals. Top–down control is thought to be actualized through enhancement of activity in cortical areas processing relevant information and suppression of activity in cortical areas processing irrelevant information (Pinsk, Doniger, & Kastner, 2004). These mechanisms underlying visual selective attention have been related to a neural network consisting of dorsal parietal and frontal brain areas (Noudoost, Chang, Steinmetz, & Moore, 2010; Miller & D'Esposito, 2005; Corbetta & Shulman, 2002). Recent research in monkeys using implanted electrodes has corroborated these results and in addition showed that top–down control is initiated in the frontal cortex, whereas activity in parietal areas is affected in a later stage of processing (Buschman & Miller, 2007). The communication between distant regions such as frontal and parietal areas is reflected in the phase locking of their oscillatory activity (Sauseng & Klimesch, 2008; Fries, 2005).

In ERP studies, reduced top–down suppression of irrelevant information has been linked to increased amplitude of early ERP components, such as the P1 (Zanto & Gazzaley, 2009) and N170 (de Fockert et al., 2009). In a working memory task, for example, young participants showed a reduction in P1 amplitude when irrelevant images were presented during task performance compared with viewing the same images with no accompanying task. The elderly did not show this reduction, which was interpreted as evidence that this group was unable to suppress irrelevant information during task performance (Zanto & Gazzaley, 2009).

Top–down suppression of irrelevant information has also been linked to an increase in EEG alpha power in young participants (Freunberger et al., 2008). When young participants received information in advance about the location of an upcoming target, alpha power was larger in the hemisphere ipsilateral to the target than in the hemisphere contralateral to the target. This indicates that participants prepared for the upcoming stimulus by suppressing visual processing activity at irrelevant spatial locations. These findings are consistent with those of studies investigating working memory and long-term memory performance, showing that increased alpha power in task-irrelevant regions was predictive of good performance (Meeuwissen, Takashima, Fernandez, & Jensen, 2011; Haegens, Osipova, Oostenveld, & Jensen, 2010).

The pFC, implicated in inhibition of irrelevant sensory information (Aron, Robbins, & Poldrack, 2004; Knight, Staines, Swick, & Chao, 1999), is one of the areas showing the greatest atrophy with age (Raz et al., 2005). Thus, structural decline in this brain area might be responsible, at least partly, for the inhibitory deficit in the elderly. However, according to the scaffolding theory of aging (Park & Reuter-Lorenz, 2009), enhanced activity in the frontal areas is a hallmark of the brain's adaptation to a range of neural challenges that it faces during the life span. The theory posits that, despite functional deterioration, an adequate performance level in the elderly is maintained due to engagement of additional neural circuitry. The additional circuitry is suggested to primarily involve frontal brain areas; however, parietal, temporal, or occipital areas might be included, as well.

It is important to note that studies of aging often have treated the elderly as a homogeneous group, assuming the same general pattern of cognitive decline in all individuals (for exceptions, see Daffner et al., 2011; Nagel et al., 2009; Duverne, Motamedinia, & Rugg, 2008; Colcombe, Kramer, Erickson, & Scalf, 2005). However, large individual differences have been observed within the elderly population (Park & Reuter-Lorenz, 2009). In view of the need to develop interventions to slow age-related changes in performance (e.g., cognitive training methods, adaptations of work environment), it is of great importance to understand why some older individuals are able to maintain their level of cognitive function into old age while others cannot. The main aim of this study is to examine age-related changes in processing of relevant and irrelevant information using both behavioral and electrophysiological indices of performance, taking into account differences between high- and low-performing individuals.

Inhibition was measured using a selective attention task, in which relevant and irrelevant information could be presented simultaneously. Participants were instructed to press “yes” when a target letter was presented on one of the relevant positions on the screen and press “no” in all other cases. An inhibition inefficiency score was derived by assessing how RTs were affected by a target letter presented on one of the irrelevant locations on the screen compared with trials in which no target letters were presented. On the basis of previous research, we hypothesized that an individual's ability to inhibit irrelevant information would be related to the amplitude of the P1 ERP component (Gazzaley et al., 2008). In addition, we expected an increase in functional connectivity between signals measured at frontal and occipito-parietal electrode locations, as reflected in higher phase locking values (PLV) in the alpha band, in individuals with a high ability to inhibit irrelevant information compared with those who have a lower ability to inhibit irrelevant information.

METHODS

Participants

Forty-four older adults (20 men, Mage = 65.8 years, age range = 60–74 years) and 40 younger adults (20 men, Mage = 19.8 years, age range = 18–26 years) participated in this experiment. All participants were right-handed and had no history of neurological or psychiatric disorders. Older participants had a score above 26 on the Mini Mental Status Examination (Folstein, Folstein, & McHugh, 1975) and below 16 on each of the subscales of the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983). All participants had normal or corrected-to-normal visual acuity. The study adhered to the Declaration of Helsinki and was approved by the local ethics committee. Informed consent was obtained from all participants.

Stimuli and Apparatus

The task used in the current experiment was a modified version of the selective attention task used by Wijers et al. (1987). In this version of the task (Figure 1), an experimental block started with the presentation of the target letter, followed by the presentation of a cue frame, indicating which diagonal (right–up, left–up, horizontal) was relevant. Both the target letter and the cue frame were presented for 5000 msec. After the instruction, a series of 150 trials was presented in each block. In each trial, the stimuli were presented for 300 msec followed by an ISI varying randomly between 2000 and 2500 msec. A fixation cross in the middle of the screen remained visible throughout a block of trials.

Figure 1. 

Schematic overview of different task conditions.

Figure 1. 

Schematic overview of different task conditions.

Participants were required to pay attention to the information presented on the relevant diagonal and to ignore the information presented on the irrelevant diagonals. For half of the participants, a right-hand index finger response was required if a target letter appeared on a relevant diagonal position (relevant target) and a right-hand middle finger response was required in all other conditions (i.e., target letter on irrelevant diagonal positions [irrelevant target] or no target letter presented on the relevant diagonal [nontarget]). For the other half of the participants, index and middle finger responses were reversed. Relevant target trials made up 25% of the total number of trials. There were never two target letters present in one trial. In addition, display load was varied; the stimulus display contained two, four, or six letters. On diagonals where no letters were displayed, masks were presented to keep the amount of visual input similar over conditions. The display load manipulation will not be discussed in the current paper. The outcome measure of interest in this task was the ability to inhibit irrelevant information on the screen. This was measured by the difference in median RT between irrelevant target trials and nontarget trials, averaged over the other task variations such as display load and whether or not letters were presented on the relevant diagonal. This difference score is referred to as the inhibition inefficiency score.

Stimuli were presented on a Pentium IV PC, equipped with a 17-in. monitor. Stimulus generation and response collection were controlled using E-prime 1.2 (Psychology Software Tools, Inc., Sharpsburgh, PA). All stimuli were white on a black screen in Arial font, size 18. Stimulus letters were randomly chosen from the alphabet, excluding the letters g, i, o, q, u, x, and y. The visual angle from the center of fixation to each of the letters was 2.3°, with a viewing distance of 75 cm. In total, there were six blocks of 150 stimuli. Between every two blocks, there was a 2-min break to avoid the effects of mental fatigue due to prolonged task performance.

Procedure

The experiment consisted of two sessions carried out on separate days. In the first session, participants performed a series of neuropsychological tests, which were used to check whether differences between groups in the inhibition task were related to the speed of information processing or intelligence. The test battery included the Dutch Adult Reading Test (Schmandt, Lindeboom, & Harskamp, 1992), the WAIS III digit span, digit symbol and matrix reasoning tests (Uterwijk, 2001), and a simple RT test (see Table 1). Visual acuity was measured using reading charts at a distance of 75 cm; a score of 1 reflects normal visual acuity. In addition, participants practiced the selective attention task in three blocks of 50 trials. During training, feedback on performance levels was given after each block. In the second session, participants performed the selective attention task while EEG was measured.

Table 1. 

Neuropsychological, Demographic, and Experimental Variables


Young HP
Young LP
Old HP
Old LP
Sex (male/female) 11/8 9/10 9/11 10/10 
Age (years) 19.6 (1.2) 20.2 (2.2) 65.6 (4.3) 65.6 (3.9) 
Visual acuity* 1.1 (0.1) 1.0 (0.2) 0.8 (0.2) 0.8 (0.1) 
Dutch Reading test IQ* 101 (4) 102 (6) 107 (8) 110 (12) 
WAIS Matrices IQ 110 (8) 111 (11) 108 (11) 107 (11) 
Digit Symbol* 87 (11) 85 (14) 67 (11) 62 (15) 
RT test (msec)* 219 (20) 220 (22) 236 (18) 240 (34) 
RT nontarget trials (msec)* 399 (56) 416 (61) 567 (71) 561 (70) 
Inhibition inefficiency (msec)**,*** 6.4 (3.6) 17.3 (3) 7.5 (5.1) 24.7 (6.6) 
P1 amplitude (μV)** 3.2 (1.5) 3.7 (1.6) 3 (1.9) 5.4 (3.4) 

Young HP
Young LP
Old HP
Old LP
Sex (male/female) 11/8 9/10 9/11 10/10 
Age (years) 19.6 (1.2) 20.2 (2.2) 65.6 (4.3) 65.6 (3.9) 
Visual acuity* 1.1 (0.1) 1.0 (0.2) 0.8 (0.2) 0.8 (0.1) 
Dutch Reading test IQ* 101 (4) 102 (6) 107 (8) 110 (12) 
WAIS Matrices IQ 110 (8) 111 (11) 108 (11) 107 (11) 
Digit Symbol* 87 (11) 85 (14) 67 (11) 62 (15) 
RT test (msec)* 219 (20) 220 (22) 236 (18) 240 (34) 
RT nontarget trials (msec)* 399 (56) 416 (61) 567 (71) 561 (70) 
Inhibition inefficiency (msec)**,*** 6.4 (3.6) 17.3 (3) 7.5 (5.1) 24.7 (6.6) 
P1 amplitude (μV)** 3.2 (1.5) 3.7 (1.6) 3 (1.9) 5.4 (3.4) 

Numbers represent mean (SD).

*Significant difference between old and young participants (p < .005).

**Significant difference between high and low performers (p < .01).

***Significant interaction between age and performance level (p < .005).

Performance Level Split

To examine the relation between individual differences in inhibition and brain activity, both the older and younger groups were split into high and low performers. The split was based on the inhibition inefficiency score; the difference in median RTs between trials in which a target was presented on an irrelevant location and trials in which no target letters were presented. Participants with an inhibition inefficiency score smaller than the median in their age group were considered to be high performers, and those with a score above the median are the low performers. Participants with an inhibition inefficiency score higher or lower than 2 standard deviations from the group mean were excluded from the analysis to prevent artificial inflation of correlations. This led to the removal of four outliers, two younger participants and two older participants.

EEG Measurement and Data Analysis

EEG was recorded using 64 tin electrodes attached to a cap (ElectroCap International, Inc., Eaton, OH). The electrodes were placed at sites specified by the international 10–10 system (except for F1, F2, CP1, CP2, FT7, and FT8). A REFA 8–72 amplifier (Twente Medical Systems, Enschede, The Netherlands) was used to sample the signal at 500 Hz, with a low-pass filter of 135 Hz (48 dB/oct). The EOG was recorded bilaterally from electrodes placed at the outer canthi of both eyes and above and below the left eye. Data acquisition was performed using Brain Vision Recorder (version 1.03, BrainProducts GmbH, Munich, Germany).

Preprocessing of data was executed using Brain Vision Analyzer (version 1.05, BrainProducts GmbH, Munich, Germany). Trials with incorrect responses were excluded from the analysis. Data were down-sampled to 250 Hz, rereferenced to the average of the mastoid electrodes and filtered with a high-pass filter of 0.16 Hz. For time–frequency analysis, a low-pass filter of 55 Hz (48 dB/oct) was used. A low-pass filter of 40 Hz (24 dB/oct) was used for the ERP analysis. Ocular correction was applied using the algorithm of Gratton, Coles, and Donchin (1983). No horizontal eye movements were observed in relevant segments. Segments for ERP analysis ranged from −200 to 1000 msec around stimulus onset, and baseline correction was applied to the prestimulus interval from −200 to 0 msec. Segments for the phase locking analysis ranged from 1 sec prestimulus to 1 sec poststimulus. For the ERP analysis, trials were considered artifacts when the difference between the highest and the lowest voltage within a segment at PO7 or PO8 was more than 200 μV and when there was a difference larger than 50 μV between subsequent data points. For the time–frequency analysis, trials were considered artifacts when there was a difference larger than 75 μV between subsequent data points on one of the channels involved in the analysis. The number of trials considered as artifacts did not differ significantly between high- and low-performing participants. After the exclusion of artifacts and incorrect trials, an average of 817 trials remained for subsequent analysis.

To calculate PLVs between different frontal and occipito-parietal ROIs, the Fieldtrip toolbox for EEG/MEG analysis was used (Oostenveld, Fries, Maris, & Schoffelen, 2011). The choice for particular ROIs was partly based on previous research (Zanto, Rubens, Bollinger, & Gazzaley, 2010) and extended with frontal ROIs, as it has been suggested that inhibition and selective attention involves a widespread fronto-parietal network (Pinsk et al., 2004). Except for the central frontal ROI (average of three electrodes: AFz, Fz, and FCz), all ROIs were averages of the signals measured at five electrodes: FC1, FC3, FC5, C3, F3 (left frontal), FC2, FC4, FC6, F4, C4 (right frontal), P3, P5, P7, PO3, PO7 (left occipito-parietal), POz, Oz, O1, O2, Iz (central occipito-parietal), and P4, P6, P8, PO4, PO8 (right occipito-parietal).

Time–frequency analyses and phase computations were performed using Morlet wavelets (family ratio: fof = 7) in three frequency bands: the alpha band (8–12 Hz), the lower beta band (13–20 Hz), and the upper beta band (21–30 Hz). These frequency bands were based on previous research (Sauseng & Klimesch, 2008), indicating that the long-range connectivity in alpha and beta waves plays a role in inhibition and attention processes. PLVs were computed by measuring the intertrial variability of the difference in phase between two ROIs at each time–frequency point (Lachaux, Rodriguez, Martinerie, & Varela, 1999). This procedure results in a measure between 0 and 1, where 0 represents a random phase difference and 1 represents a constant phase difference. In addition to PLV, the power values, as computed by the Morlet wavelets, were examined to check for artificial differences in PLV, induced by power differences. Averages of the phase locking and power values were computed in four 200-msec intervals, ranging from 400 msec before stimulus onset to 400 msec after stimulus onset. Because of the Morlet wavelet procedure, the PLV and power values at each time point are estimates based on a range of time points encompassing seven cycles of the frequency of interest. Data of five participants were removed from the PLV and power analysis because of bad data quality for one of the electrodes within one of the ROIs.

Data Reduction and Statistical Analyses

On the basis of the findings in previous studies, where the P1 and N1 were linked to inhibition of irrelevant information, we specifically looked at these ERP components (Gazzaley, Cooney, McEvoy, Knight, & D'Esposito, 2005). P1 peak amplitude was quantified as the most positive value between 50 and 150 msec after stimulus onset at PO7 and PO8 and the N1 as the most negative value between 120 and 220 msec at PO7 and PO8. Data from one participant were excluded from the ERP analysis because of bad signal quality at electrode PO8. Analysis of behavioral data, ERP peak amplitudes, and latencies was performed using mixed effects ANOVAs. Within-subject factors were task condition and electrode (only for the analysis of ERPs); between-subject factors were age group, performance level (high/low), and the interaction between age and performance level. To check whether the behavioral results were confounded by effects of processing speed, two different measures of processing speed, RT in the RT task and RT in nontarget trials, were used as covariates in two separate ANCOVAs. Planned comparisons and post hoc tests were performed using t test. For the t tests, corrections for unequal variances were applied when required. All correlation analyses (between phase locking, P1 amplitude and inhibition inefficiency scores) were performed using one-sided Spearman rank correlations. The Spearman rank correlation coefficient was used instead of the Pearson correlation coefficient to cope with deviations from normality and linearity and to reduce effects of possible outliers.

To assess whether correlations were significantly different in the older and younger groups, a Monte Carlo resampling procedure was used. Participants were randomly assigned to one of two groups. Within each group, the correlation was computed, and subsequently, the difference between the correlations in the groups was stored. This procedure was repeated 5000 times to generate a null distribution. Only when the difference in the correlations between the older and younger groups was larger than the largest 5% of this null distribution, we concluded that the correlations were significantly different between the age groups and we calculated separate Spearman rank correlations. The effect of age on PLVs and power values was computed using the Mann–Whitney U test, a nonparametric alternative for the t test. This test was used instead of the t test to reduce the effects of outliers and to cope with nonnormal data. The standard effect size of the Mann–Whitney U test (r) was computed by dividing the z value by the square root of the number of participants in the analysis. The significance level was set at α = .05.

For the analysis of PLVs, each time–frequency bin contained 15 comparisons (electrode pairs); for the analysis of power values, there were six comparisons (electrodes) in each bin. Monte Carlo resampling procedures were used to evaluate the probability of the observed number of significant effects in each time–frequency bin (similar to the procedure used by Hanslmayr et al., 2007). The scores of participants were randomly exchanged, and correlations or group differences within a time–frequency bin were computed. The number of significant (p < .05) effects was stored. This procedure was repeated 5000 times to generate a null distribution. When the actual number of significant results was larger than the cutoff number corresponding to the largest 5% of this null distribution, we concluded that the number of significant differences was larger than could be expected based on chance. In the figures, we indicated whether results in a given time–frequency bin were found significant according to this procedure by adding an asterisk. Only results that were significant according to this procedure were presented in the Results section.

RESULTS

Behavioral Data

The inhibition inefficiency score measures the extent to which participants were affected by irrelevant information. A median split on the inhibition inefficiency score was used to categorize participants into high and low performers (see Methods). As accuracy levels for both conditions were almost at ceiling level (>99% accuracy), the split only incorporated differences in RTs. In all groups, the inhibition inefficiency score was significantly larger than zero, t(18/19) = 6.6–25.1, p < .0005. The inhibition inefficiency score was larger for older than for younger participants, F(1, 74) = 15.1, p < .0005. However, this difference depended on performance level, F(1, 74) = 8.4, p = .005, that is, old high performers (M = 7.5, SD = 5.1) and young high performers (M = 6.4, SD = 3.6) did not differ, whereas old low performers had a significantly higher inhibition inefficiency score (M = 24.7, SD = 6.6) than young low performers (M = 17.3, SD = 3), t(26.9) = −4.5, p < .0005.

ANCOVAs were used to control for the effects of processing speed on the inhibition inefficiency score. Both RTs as measured with the simple RT test and RTs in nontarget trials were entered as covariates of no interest. Effects of performance level and the age by performance interaction did not change. The effects of age group remained significant when adding the RT in nontarget trials, F(1, 73) = 6.9, p = .01, or the RT in the RT test, F(1, 73) = 9.7, p < .003, as covariates.

No differences were observed between high and low performers in average RTs for any of the task conditions, F(1, 74) = 0.39, p = ns, nor did high and low performers differ on scores for the simple RT test, F(1, 73) = −0.21, p = ns, Dutch Reading test IQ, F(1, 74) = −0.99, p = ns, or WAIS matrices IQ, F(1, 74) = 0.01, p = ns. In addition, none of the neuropsychological tests showed an age by performance interaction. Younger participants scored significantly better than older participants on tests for speed of information processing and visual acuity (Table 1). No age differences in IQ score were found, indexed by the WAIS matrices test, whereas the Dutch Reading test, a test of crystallized intelligence, showed significantly better performance for older than for younger participants.

ERP Data

The results for the P1 peak matched the pattern we found for the behavioral data; in the low-performing older group, P1 amplitude (M = 5.4, SD = 3.4) was larger than in the other groups [performance level, F(1, 73) = 8.7, p = .004]. In addition, low-performing younger participants showed a slightly larger P1 (M = 3.7, SD = 1.6) than both young (M = 3.2, SD = 1.5) and old high-performing participants (M = 3, SD = 1.9; see Figure 2).

Figure 2. 

ERP waveforms at electrodes PO7 and PO8 illustrating the P1 component. The waveform is averaged over all task conditions and superimposed for age and performance level.

Figure 2. 

ERP waveforms at electrodes PO7 and PO8 illustrating the P1 component. The waveform is averaged over all task conditions and superimposed for age and performance level.

P1 amplitude differences between high and low performers were more pronounced at the right hemisphere electrode (PO8) than at the left hemisphere electrode [PO7; performance level × electrode: F(1, 73) = 5.4, p = .022]. Correlation analyses revealed that P1 amplitude correlated significantly with the inhibition inefficiency score at PO8 (r = .342, p = .001), that is, participants with a larger inhibition inefficiency score had larger P1 amplitudes (see Figure 3). However, this correlation was not present at PO7. Monte Carlo resampling procedures revealed that correlations between P1 amplitude and inhibition inefficiency score did not differ significantly between the older and younger groups. There was no effect of trial type (target/nontarget/irrelevant target) on P1 or N1 amplitude. No effects of age or performance level or condition on N1 amplitude or P1 and N1 latency were observed.

Figure 3. 

Correlation between P1 amplitude at electrode PO8 and inhibition inefficiency. Because the nonparametric Spearman rank correlation was used, rank scores are shown.

Figure 3. 

Correlation between P1 amplitude at electrode PO8 and inhibition inefficiency. Because the nonparametric Spearman rank correlation was used, rank scores are shown.

Phase Locking

Connections between the different ROIs were examined in the phase locking analysis to identify correlations with the inhibition inefficiency score and P1 amplitude as well as differences between age groups. In Figures 4 and 5, the significant results are summarized for both the alpha and beta frequency bands. When the number of significant results for a specific time–frequency bin exceeds the number expected by chance, this is indicated by an asterisk in the figure, and these results are described in the Results section.

Figure 4. 

(A) Significant correlations (r; p < .05) between PLVs in different ROIs and the inhibition inefficiency (pooled over the two age groups). Line color indicates strength and sign of the correlation. Note that in the upper beta band, correlations are presented separately for the younger and older groups because interactions between age group and inhibition inefficiency were observed in this frequency band. An asterisk (*) indicates that the number of significant correlations or group differences in that time–frequency bin exceeds the number expected by chance (p < .05). The lower right head model shows the abbreviated names associated with the different ROIs: left occipito-parietal (L-OP), central occipito-parietal (C-OP), right occipito-parietal (R-OP), left frontal (LF), central frontal (CF), and right frontal (RF). (B) Significant differences between age groups (p < .05) in PLVs between different ROIs. Line color indicates effect size (r) and sign of the difference.

Figure 4. 

(A) Significant correlations (r; p < .05) between PLVs in different ROIs and the inhibition inefficiency (pooled over the two age groups). Line color indicates strength and sign of the correlation. Note that in the upper beta band, correlations are presented separately for the younger and older groups because interactions between age group and inhibition inefficiency were observed in this frequency band. An asterisk (*) indicates that the number of significant correlations or group differences in that time–frequency bin exceeds the number expected by chance (p < .05). The lower right head model shows the abbreviated names associated with the different ROIs: left occipito-parietal (L-OP), central occipito-parietal (C-OP), right occipito-parietal (R-OP), left frontal (LF), central frontal (CF), and right frontal (RF). (B) Significant differences between age groups (p < .05) in PLVs between different ROIs. Line color indicates effect size (r) and sign of the difference.

Figure 5. 

Significant correlations (r; p < .05) between PLVs in different ROIs and the P1 amplitude in the (A) younger and (B) older groups. Note that correlations are presented separately for the younger and older groups because interactions between age group and P1 amplitude were observed in all frequency bands. Line color indicates effect size (r) and sign of the difference. An asterisk (*) indicates that the number of significant correlations in that time–frequency bin exceeds the number expected by chance (p < .05).

Figure 5. 

Significant correlations (r; p < .05) between PLVs in different ROIs and the P1 amplitude in the (A) younger and (B) older groups. Note that correlations are presented separately for the younger and older groups because interactions between age group and P1 amplitude were observed in all frequency bands. Line color indicates effect size (r) and sign of the difference. An asterisk (*) indicates that the number of significant correlations in that time–frequency bin exceeds the number expected by chance (p < .05).

Phase locking in the alpha band (8–12 Hz) between frontal and occipito-parietal ROIs consistently correlated negatively with the inhibition inefficiency score in the first interval before stimulus onset (i.e., −400 to −200 msec). Participants with a higher ability to inhibit irrelevant information (lower inhibition inefficiency score) showed more phase locking between frontal and occipito-parietal ROIs (see Figure 4A). Correlations did not differ significantly between the older and younger groups. Age effects in the alpha band were mainly limited to poststimulus stages of stimulus processing; older participants showed more phase locking in the alpha band between frontal and occipito-parietal ROIs than young participants between 200 and 400 msec after stimulus presentation.

Effects of age on PLV in the lower beta band (13–20 Hz) were present in all time intervals, although the differences between age groups were only significant after correction for multiple comparisons in the intervals after stimulus onset. The older participants showed larger PLVs between frontal and occipito-parietal ROIs than young participants, while phase locking was reduced between frontal ROIs for the older compared with young participants (see Figure 4B). Correlations between P1 amplitude and phase locking in the lower beta band differed across age groups. In older participants, the P1 amplitude decreased with increased phase locking between frontal and occipito-parietal ROIs (over all intervals), whereas in young participants, the effects did not reach the level of corrected significance.

In the higher beta band (21–30 Hz), the Monte Carlo resampling procedure showed that the correlation between PLVs and inhibition inefficiency scores was significantly different in the older and younger groups. Although no correlations were observed between the inhibition inefficiency score and phase locking in the young group, higher PLVs between frontal and occipito-parietal ROIs in the older group were correlated with more efficient inhibition over all time intervals. Also the correlations between the P1 amplitude and phase locking in the upper beta band were significantly different between age groups. Younger participants showed no significant correlations, whereas the older participants showed a smaller P1 amplitude with increased frontal to occipito-parietal and occipito-parietal to occipito-parietal phase locking (over all intervals). These correlations were stronger than those in the lower beta band.

Because phase locking in both the alpha and the upper beta band correlated with inhibition inefficiency scores in older participants, we additionally computed the correlation between the average frontal to occipito-parietal phase locking in the alpha band (−400 to −200 msec) and phase locking in the upper beta band (see Figure 6). There was a significant interaction between young and older participants. In younger participants, there was no correlation between alpha and upper beta phase locking. Older participants with higher alpha phase locking also showed increased upper beta phase locking; this effect was mainly present between frontal and occipito-parietal ROIs, but also between occipito-parietal ROIs and between frontal ROIs (over all intervals).

Figure 6. 

Significant correlations (r; p < .05) between the average fronto-parietal PLV in the alpha band (−400 to −200 msec) and PLVs in the upper beta band, separately for the older and younger groups. Note that correlations are presented separately for the younger and older groups because interactions between age group and average fronto-parietal PLV in the alpha band were observed. Line color indicates effect size (r) and sign of the difference. An asterisk (*) indicates that the number of significant correlations in that time–frequency bin exceeds the number expected by chance (p < .05).

Figure 6. 

Significant correlations (r; p < .05) between the average fronto-parietal PLV in the alpha band (−400 to −200 msec) and PLVs in the upper beta band, separately for the older and younger groups. Note that correlations are presented separately for the younger and older groups because interactions between age group and average fronto-parietal PLV in the alpha band were observed. Line color indicates effect size (r) and sign of the difference. An asterisk (*) indicates that the number of significant correlations in that time–frequency bin exceeds the number expected by chance (p < .05).

Additional analyses of differences in absolute power levels revealed that older participants had less power in the alpha band in the right and central occipito-parietal ROIs than young participants over all intervals (significant between 0 and 400 msec; see Figure 7B). Older participants had increased power in left and right frontal and left occipito-parietal ROIs in the lower beta band. Moreover, in the higher beta band, older participants had more power than young participants in all frontal ROIs and in the left occipito-parietal ROI in pre- and poststimulus intervals.

Figure 7. 

(A) Significant correlations (r; p < .05) between power values in different ROIs and the inhibition inefficiency (pooled over the two age groups). The color of the circle indicates strength and sign of the correlation. (B) Significant differences between age groups (p < .05) in power values between different ROIs. The color of the circle indicates effect size (r) and sign of the difference. An asterisk (*) indicates that the number of significant correlations or group differences in that time–frequency bin exceeds the number expected by chance (p < .05).

Figure 7. 

(A) Significant correlations (r; p < .05) between power values in different ROIs and the inhibition inefficiency (pooled over the two age groups). The color of the circle indicates strength and sign of the correlation. (B) Significant differences between age groups (p < .05) in power values between different ROIs. The color of the circle indicates effect size (r) and sign of the difference. An asterisk (*) indicates that the number of significant correlations or group differences in that time–frequency bin exceeds the number expected by chance (p < .05).

In both younger and older participants, power in the alpha band at occipito-parietal ROIs correlated with the inhibition inefficiency score. Participants with more power in the alpha band were better at inhibiting the irrelevant information on the screen (see Figure 7A). Correlations between power and inhibition inefficiency were not significantly different for the two age groups. There were no significant correlations between power and P1 amplitude.

DISCUSSION

The main aim of this study was to examine age-related changes in inhibition of irrelevant information, taking into account differences between high- and low-performing individuals. We used a combination of behavioral, ERP, power, and functional connectivity measures to reveal neural correlates of efficient inhibition in younger and older participants. More specifically, we used P1 and N1 ERP component amplitudes to investigate whether inhibition inefficiency was related to insufficient suppression of irrelevant visual information in early processing stages. Phase locking analyses between frontal and occipito-parietal ROIs in the alpha and beta bands were used to test whether inhibition inefficiency was related to decreased connectivity between these groups of electrodes. In addition, we tested whether power in the alpha and beta bands in frontal and occipito-parietal ROIs was related to inhibition inefficiency.

The behavioral data demonstrated that, although participants were instructed to attend only to the relevant diagonal, both younger and older participants were affected by the information presented at irrelevant spatial positions. All participants responded more slowly if an irrelevant target was presented compared with the condition in which only nontargets were presented. Older adults seemed to be more affected by irrelevant information than younger adults as reflected by a larger inhibition inefficiency score. However, this apparent decline with age was carried exclusively by the low-performing older subgroup. These results clearly indicate that aging does not necessarily lead to a decline in inhibition on a behavioral level; the high-performing older performed as well as high-performing young participants. These results did not change when measures of processing speed were entered as covariates in the analysis, indicating that this is in fact a specific effect of efficient inhibition. The large differences in the decline of inhibitory function between high- and low-performing older participants underline the importance of looking at individual differences when studying aging.

Mean P1 amplitude in the different groups matched the pattern of results in the inhibition inefficiency scores; old low performers had the most pronounced P1, whereas young low performers had a larger P1 than young high performers. The P1 reflects the early influence of top–down processes on sensory processing based on global stimulus features (Klimesch, 2011; Klimesch, Sauseng, & Hanslmayr, 2007). If a stimulus appears at a spatial location the participant is not attending to, P1 amplitude was found to be smaller than when the same stimulus appears at an attended location (Mangun & Hillyard, 1991). P1 is, however, related not only to attention to relevant stimuli but also to suppression of irrelevant stimuli. In a working memory task, a stimulus elicited a smaller P1 if that stimulus was irrelevant according to the task instructions compared with the same stimulus presented without relevance instruction. This was interpreted as evidence for a reflection of inhibition (Gazzaley et al., 2008). In the current study, we found that participants who were more affected by a target on the irrelevant diagonal had a larger P1. Top–down modulation enables the selection of relevant stimuli and the suppression of irrelevant stimuli through the regulation of neuronal excitability (Gazzaley & Nobre, 2012; Kastner & Ungerleider, 2000). In this study, participants knew before stimulus onset where irrelevant information would appear on the screen. Therefore, they could use this information to apply top–down modulation to the areas processing this information. This could lead to a decrease in the neural excitability of these areas, which in turn could cause the reduction in P1. Therefore, we interpret the increased P1 for low-performing participants as reflecting a deficit in inhibition of the information presented at irrelevant diagonals. An alternative explanation might be that the differences in P1 amplitude are related to deficits in lower-level visual processing, but because we found no differences in visual acuity between high- and low-performing participants, this explanation seems less likely. We found no differences in P1 amplitude between the various trial types, which is in accordance with the suggestion that P1 reflects early categorization, which is based on global stimulus features (Klimesch, 2011) and confirms that the effect we found is indeed related to top–down modulation initiated before stimulus onset. No relation was found between the inhibition inefficiency score and N1 latency or amplitude. The N1 component has been related to the operation of discrimination processes, as it is larger in choice RT tasks than in simple RT tasks, independent of task difficulty (Vogel & Luck, 2000). This shows that inhibition inefficiency is specifically related to inhibition of information at irrelevant spatial locations as reflected in the P1 and not to a general increase in the amplitude of ERP components.

Functional communication between brain areas implicated in the neural network underlying top–down modulation was examined by computing PLVs in the alpha and beta bands (Sauseng & Klimesch, 2008). We found that specifically in the interval from 400 to 200 msec before stimulus onset, phase locking between frontal and occipito-parietal ROIs in the alpha band was increased for those participants with a lower inhibition inefficiency score, that is, in those who were less distracted by irrelevant target letters. We also found that an increase in alpha power in occipito-parietal ROIs was related to decreased inhibition inefficiency in both younger and older participants. Oscillations in the alpha band have been consistently related to inhibition (Jensen & Mazaheri, 2010; Klimesch et al., 2007). In addition, alpha oscillations are primarily related to top–down communication (Klimesch et al., 2007; Von Stein, Chiang, & König, 2000). In line with these findings, the relation between prestimulus phase locking and processing of irrelevant information after stimulus onset might reflect the influence of top–down control via alpha phase locking, modulating cortical excitability in specific cortical areas. On the basis of task instructions, participants know the position of irrelevant information beforehand; this information can be used to suppress the excitability of areas processing (ir)relevant stimuli. As the observed increase in phase locking was present in the prestimulus interval, an interval during which participants prepared for the upcoming stimulus, it is plausible that the increase in functional communication between frontal and occipito-parietal ROIs indeed represents top–down control as opposed to bottom–up signaling. Note, however, that phase locking is a measure of functional connectivity; therefore, it provides no information about the direction of connectivity by itself. Together, these results show that alpha power is indeed associated with increased inhibition and that top–down modulation of alpha activity on occipito-parietal electrodes before stimulus onset is associated with efficient inhibition of irrelevant information in both young and old.

It has been debated whether increased activity in frontal areas in older patients is beneficial to task performance or is a sign of decline in functional specificity (i.e., dedifferentiation). Some authors claim that patterns of brain activity similar to young participants predict optimal performance (Nagel et al., 2009; Li & Sikström, 2002), whereas others argue that additional activity in frontal areas contributes to task performance in older participants (Davis, Dennis, Daselaar, Fleck, & Cabeza, 2008; Reuter-Lorenz & Cappell, 2008; Cabeza, 2002). In our data, we found differences in power values between older and younger participants, which were not related to any of the behavioral measures. Older participants had more power in frontal ROIs than younger participants, mainly in the higher beta band and to a lesser degree also in the lower beta band. In addition older participants had higher PLVs between frontal and occipito-parietal ROIs than younger participants across alpha and beta frequency bands, this effect was most clear in the interval from 200 to 400 msec after stimulus onset. On the other hand, we found reduced alpha power in the central occipito-parietal ROI in older compared with younger participants over all intervals. Therefore, we hypothesize that the (higher) beta power increases in frontal ROIs and the increase in phase locking, most notably in the intervals after stimulus onset in older patients, are used to counteract the decline in alpha power in central occipito-parietal ROIs.

We found that older participants who performed well showed more phase locking between frontal and occipito-parietal ROIs in the higher beta band than low-performing older participants over all time intervals. Younger participants showed no correlation between performance and phase locking in this frequency band. Although knowledge about the functional interpretation of beta band oscillations is scarce compared with the alpha band, available evidence links phase locking in the beta band to modulation of attention (Wróbel, Ghazaryan, Bekisz, Bogdan, & Kamiński, 2007; Gross et al., 2004; Wróbel, 2000). Although phase locking in the alpha band was specifically related to inhibition of the information on irrelevant locations, the higher beta band communication between frontal and occipito-parietal ROIs might be related to the attention attributed to items on the relevant diagonal. It is known that the functional specificity of visual processing areas decreases with age (Park et al., 2004). As a result, increased top–down control of areas processing relevant stimuli might be required for good performance. In addition, the increased phase locking in the beta band might be used to counteract the effects of reduced alpha power in older patients in the central occipito-parietal ROIs. Although the usual approach for detecting compensation mechanisms is to assess activation levels in particular brain areas (Nagel et al., 2009; Davis et al., 2008), our data suggest that it is an increase in communication between areas, specifically in the higher beta band, that is related to effective inhibition in the old. Considering the specificity of this effect for the older group and the strong relation with performance efficiency, we suggest that increased beta band phase locking might represent a neural compensation mechanism employed by high-performing older patients. This interpretation is supported by analyses showing that older participants with more phase locking in the upper beta band tended to have a smaller P1 and more prestimulus alpha phase locking. Both of these factors were found to indicate a low inhibition inefficiency (good performance) in the current study.

There are several models that divide attention into subsystems of stimulus selection and conflict resolution. In the current study, we focused on the selection of relevant and the exclusion of irrelevant information, which corresponds to the orienting system (Raz & Buhle, 2006; Posner & Petersen, 1990) or the perceptual selection stage (Lavie, 2005; Lavie, Hirst, De Fockert, & Viding, 2004). Both models suggest that the orienting or perceptual selection stage is automatic and does not require active cognitive control. This assertion is contradicted by a recent study, which has shown that perceptual selection is affected by cognitive load and that distracters can be excluded in the perceptual stage to prevent them from further interference (Caparos & Linnell, 2010). This pattern appears to be reflected in our data as well; the conflict measured with the inhibition inefficiency score between the correct letter (response: yes) and the incorrect location (response: no) was reduced in high-performing participants by employing perceptual selection (as reflected in increased alpha phase locking and decreased P1 amplitude), even before stimulus onset.

In conclusion, we have shown that, on a behavioral level, only a subgroup of older patients showed a deficit in inhibition whereas others seem to perform comparably with young individuals. This age-related decline in inhibition seems to be related to a deficit in suppression of irrelevant information in the early stages of visual processing, as reflected in the relation between P1 amplitude and the inhibition inefficiency score. Increased top–down modulation from frontal to occipito-parietal ROIs in the alpha band and increased alpha power in occipito-parietal ROIs appear to be an underlying mechanism facilitating performance in both young and older participants. The relation between phase locking in the higher beta band and inhibition inefficiency suggests that increases in connectivity in high-performing older patients are not passive age-related changes but active compensation mechanism employed to maintain adequate performance. Successful aging seems to be associated with maintenance of efficient information processing capabilities, extended by effective compensation mechanisms resulting in performance levels comparable with young individuals.

Acknowledgments

We thank Michael D. Rugg, Jacob Jolij, Luca Nanetti, and four anonymous reviewers for their helpful suggestions on earlier drafts of this paper.

Reprint requests should be sent to Linda Geerligs, Department of Experimental Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands, or via e-mail: l.geerligs@rug.nl.

REFERENCES

REFERENCES
Aron
,
A. R.
,
Robbins
,
T. W.
, &
Poldrack
,
R. A.
(
2004
).
Inhibition and the right inferior frontal cortex.
Trends in Cognitive Sciences
,
8
,
170
177
.
Buschman
,
T. J.
, &
Miller
,
E. K.
(
2007
).
Top–down versus bottom–up control of attention in the prefrontal and posterior parietal cortices.
Science
,
315
,
1860
1864
.
Cabeza
,
R.
(
2002
).
Hemispheric asymmetry reduction in older adults: The HAROLD model.
Psychology and Aging
,
17
,
85
100
.
Caparos
,
S.
, &
Linnell
,
K. J.
(
2010
).
The spatial focus of attention is controlled at perceptual and cognitive levels.
Journal of Experimental Psychology: Human Perception and Performance
,
36
,
1080
1107
.
Colcombe
,
S. J.
,
Kramer
,
A. F.
,
Erickson
,
K. I.
, &
Scalf
,
P.
(
2005
).
The implications of cortical recruitment and brain morphology for individual differences in inhibitory function in aging humans.
Psychology & Aging
,
20
,
363
375
.
Corbetta
,
M.
, &
Shulman
,
G. L.
(
2002
).
Control of goal-directed and stimulus-driven attention in the brain.
Nature Reviews Neuroscience
,
3
,
201
215
.
Daffner
,
K. R.
,
Sun
,
X.
,
Tarbi
,
E. C.
,
Rentz
,
D. M.
,
Holcomb
,
P. J.
, &
Riis
,
J. L.
(
2011
).
Does compensatory neural activity survive old-old age?
Neuroimage
,
54
,
427
438
.
Davis
,
S. W.
,
Dennis
,
N. A.
,
Daselaar
,
S. M.
,
Fleck
,
M. S.
, &
Cabeza
,
R.
(
2008
).
Qué PASA? The posterior-anterior shift in aging.
Cerebral Cortex
,
18
,
1201
1209
.
de Fockert
,
J. W.
,
Ramchurn
,
A.
,
van Velzen
,
J.
,
Bergström
,
Z.
, &
Bunce
,
D.
(
2009
).
Behavioral and ERP evidence of greater distractor processing in old age.
Brain Research
,
1282
,
67
73
.
Duverne
,
S.
,
Motamedinia
,
S.
, &
Rugg
,
M. D.
(
2008
).
The relationship between aging, performance, and the neural correlates of successful memory encoding.
Cerebral Cortex
,
19
,
733
744
.
Folstein
,
M. F.
,
Folstein
,
S. E.
, &
McHugh
,
P. R.
(
1975
).
“Mini mental state”. A practical method for grading the cognitive state of patients for the clinician.
Journal of Psychiatric Research
,
12
,
189
198
.
Freunberger
,
R.
,
Höller
,
Y.
,
Griesmayr
,
B.
,
Gruber
,
W.
,
Sauseng
,
P.
, &
Klimesch
,
W.
(
2008
).
Functional similarities between the P1 component and alpha oscillations.
European Journal of Neuroscience
,
27
,
2330
2340
.
Fries
,
P.
(
2005
).
A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence.
Trends in Cognitive Sciences
,
9
,
474
480
.
Gazzaley
,
A.
,
Clapp
,
W.
,
Kelley
,
J.
,
McEvoy
,
K.
,
Knight
,
R. T.
, &
D'Esposito
,
M.
(
2008
).
Age-related top–down suppression deficit in the early stages of cortical visual memory processing.
Proceedings of the National Academy of Sciences, U.S.A.
,
105
,
13122
13126
.
Gazzaley
,
A.
,
Cooney
,
J. W.
,
McEvoy
,
K.
,
Knight
,
R. T.
, &
D'Esposito
,
M.
(
2005
).
Top–down enhancement and suppression of the magnitude and speed of neural activity.
Journal of Cognitive Neuroscience
,
17
,
507
517
.
Gazzaley
,
A.
,
Cooney
,
J. W.
,
Rissman
,
J.
, &
D'Esposito
,
M.
(
2005
).
Top–down suppression deficit underlies working memory impairment in normal aging.
Nature Neuroscience
,
8
,
1298
1300
.
Gazzaley
,
A.
, &
Nobre
,
A. C.
(
2012
).
Top–down modulation: Bridging selective attention and working memory.
Trends in Cognitive Sciences
,
16
,
129
135
.
Gratton
,
G.
,
Coles
,
M. G. H.
, &
Donchin
,
E.
(
1983
).
A new method for off-line removal of ocular artifact.
Electroencephalography and Clinical Neurophysiology
,
55
,
468
484
.
Gross
,
J.
,
Schmitz
,
F.
,
Schnitzler
,
I.
,
Kessler
,
K.
,
Shapiro
,
K.
,
Hommel
,
B.
,
et al
(
2004
).
Modulation of long-range neural synchrony reflects temporal limitations of visual attention in humans.
Proceedings of the National Academy of Sciences, U.S.A.
,
101
,
13050
13055
.
Haegens
,
S.
,
Osipova
,
D.
,
Oostenveld
,
R.
, &
Jensen
,
O.
(
2010
).
Somatosensory working memory performance in humans depends on both engagement and disengagement of regions in a distributed network.
Human Brain Mapping
,
31
,
26
35
.
Hanslmayr
,
S.
,
Aslan
,
A.
,
Staudigl
,
T.
,
Klimesch
,
W.
,
Herrmann
,
C. S.
, &
Bäuml
,
K. H.
(
2007
).
Prestimulus oscillations predict visual perception performance between and within subjects.
Neuroimage
,
37
,
1465
1473
.
Hasher
,
L.
, &
Zacks
,
R. T.
(
1988
).
Working memory, comprehension, and aging: A review and a new view.
In G. H. Bower (Ed.)
,
The psychology of learning and motivation
(pp.
193
225
).
San Diego, CA
:
Academic Press
.
Jensen
,
O.
, &
Mazaheri
,
A.
(
2010
).
Shaping functional architecture by oscillatory alpha activity: Gating by inhibition.
Frontiers in Human Neuroscience
,
4
,
186
.
Kastner
,
S.
, &
Ungerleider
,
L. G.
(
2000
).
Mechanisms of visual attention in the human cortex.
Annual Review of Neuroscience
,
23
,
315
341
.
Klimesch
,
W.
(
2011
).
Evoked alpha and early access to the knowledge system: The P1 inhibition timing hypothesis.
Brain Research
,
1408
,
52
71
.
Klimesch
,
W.
,
Sauseng
,
P.
, &
Hanslmayr
,
S.
(
2007
).
EEG alpha oscillations: The inhibition-timing hypothesis.
Brain Research Reviews
,
53
,
63
88
.
Knight
,
R. T.
,
Staines
,
W. R.
,
Swick
,
D.
, &
Chao
,
L. L.
(
1999
).
Prefrontal cortex regulates inhibition and excitation in distributed neural networks.
Acta Psychologica
,
101
,
159
178
.
Kramer
,
A. F.
,
Humphrey
,
D. G.
,
Larish
,
J. F.
,
Logan
,
G. D.
, &
Strayer
,
D. L.
(
1994
).
Aging and inhibition: Beyond a unitary view of inhibitory processing in attention.
Psychology and Aging
,
9
,
491
512
.
Lachaux
,
J.
,
Rodriguez
,
E.
,
Martinerie
,
J.
, &
Varela
,
F. J.
(
1999
).
Measuring phase synchrony in brain signals.
Human Brain Mapping
,
8
,
194
208
.
Lavie
,
N.
(
2005
).
Distracted and confused?: Selective attention under load.
Trends in Cognitive Sciences
,
9
,
75
82
.
Lavie
,
N.
,
Hirst
,
A.
,
De Fockert
,
J. W.
, &
Viding
,
E.
(
2004
).
Load theory of selective attention and cognitive control.
Journal of Experimental Psychology: General
,
133
,
339
354
.
Li
,
S. C.
, &
Sikström
,
S.
(
2002
).
Integrative neurocomputational perspectives on cognitive aging, neuromodulation, and representation.
Neuroscience and Biobehavioral Reviews
,
26
,
795
808
.
Mager
,
R.
,
Bullinger
,
A. H.
,
Brand
,
S.
,
Schmidlin
,
M.
,
Schärli
,
H.
,
Müller-Spahn
,
F.
,
et al
(
2007
).
Age-related changes in cognitive conflict processing: An event-related potential study.
Neurobiology of Aging
,
28
,
1925
1935
.
Mangun
,
G. R.
, &
Hillyard
,
S. A.
(
1991
).
Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming.
Journal of Experimental Psychology: Human Perception and Performance
,
17
,
1057
1074
.
Meeuwissen
,
E. B.
,
Takashima
,
A.
,
Fernandez
,
G.
, &
Jensen
,
O.
(
2011
).
Increase in posterior alpha activity during rehearsal predicts successful long-term memory formation of word sequences.
Human Brain Mapping
,
32
,
2045
2053
.
Miller
,
B. T.
, &
D'Esposito
,
M.
(
2005
).
Searching for “the top” in top–down control.
Neuron
,
48
,
535
538
.
Nagel
,
I. E.
,
Preuschhof
,
C.
,
Li
,
S. C.
,
Nyberg
,
L.
,
Bäckman
,
L.
,
Lindenberger
,
U.
,
et al
(
2009
).
Performance level modulates adult age differences in brain activation during spatial working memory.
Proceedings of the National Academy of Sciences, U.S.A.
,
106
,
22552
22557
.
Noudoost
,
B.
,
Chang
,
M. H.
,
Steinmetz
,
N. A.
, &
Moore
,
T.
(
2010
).
Top–down control of visual attention.
Current Opinion in Neurobiology
,
20
,
183
190
.
Oostenveld
,
R.
,
Fries
,
P.
,
Maris
,
E.
, &
Schoffelen
,
J.-M.
(
2011
).
FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.
Computational Intelligence and Neuroscience
,
2011
, :156869. doi: 10.1155/2011/156869.
Park
,
D. C.
,
Polk
,
T. A.
,
Park
,
R.
,
Minear
,
M.
,
Savage
,
A.
, &
Smith
,
M. R.
(
2004
).
Aging reduces neural specialization in ventral visual cortex.
Proceedings of the National Academy of Sciences, U.S.A.
,
101
,
13091
13095
.
Park
,
D. C.
, &
Reuter-Lorenz
,
P.
(
2009
).
The adaptive brain: Aging and neurocognitive scaffolding.
Annual Review of Psychology
,
60
,
173
196
.
Pinsk
,
M. A.
,
Doniger
,
G. M.
, &
Kastner
,
S.
(
2004
).
Push-pull mechanism of selective attention in human extrastriate cortex.
Journal of Neurophysiology
,
92
,
622
629
.
Posner
,
M. I.
, &
Petersen
,
S. E.
(
1990
).
The attention system of the human brain.
Annual Review of Neuroscience
,
13
,
25
42
.
Raz
,
A.
, &
Buhle
,
J.
(
2006
).
Typologies of attentional networks.
Nature Reviews Neuroscience
,
7
,
367
379
.
Raz
,
N.
,
Lindenberger
,
U.
,
Rodrigue
,
K. M.
,
Kennedy
,
K. M.
,
Head
,
D.
,
Williamson
,
A.
,
et al
(
2005
).
Regional brain changes in aging healthy adults: General trends, individual differences and modifiers.
Cerebral Cortex
,
15
,
1676
1689
.
Reuter-Lorenz
,
P. A.
, &
Cappell
,
K. A.
(
2008
).
Neurocognitive aging and the compensation hypothesis.
Current Directions in Psychological Science
,
17
,
177
182
.
Sauseng
,
P.
, &
Klimesch
,
W.
(
2008
).
What does phase information of oscillatory brain activity tell us about cognitive processes?
Neuroscience and Biobehavioral Reviews
,
32
,
1001
1013
.
Schmandt
,
B.
,
Lindeboom
,
J.
, &
van Harskamp
,
F.
(
1992
).
NLV Nederlandse Leestest voor Volwassenen Handleiding [Manual Dutch Adult Reading Test].
Lisse, The Netherlands
:
Swets & Zeitlinger
.
Uterwijk
,
J.
(
2001
).
WAIS-III Nederlandstalige bewerking. Technische handleiding [Manual Dutch adaptation WAIS-III].
Lisse, The Netherlands
:
Swets & Zeitlinger
.
Vogel
,
E. K.
, &
Luck
,
S. J.
(
2000
).
The visual N1 component as an index of a discrimination process.
Psychophysiology
,
37
,
190
203
.
Von Stein
,
A.
,
Chiang
,
C.
, &
König
,
P.
(
2000
).
Top–down processing mediated by interareal synchronization.
Proceedings of the National Academy of Sciences, U.S.A.
,
97
,
14748
14753
.
Wijers
,
A. A.
,
Okita
,
T.
,
Mulder
,
G.
,
Mulder
,
L. J.
,
Lorist
,
M. M.
,
Poiesz
,
R.
,
et al
(
1987
).
Visual search and spatial attention: ERPs in focussed and divided attention conditions.
Biological Psychology
,
25
,
33
60
.
Wild-Wall
,
N.
,
Falkenstein
,
M.
, &
Hohnsbein
,
J.
(
2008
).
Flanker interference in young and older participants as reflected in event-related potentials.
Brain Research
,
1211
,
72
84
.
Wróbel
,
A.
(
2000
).
Beta activity: A carrier for visual attention.
Acta Neurobiologiae Experimentalis
,
60
,
247
260
.
Wróbel
,
A.
,
Ghazaryan
,
A.
,
Bekisz
,
M.
,
Bogdan
,
W.
, &
Kamiński
,
J.
(
2007
).
Two streams of attention-dependent β activity in the striate recipient zone of cat's lateral posterior-pulvinar complex.
The Journal of Neuroscience
,
27
,
2230
2240
.
Zanto
,
T. P.
, &
Gazzaley
,
A.
(
2009
).
Neural suppression of irrelevant information underlies optimal working memory performance.
The Journal of Neuroscience
,
29
,
3059
3066
.
Zanto
,
T. P.
,
Rubens
,
M. T.
,
Bollinger
,
J.
, &
Gazzaley
,
A.
(
2010
).
Top–down modulation of visual feature processing: The role of the inferior frontal junction.
Neuroimage
,
53
,
736
745
.
Zigmond
,
A. S.
, &
Snaith
,
R. P.
(
1983
).
The hospital anxiety and depression scale.
Acta Psychiatrica Scandinavica
,
67
,
361
370
.