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Age effects on attentional blink performance in meditation
Consciousness and Cognition 18 (2009) 593–599
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Age effects on attentional blink performance in meditation Sara van Leeuwen a,b,*, Notger G. Müller b,c, Lucia Melloni a,b a b c
Cognitive Neurology Unit, Johann Wolfgang Goethe-University & Brain Imaging Center, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany Max Planck Institute for Brain Research, Department of Neurophysiology, Deutschordenstraße 46, 60528 Frankfurt am Main, Germany Universitätsklinik für Neurologie, Leipziger Str. 44, 39104 Magdeburg, Germany
a r t i c l e
i n f o
Article history: Received 18 August 2008 Available online 9 June 2009
Keywords: Meditation Attention Aging Plasticity
a b s t r a c t Here we explore whether mental training in the form of meditation can help to overcome age-related attentional decline. We compared performance on the attentional blink task between three populations: A group of long-term meditation practitioners within an older population, a control group of age-matched participants and a control group of young participants. Members of both control groups had never practiced meditation. Our results show that long-term meditation practice leads to a reduction of the attentional blink. Meditation practitioners taken from an older population showed a reduction in blink as compared to a control group taken from a younger population, whereas, the control group age-matched to the meditators’ group revealed a blink that was comparatively larger and broader. Our results support the hypothesis that meditation practice can: (i) alter the efficiency with which attentional resources are distributed and (ii) help to overcome age-related attentional deficits in the temporal domain. Ó 2009 Elsevier Inc. All rights reserved.
1. Introduction Human attentional resources are limited, thereby imposing the need to select information for further processing and conscious identification. A convincing demonstration of this in a laboratory setting is the attentional blink (AB) phenomenon. The AB task is a measure of the temporal characteristics of attention. During the AB task participants are asked to identify two targets embedded in a rapid serial visual presentation (RSVP). In an RSVP paradigm stimuli are presented briefly in the same location and in rapid succession. Whereas the first target is generally correctly identified, the second target is poorly identified if it appears between 200 and 500 ms after the first target (Raymond, Shapiro, & Arnell, 1992). There appears to be a refractory period in which attention is not available for processing the second target if it appears between 200 and 500 ms after the first. Targets presented later in the RSVP stream are normally easily identified though this is not the case in aging populations in which the blink recovers more slowly (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003). Aging, in fact, causes overall AB performance to worsen, both on early and late lags. Specifically, it has been shown that age negatively correlates with performance on the AB task and that the magnitude of the AB increases with age (GeorgiouKaristianis et al., 2007; Maciokas & Crognale, 2003). This increase in blink is twofold: Firstly, older participants miss the second target more frequently and secondly, they miss it for longer periods of time following T1 detection (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003). Presumably, aging participants have a longer blink due to a reduced ability to
* Corresponding author. Address: Max Planck Institute for Brain Research, Department of Neurophysiology, Deutschordenstrasse 46, D-60528 Frankfurt am Main, Germany. E-mail address: [email protected] (S. van Leeuwen). 1053-8100/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.concog.2009.05.001
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sustain attention. Both sustained attention and the efficiency of inhibitory mechanisms have been shown to be reduced in the older adults (Chao & Knight, 1997). This weakened inhibitory control leads (Pagnoni & Cekic, 2007) to increased distractedness and is thought to result from altered prefrontal cortex function in aging populations (Chao & Knight, 1997). Attentional training in the form of meditation can be expected to counteract these aging effects, helping to preserve attentional resources. The results of previous research have indeed suggested this. The cortical thickness of brain regions functionally related to attention was thicker in meditation practitioners than in controls (Lazar et al., 2005). Most interestingly, between group differences were most pronounced in older participants suggesting that meditation may offset age-related cortical thinning. Similarly, while grey matter volume is expected to decrease with age, no such correlation was found in a group of Zen meditation practitioners (Pagnoni & Cekic, 2007). As attention is crucial to other processes like memory, consciousness and decision making, understanding the possibilities and consequences for training attention and overcoming cognitive attentional decline may prove to be very valuable. Additional studies have provided evidence that meditation does indeed alter attentional processing (Brefczynski-Lewis, Lutz, Schaefer, Levinson, & Davidson, 2007; Carter et al., 2005; Jha, Krompinger, & Baime, 2007; Lazar et al., 2000, 2005; Lutz, Greischar, Rawlings, Ricard, & Davidson, 2004; Tang et al., 2007). A recent study by Tang et al. (2007) revealed that 20 min of meditation a day over a five-day period improves executive attention (Tang et al., 2007). In this study executive attention was assessed by determining the efficiency of mental conflict resolution. Other studies, employing fMRI, have shown that meditation is accompanied by increased activation in cortical regions involved in the control of attention (Brefczynski-Lewis et al., 2007; Lazar et al., 2000). These cortical regions are also those regions known to be involved in target processing during the attentional blink (Kranczioch, Debener, Schwarzbach, Goebel, & Engel, 2005; Marois, Chun, & Gore, 2000). Furthermore, in response to distractor sounds, expert meditators had more activation in regions related to response inhibition and attention than did novice meditators. Furthermore, the fact that the level of activation correlated with hours of practice suggests plasticity of the attention mechanisms (Brefczynski-Lewis et al., 2007). Together, these findings suggest that mental training, in the form of meditation, leads to lasting alterations in the attention controlling systems. Despite the fact that attentional recourses are limited, this limitation can be significantly altered as a result of an externally induced change in attentional state (Olivers & Nieuwenhuis, 2005, 2006) as well as through different types of training (Green & Bavelier, 2003; Slagter et al., 2007). Olivers and Nieuwenhuis (2005, 2006) showed that when participants are asked to focus less on the task or are distracted by music, they reveal a reduction in AB (Olivers & Nieuwenhuis, 2005, 2006). This reduction in blink is thought to result from a more distributed attentional state. Furthermore, participants showed a reduction in AB following 6 months of action video game playing (Green & Bavelier, 2003). This suggests that exposure to an altered external visual environment can modify the visual attentional system. Internally driven mental training in the form of meditation can also be expected to affect performance on the AB task. A fundamental aspect of many forms of meditation is attentional training and instructions for practice lay much emphasis on this aspect. In fact, internally driven non-task specific intense mental training in the form of a 3-month meditation retreat has shown to improve performance on the task (Slagter et al., 2007). After the retreat, meditation practitioners exhibited a significant reduction of the AB. Furthermore, meditation practitioners were better able to share their attentional resources between the first and second target, as measured by the P3b ERP component. Specifically, meditation practitioners showed a reduction in amplitude of the P3b component for T1 as compared to control participants. To investigate the effect of meditation training on attention in an aging population, the current experiment tested performance on the AB task in an older population of long-term meditation practitioners and compared this to performance levels of both age-matched and younger control participants who had never engaged in any meditation practice. We hypothesized that the long-term practice of meditation would produce changes in attentional processing that mitigate aging effects on the temporal characteristics of attention. Specifically, we predicted that older populations of meditation practitioners engaging in regular Shamatha–Vipashyana meditation practice on a long-term basis would have a reduced AB as compared to age- and education-matched control participants. Shamatha meditation is a concentration practice that serves to enhance sustained voluntary attention on an object such as the breath. During the course of this practice, practitioners often realize that their mind has wandered. Upon detection of this, they are instructed to let go of the wandering thought, without attributing too much importance to it, and re-engage their attention back to the breath. Lutz, Slagter, Dunne, and Davidson (2008) refer to this as a focused attention (FA) meditation. Vipashyana meditation involves no explicit focus on the breath or any other object. It is a follow up to the monitoring of the mind that occurs during Shamatha practice. It involves the open non-reactive monitoring of experience itself, without focusing on, or clinging to, any specific object. This style of meditation can also be referred to as an open monitoring (OM) style of meditation (Lutz et al., 2008). It can be understood as a more distributed state of awareness that involves the open, non-reactive awareness of automatic cognitive and emotional interpretations of sensory, perceptual and endogenous stimuli (Lutz et al., 2008). We predicted that long-term practice in directing and sustaining attention to an object combined with training in the open, non-reactive, monitoring of cognitive events should allow meditation practitioners not only to direct and sustain attention to a target more efficiently but also to react and cling to the first target in the stream less strongly, allowing sufficient recourses to process the second target in the AB task stream. Furthermore, even though performance on the AB task is expected to be negatively correlated with age, we expected meditation practitioners to perform better than their age-matched counterparts and equally well as the control group of younger participants.
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2. Method 2.1. Participants Seventeen meditation practitioners (mean age 49.8, SD 5, 10 males), seventeen age, sex and education matched controls (mean age 50, SD 5.4, 10 males) and seventeen young controls, (mean age 24.3, SD 2.27, 7 males) who had never engaged in any form of meditation practice, participated in this experiment. Participants were paid for participation in the study conducted in conformity with the Declaration of Helsinki and approved by the local ethics committee. For this study a rather homogenous group of meditation practitioners who had engaged in the long-term practice of meditation were chosen as participants. Fourteen of the seventeen meditation practitioners were recruited from the Shambala meditation center. These practitioners regularly practiced Shamatha–Vipashyana (FA and OM) meditation and a fraction of them were also trained in loving kindness and compassion meditation. The other three practitioners from a Zen Dojo in Frankfurt am Main, Germany were trained in the practice of Zazen. Zazen is much like Shamatha practice in that the practitioners also focus their concentration on the breath while seated in the lotus position. Practitioners reported having between 1 and 29 years of experience. 2.2. Stimuli Stimuli were generated and responses recorded using Presentation 9.90 Software (www.neurobs.com). The experimental paradigm is illustrated in Fig. 1. On each trial, a fixation cross subtending approximately 0.57° 0.57° of visual angle was presented in the center of the screen for a duration set randomly between 1000 and 1500 ms. The fixation cross was subsequently replaced by a rapid serial visual presentation of 10–21 letters, each measuring approximately 0.86° 0.86°. Each letter was randomly drawn from the alphabet with the restriction that successive letters had to differ. The presented letters included all letters of the alphabet with the exception of the letters I, O, Q, S, X and Z. The letters were presented for 100 ms. Two of the letters in the stream were replaced with digits, randomly drawn (without replacement) from the set 2 to 9. The first digit (T1) was presented 7 or 10 temporal positions from the beginning of the RSVP stream. The temporal distance between the first digit (T1) and the second digit (T2) was systematically varied from 1 to 7 items, corresponding to lags of 100, 200, 300, 400, 500, 600 and 700 ms and the second digit (T2) was presented 2 or 4 temporal positions from the end of the stream. All T1 stimuli were presented in red and the T2 stimuli in black on a light grey background. 2.3. Procedure The participants’ task was to identify both T1 and T2. They were asked to respond to each trial by typing the digits in order on a standard keyboard. No time limit was set for this task. Participants were instructed to guess whenever they failed to identify a digit. The experiment started with seven practice trials, followed by two blocks of 140 trials, resulting in a total of 40 trials per lag which were randomly presented. The experiment lasted approximately 30 min and participants were paid €5.
Fig. 1. Outline of the AB paradigm. On every trial, between 10 and 21 items were presented at the center of the screen, preceded by a 1000–1500 ms fixation cross. Each item was presented for 100 ms each. Among the items were two target digits (T1 and T2), which observers had to report at the end of the trial. The first target was presented in red and the second in black. The interval between T1 and T2, referred to as lag, varied from 1 to 7 temporal positions (i.e., from 100 ms to 700 ms).
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3. Results A one-way analysis of variance (ANOVA) comparing the ages of the participants in the meditation group with the control group verified that there was no significant age difference between them F(1, 33) = .106, p > .05. An ANOVA comparing the years of education of the participants confirmed that there was no significant difference in years of education between the two groups F(1, 33) = .04, p > .05. An ANOVA conducted on T1 accuracy with group (meditators, age-matched controls and young controls) as a betweensubject factor and lag (1–7) as a within-subject factor, showed no significant main effect of group F(2, 48) = 1.4, p > .05, and no lag group interaction F(12, 88) = .93, p > .05. The main effect of lag was significant, F(6, 43) = 8.37, p < .0001, reflecting lower accuracy at lag1 (mean = 91.6, SD = 9.7) than at the other lags (mean = 98.29, SD = 3.76). This effect is equivalent to backward masking. Fig. 2A shows the proportion of trials in which T1 had been correctly identified as a function of lag for meditators, age-matched controls and young controls, respectively. In the analysis of T2 accuracy, only trials in which T1 had been correctly identified were considered. An ANOVA conducted on T2 accuracy with group as a between-subjects factor and lag as a within-subjects factor, revealed a main effect of group F(2, 48) = 5.71, p < .01 and a main effect of lag F(6, 43) = 19.92, p < .001. This analysis also revealed a significant group lag interaction F(12, 88) = 2.05, p < .01. Mean T2 accuracy followed a classical AB pattern (see Fig. 2B). Mean T2 accuracy decreased from lag1 (95%) to lag2 (75%) and then increased again reaching 88% at lag7. Accuracy in the RSVP task was significantly higher for the meditators as compared to the age-matched controls (F(1, 33) = 11.05, p < .01 mean 89.3 and 78.3, respectively). Accuracy was also significantly higher for the young controls as compared to the age-matched controls (F(1, 33) = 4.1, p < .05 mean 86.4 and 78.3, respectively). No difference was found between the meditator group and the young control group (F(1, 33) = 2.84, p > .05). The groups did not perform differently on lag1 (all p > .05). A one way ANOVA on lag2 revealed a main effect of group (F(2, 50) = 5.28, p < .01). Paired t-tests showed that performance on lag2 differed between the meditation group and the young control group (F(1, 33) = 4.48, p < .05, mean 84.9 and 70.8, respectively), and also between the meditation group and the age-matched control group (F(1, 33) = 7.2, p < .05, mean 84.9 and 68.3, respectively). No other differences between
Fig. 2. Average detection accuracy for the first target digit (A) and second target digit (B) in a rapid serial visual stream of letters. Results are presented separately for each group (meditators, age-matched controls and young controls). Error bars denote standard errors of the means.
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groups were significant for lag2. For lag3 there was no significant difference between groups. Lag4 showed a main effect of group (F(2, 50) = 3.94, p < .05). Paired t-tests showed a significant difference between the age-matched group and the young control group only (F(1, 33) = 4.4, p < .05, mean 75.6 and 87.5, respectively). Lag5, Lag6 and Lag7 all showed a significant effect of group (F(2, 50) = 3.57, p < .05, F(2, 50) = 5.57, p < .01, and F(2, 50) = 10.87, p < .001 respectively). For Lag5, the agematched control group differed significantly from the young control group (F(1, 33) = 4.3, p < .05, mean 81.4 and 91.1, respectively). For Lag6 and Lag7 performance differed significantly between both the meditator group and the age-matched control group as well as between the age-matched control group and the young control group (lag6 F(1, 33) = 5.8, < .05, mean 90.3 and 78,4, respectively, and F(1, 33) = 6.7, < .05, mean 78.4 and 91.1, respectively; for lag7 F(1, 33) = 13.03, p < .01, mean 92.7 and 79,4, respectively, and F(1, 33) = 12.2, p < .01, mean 79.4 and 92.7, respectively).
4. Discussion Our results suggest that the practice of meditation aids in overcoming age-related attentional deficits in the temporal domain. Two pieces of evidence support this conclusion. First, the meditation group outperformed the age-matched control group showing a reduction in attentional blink. Not only was the magnitude of the blink larger for the age-matched control group as compared to the meditation group but their blink also persisted for a longer period of time, outlasting that of both the meditation and the young control group. Second, the meditation group, stemming from an older population, performed even better than the young control group even though performance on the attentional blink has been shown to drop with age (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003). All together, this strongly suggests that meditation practice can counteract age-related deterioration in performance on the AB task. In line with previous research, we also found a significant difference in attentional blink performance between the agematched control group and the young control group. A further investigation of this reduction in performance showed that attentional blink performance in the age-matched control group was significantly reduced in both early and later lags (4–7) as compared to the young control group. That is, as participants’ age increases, performance on the AB task decreases. Our findings are in line with previous studies (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003), showing that the blink is larger in magnitude and also longer lasting in older populations. Presumably, this deficit is due to the fact that older participants show increased distractedness as a result of reduced inhibitory control and have more difficulties sustaining attention for longer periods of time (Chao & Knight, 1997). Our results suggest that the practice of meditation does more than mitigate these aging effects. The meditators performed significantly better than the age-matched controls on lags 2, 6 and 7 but they also performed significantly better than the young controls on lag2. Accordingly, there is evidence of a neuroprotective effect of meditation. Lazar et al. reported a difference between meditators and controls in cortical thickness over prefrontal areas and Pagnoni and Cekic (2007) reported a reduced age related decline in both global and regional grey matter volume. Due to the cross-sectional design of this study it is conceivable that differences in performance between the meditation group and control group are due to a different factor i.e., individual variability, and not due to meditation. People who choose to practice meditation could already be different from those who do not. Likewise, in a longitudinal design in which participants are tested before and after a meditation retreat, differences could also be attributed to a different factor. They could, for instance, be attributed to the fact that the participants have just spent an extended period of time in a much less stressful environment. Given that both experimental designs – cross-sectional and longitudinal – have clear limitations, replicating findings with different designs is crucial in validating the hypothesis that meditation itself causes alterations in attention. Previous evidence has shown that meditation alters performance in the attentional blink task. Slagter et al. (2007) carried out a longitudinal AB study on meditators and found a significant reduction in AB after participants had taken part in a threemonth meditation retreat. Thus, this and the current findings provide evidence that the practice of meditation leads to a reduction in AB. However, they failed to observe differences in AB performance between controls and meditators before the meditators took part in the retreat. This might be explained, as Slagter et al. themselves suggest, by the differences across the practitioners who took part in their study and by the styles and traditions of the previously learned meditation (Slagter et al., 2007). It is known that meditation practice can differ significantly across traditions; some laying more emphasis on the training of attention than others. Different types of meditation have also recently been linked to distinct patterns of neural activity depending on the emphasis of the type of meditation, proving that differences in styles of meditation are not just subjective (Lehmann et al., 2001). These differences might have introduced undesired sources of variability leading to a null effect. In contrast to the study of Slagter et al. (2007) the participants in this study belonged to a rather homogeneous meditation group whose practice laid ample emphasis on the training of attention in the form of both FA and OM meditation practice. Our study shows that long-term meditation practitioners who have not just partaken in an intense three-month meditation retreat also show a reduction in AB and therefore an improvement in the ability to more efficiently distribute limited attentional resources. Furthermore, in contrast to the Slagter et al. study, here we tested a population of older participants and show that the practice of meditation could overcome the effect of aging on attentional processes to such an extent that older practitioners show a reduction in blink as compared to young control participants. In addition, meditators had an increased ability to sustain attention at longer lags as compared to the age-matched control group. Our study was able to reveal this difference in AB performance due to the fact that our meditators stemmed from an older population, whereas the participants of the Slagter et al. study stemmed from a younger population.
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It is worthwhile to consider the mechanisms by which the type of meditation under investigation here may lead to a reduced blink of shorter duration. Both the participants of the Slagter et al. (2007) study and the practitioners of this study had gained practice in OM meditation. In the first case by participating in a 3 months intensive OM meditation retreat and in the second by practicing both FA and OM meditation more gradually over a period of years. OM meditation is thought to cultivate non-reactive awareness of experience, without focus on an object. By non-reactive is meant that the practitioner does not react to a thought or emotion when it appears but simply allows it to be and then allows it to pass without evaluating or interpreting it and without allowing a chain reaction of thoughts to take effect (Lutz et al., 2008). The current and previous results suggest that practice in OM meditation does indeed allow practitioners to more efficiently register the first target by not ‘over-investing’ in it and disengaging attention from it relatively quickly. This leaves practitioners with sufficient resources to process the second target upon detection of the first, more often than is the case for non-practitioners (Lutz et al., 2008; Slagter et al., 2007). Support for this hypothesis is found in a reduction of the amplitude of the P3b component related to the first target (Slagter et al., 2007). However, as previously mentioned, in addition to having practiced OM meditation, the meditation group in our experiment had also engaged in extensive FA meditation training. The ability to sustain attention over longer periods of time is fundamental to FA meditation practice. Presumably, this is why our meditation group showed an enhanced ability to sustain attention during the AB task in addition to a reduction in AB as compared to the agematched controls. This is reflected in the fact that the meditation group outperformed the age-matched control group at longer lags. As expected, Zen meditation practitioners, who had ample practice in FA meditation, also showed improved performance on a sustained attention task (Pagnoni & Cekic, 2007). It may be argued that motivation and not necessarily differences between groups could explain the difference in performance between the meditators and the age-matched controls. This is very unlikely because even if the meditation group found it more rewarding to take part in the experiment, performance in the AB has been found to be impervious to manipulations of motivation. Olivers and Nieuwenhuis (2005) introduced a reward condition in their AB experiment in which participants were financially rewarded according to their performance. There was no significant difference in the results between the reward group and the standard group. Only at the longest lag (corresponding to lag5 in our study) was there a slight trend towards better performance for the reward group. The authors conclude that the duration, but not the magnitude, of the AB may be reduced under conditions of higher motivation. It is however unlikely that motivation is the sole explanation of the present finding that meditators performed better at longer lags than did controls. Firstly, differences in performance between groups at longer lags reached high levels of significance in this study. Secondly, further support for the finding that meditation helps to overcome age-related cognitive decline in the ability to sustain attention can be found in the literature (Chambers, Lo, & Allen, 2008; Pagnoni & Cekic, 2007). Pagnoni and Cekic (2007) measured the individual capacity of sustained attention by means of a rapid visual information processing task and found that meditators showed virtually no age-related deterioration in performance of the task. In comparison, the control group did show an age-related reduction in target sensitivity and quickness to respond. The fact that this study and that of Slagter et al. (2007) indicate that the practice of meditation leads to a reduction in AB does not stipulate how much practice one must have undertaken in order to obtain this reduction in blink. Previous studies suggest that attentional processing begins to change relatively early in the training process (Jha et al., 2007; Tang et al., 2007). However, the course of this change in attentional processing is still rather unclear. The change could take place rather gradually or quite abruptly during the practice. We found no correlation between years of meditation experience and performance on the task (data not shown). This could be due to the small sample size of this study. Also, ideally we would have been able to correlate the amount of hours of meditation practice with performance on the task. This would have been a more exact method of correlating experience with performance. However, estimating total hours of experience proved to be rather impossible. At times participants meditated for 90 min and at other times only 15 min. There were times when they meditated only twice a week and times when they meditated every day of the week. For this reason we could not make a meaningful calculation of total hours of experience. In the future, carefully controlled longitudinal studies, with larger sample sizes will be needed in order to investigate the change in attentional processing as a function of hours of meditation practice, how this can vary between participants and meditation practices and how long this change in processing is retained after training. The present results leave these questions unanswered and should be tackled in future research. Until such data is made available, the current results should be taken with caution due to the cross-sectional design of the study. Research on perceptual learning has shown that learning can occur throughout almost the entire life span (Goldstone, 1998). Nonetheless, those forms of learning are typically fixed to the specific task trained. They require a lot of practice and have shown little generalization to novel contexts. For example, the dynamics of perceptual switches have been shown to be altered through task specific, binocular rivalry training. Binocular rivalry is a perceptual phenomenon that occurs when two different images are presented to each eye. Rather than seeing a blend of the two images participants see either the one or the other and this fluctuates randomly at a rate between 2 and 5 s. When observers experienced binocular rivalry repeatedly over many days, the rate of perceptual switches increased (Suzuki & Grabowecky, 2007). In contrast to this type of task-specific training, task unspecific training in the form of meditation can generalize to other tasks. For example, Buddhist monks showed an alteration in the inherent fluctuations in conscious state induced by binocular rivalry, both during and after meditation. These individuals, highly trained in meditation, could sustain attention on a percept for long periods of time even though in the normal population it is known to fluctuate at a rate between 2 and 5 s. Three of the tested monks even reported complete perceptual stability throughout the entire 5 min meditation period (Carter et al., 2005). The results presented here and other studies (Carter et al., 2005; Green and Bavelier, 2003; Jha et al., 2007; Slagter et al., 2007; Tang et al., 2007) further support the notion that a
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task unspecific form of training, such as meditation or video game playing, could have a general impact on a higher cognitive function such as attention. Specifically, this study supports the notion that mental training in the form of meditation leads to a reduction in AB, showing that it alters the efficiency with which attention processes elements in time. This mental training in the form of meditation seems to counteract an age-related increase in the magnitude of the AB, allowing practitioners to overcome age-related attentional cognitive decline in the temporal domain. The present findings, despite the limitations of small sample size and cross-sectional design, provide valuable evidence that internally driven mental training in the form of meditation is affecting attentional processing in some way, rendering attentional processing a plastic process and therefore subject to training effects e.g. (Green and Bavelier, 2003; Slagter et al., 2007). Acknowledgments We would like to acknowledge the Grants that financed this research: The DFG Mu 1364/2, BMBF 01GO0507, the Dr. Paul and Cilli Weill-Stiftung and the August Scheidel-Stiftung. We are grateful to Prof. Dr. Wolf Singer for giving feedback on the manuscript, to Arjen Alink for helping to program the experiment and to Verena Krupp for helping us in testing participants. We also thank the reviewers for their constructive comments. References Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B., & Davidson, R. J. (2007). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11483–11488. Carter, O. L., Presti, D. E., Callistemon, C., Ungerer, Y., Liu, G. B., & Pettigrew, J. D. (2005). Meditation alters perceptual rivalry in Tibetan Buddhist monks. Current Biology, 15(11), R412–413. Chambers, R., Lo, B. C. Y., & Allen, N. B. (2008). 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Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences of the United States of America, 101(46), 16369–16373. Lutz, A., Slagter, H. A., Dunne, J. D., & Davidson, R. J. (2008). Attention regulation and monitoring in meditation. Trends in Cognitive Sciences, 12(4), 163–169. Maciokas, J. B., & Crognale, M. A. (2003). Cognitive and attentional changes with age: evidence from attentional blink deficits. Experimental Aging Research, 29(2), 137–153. Marois, R., Chun, M. M., & Gore, J. C. (2000). Neural correlates of the attentional blink. Neuron, 28(1), 299–308. Olivers, C. N., & Nieuwenhuis, S. (2005). The beneficial effect of concurrent task-irrelevant mental activity on temporal attention. Psychological Science, 16(4), 265–269. Olivers, C. N., & Nieuwenhuis, S. (2006). The beneficial effects of additional task load, positive affect, and instruction on the attentional blink. 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Contents lists available at ScienceDirect
Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog
Age effects on attentional blink performance in meditation Sara van Leeuwen a,b,*, Notger G. Müller b,c, Lucia Melloni a,b a b c
Cognitive Neurology Unit, Johann Wolfgang Goethe-University & Brain Imaging Center, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany Max Planck Institute for Brain Research, Department of Neurophysiology, Deutschordenstraße 46, 60528 Frankfurt am Main, Germany Universitätsklinik für Neurologie, Leipziger Str. 44, 39104 Magdeburg, Germany
a r t i c l e
i n f o
Article history: Received 18 August 2008 Available online 9 June 2009
Keywords: Meditation Attention Aging Plasticity
a b s t r a c t Here we explore whether mental training in the form of meditation can help to overcome age-related attentional decline. We compared performance on the attentional blink task between three populations: A group of long-term meditation practitioners within an older population, a control group of age-matched participants and a control group of young participants. Members of both control groups had never practiced meditation. Our results show that long-term meditation practice leads to a reduction of the attentional blink. Meditation practitioners taken from an older population showed a reduction in blink as compared to a control group taken from a younger population, whereas, the control group age-matched to the meditators’ group revealed a blink that was comparatively larger and broader. Our results support the hypothesis that meditation practice can: (i) alter the efficiency with which attentional resources are distributed and (ii) help to overcome age-related attentional deficits in the temporal domain. Ó 2009 Elsevier Inc. All rights reserved.
1. Introduction Human attentional resources are limited, thereby imposing the need to select information for further processing and conscious identification. A convincing demonstration of this in a laboratory setting is the attentional blink (AB) phenomenon. The AB task is a measure of the temporal characteristics of attention. During the AB task participants are asked to identify two targets embedded in a rapid serial visual presentation (RSVP). In an RSVP paradigm stimuli are presented briefly in the same location and in rapid succession. Whereas the first target is generally correctly identified, the second target is poorly identified if it appears between 200 and 500 ms after the first target (Raymond, Shapiro, & Arnell, 1992). There appears to be a refractory period in which attention is not available for processing the second target if it appears between 200 and 500 ms after the first. Targets presented later in the RSVP stream are normally easily identified though this is not the case in aging populations in which the blink recovers more slowly (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003). Aging, in fact, causes overall AB performance to worsen, both on early and late lags. Specifically, it has been shown that age negatively correlates with performance on the AB task and that the magnitude of the AB increases with age (GeorgiouKaristianis et al., 2007; Maciokas & Crognale, 2003). This increase in blink is twofold: Firstly, older participants miss the second target more frequently and secondly, they miss it for longer periods of time following T1 detection (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003). Presumably, aging participants have a longer blink due to a reduced ability to
* Corresponding author. Address: Max Planck Institute for Brain Research, Department of Neurophysiology, Deutschordenstrasse 46, D-60528 Frankfurt am Main, Germany. E-mail address: [email protected] (S. van Leeuwen). 1053-8100/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.concog.2009.05.001
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sustain attention. Both sustained attention and the efficiency of inhibitory mechanisms have been shown to be reduced in the older adults (Chao & Knight, 1997). This weakened inhibitory control leads (Pagnoni & Cekic, 2007) to increased distractedness and is thought to result from altered prefrontal cortex function in aging populations (Chao & Knight, 1997). Attentional training in the form of meditation can be expected to counteract these aging effects, helping to preserve attentional resources. The results of previous research have indeed suggested this. The cortical thickness of brain regions functionally related to attention was thicker in meditation practitioners than in controls (Lazar et al., 2005). Most interestingly, between group differences were most pronounced in older participants suggesting that meditation may offset age-related cortical thinning. Similarly, while grey matter volume is expected to decrease with age, no such correlation was found in a group of Zen meditation practitioners (Pagnoni & Cekic, 2007). As attention is crucial to other processes like memory, consciousness and decision making, understanding the possibilities and consequences for training attention and overcoming cognitive attentional decline may prove to be very valuable. Additional studies have provided evidence that meditation does indeed alter attentional processing (Brefczynski-Lewis, Lutz, Schaefer, Levinson, & Davidson, 2007; Carter et al., 2005; Jha, Krompinger, & Baime, 2007; Lazar et al., 2000, 2005; Lutz, Greischar, Rawlings, Ricard, & Davidson, 2004; Tang et al., 2007). A recent study by Tang et al. (2007) revealed that 20 min of meditation a day over a five-day period improves executive attention (Tang et al., 2007). In this study executive attention was assessed by determining the efficiency of mental conflict resolution. Other studies, employing fMRI, have shown that meditation is accompanied by increased activation in cortical regions involved in the control of attention (Brefczynski-Lewis et al., 2007; Lazar et al., 2000). These cortical regions are also those regions known to be involved in target processing during the attentional blink (Kranczioch, Debener, Schwarzbach, Goebel, & Engel, 2005; Marois, Chun, & Gore, 2000). Furthermore, in response to distractor sounds, expert meditators had more activation in regions related to response inhibition and attention than did novice meditators. Furthermore, the fact that the level of activation correlated with hours of practice suggests plasticity of the attention mechanisms (Brefczynski-Lewis et al., 2007). Together, these findings suggest that mental training, in the form of meditation, leads to lasting alterations in the attention controlling systems. Despite the fact that attentional recourses are limited, this limitation can be significantly altered as a result of an externally induced change in attentional state (Olivers & Nieuwenhuis, 2005, 2006) as well as through different types of training (Green & Bavelier, 2003; Slagter et al., 2007). Olivers and Nieuwenhuis (2005, 2006) showed that when participants are asked to focus less on the task or are distracted by music, they reveal a reduction in AB (Olivers & Nieuwenhuis, 2005, 2006). This reduction in blink is thought to result from a more distributed attentional state. Furthermore, participants showed a reduction in AB following 6 months of action video game playing (Green & Bavelier, 2003). This suggests that exposure to an altered external visual environment can modify the visual attentional system. Internally driven mental training in the form of meditation can also be expected to affect performance on the AB task. A fundamental aspect of many forms of meditation is attentional training and instructions for practice lay much emphasis on this aspect. In fact, internally driven non-task specific intense mental training in the form of a 3-month meditation retreat has shown to improve performance on the task (Slagter et al., 2007). After the retreat, meditation practitioners exhibited a significant reduction of the AB. Furthermore, meditation practitioners were better able to share their attentional resources between the first and second target, as measured by the P3b ERP component. Specifically, meditation practitioners showed a reduction in amplitude of the P3b component for T1 as compared to control participants. To investigate the effect of meditation training on attention in an aging population, the current experiment tested performance on the AB task in an older population of long-term meditation practitioners and compared this to performance levels of both age-matched and younger control participants who had never engaged in any meditation practice. We hypothesized that the long-term practice of meditation would produce changes in attentional processing that mitigate aging effects on the temporal characteristics of attention. Specifically, we predicted that older populations of meditation practitioners engaging in regular Shamatha–Vipashyana meditation practice on a long-term basis would have a reduced AB as compared to age- and education-matched control participants. Shamatha meditation is a concentration practice that serves to enhance sustained voluntary attention on an object such as the breath. During the course of this practice, practitioners often realize that their mind has wandered. Upon detection of this, they are instructed to let go of the wandering thought, without attributing too much importance to it, and re-engage their attention back to the breath. Lutz, Slagter, Dunne, and Davidson (2008) refer to this as a focused attention (FA) meditation. Vipashyana meditation involves no explicit focus on the breath or any other object. It is a follow up to the monitoring of the mind that occurs during Shamatha practice. It involves the open non-reactive monitoring of experience itself, without focusing on, or clinging to, any specific object. This style of meditation can also be referred to as an open monitoring (OM) style of meditation (Lutz et al., 2008). It can be understood as a more distributed state of awareness that involves the open, non-reactive awareness of automatic cognitive and emotional interpretations of sensory, perceptual and endogenous stimuli (Lutz et al., 2008). We predicted that long-term practice in directing and sustaining attention to an object combined with training in the open, non-reactive, monitoring of cognitive events should allow meditation practitioners not only to direct and sustain attention to a target more efficiently but also to react and cling to the first target in the stream less strongly, allowing sufficient recourses to process the second target in the AB task stream. Furthermore, even though performance on the AB task is expected to be negatively correlated with age, we expected meditation practitioners to perform better than their age-matched counterparts and equally well as the control group of younger participants.
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2. Method 2.1. Participants Seventeen meditation practitioners (mean age 49.8, SD 5, 10 males), seventeen age, sex and education matched controls (mean age 50, SD 5.4, 10 males) and seventeen young controls, (mean age 24.3, SD 2.27, 7 males) who had never engaged in any form of meditation practice, participated in this experiment. Participants were paid for participation in the study conducted in conformity with the Declaration of Helsinki and approved by the local ethics committee. For this study a rather homogenous group of meditation practitioners who had engaged in the long-term practice of meditation were chosen as participants. Fourteen of the seventeen meditation practitioners were recruited from the Shambala meditation center. These practitioners regularly practiced Shamatha–Vipashyana (FA and OM) meditation and a fraction of them were also trained in loving kindness and compassion meditation. The other three practitioners from a Zen Dojo in Frankfurt am Main, Germany were trained in the practice of Zazen. Zazen is much like Shamatha practice in that the practitioners also focus their concentration on the breath while seated in the lotus position. Practitioners reported having between 1 and 29 years of experience. 2.2. Stimuli Stimuli were generated and responses recorded using Presentation 9.90 Software (www.neurobs.com). The experimental paradigm is illustrated in Fig. 1. On each trial, a fixation cross subtending approximately 0.57° 0.57° of visual angle was presented in the center of the screen for a duration set randomly between 1000 and 1500 ms. The fixation cross was subsequently replaced by a rapid serial visual presentation of 10–21 letters, each measuring approximately 0.86° 0.86°. Each letter was randomly drawn from the alphabet with the restriction that successive letters had to differ. The presented letters included all letters of the alphabet with the exception of the letters I, O, Q, S, X and Z. The letters were presented for 100 ms. Two of the letters in the stream were replaced with digits, randomly drawn (without replacement) from the set 2 to 9. The first digit (T1) was presented 7 or 10 temporal positions from the beginning of the RSVP stream. The temporal distance between the first digit (T1) and the second digit (T2) was systematically varied from 1 to 7 items, corresponding to lags of 100, 200, 300, 400, 500, 600 and 700 ms and the second digit (T2) was presented 2 or 4 temporal positions from the end of the stream. All T1 stimuli were presented in red and the T2 stimuli in black on a light grey background. 2.3. Procedure The participants’ task was to identify both T1 and T2. They were asked to respond to each trial by typing the digits in order on a standard keyboard. No time limit was set for this task. Participants were instructed to guess whenever they failed to identify a digit. The experiment started with seven practice trials, followed by two blocks of 140 trials, resulting in a total of 40 trials per lag which were randomly presented. The experiment lasted approximately 30 min and participants were paid €5.
Fig. 1. Outline of the AB paradigm. On every trial, between 10 and 21 items were presented at the center of the screen, preceded by a 1000–1500 ms fixation cross. Each item was presented for 100 ms each. Among the items were two target digits (T1 and T2), which observers had to report at the end of the trial. The first target was presented in red and the second in black. The interval between T1 and T2, referred to as lag, varied from 1 to 7 temporal positions (i.e., from 100 ms to 700 ms).
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3. Results A one-way analysis of variance (ANOVA) comparing the ages of the participants in the meditation group with the control group verified that there was no significant age difference between them F(1, 33) = .106, p > .05. An ANOVA comparing the years of education of the participants confirmed that there was no significant difference in years of education between the two groups F(1, 33) = .04, p > .05. An ANOVA conducted on T1 accuracy with group (meditators, age-matched controls and young controls) as a betweensubject factor and lag (1–7) as a within-subject factor, showed no significant main effect of group F(2, 48) = 1.4, p > .05, and no lag group interaction F(12, 88) = .93, p > .05. The main effect of lag was significant, F(6, 43) = 8.37, p < .0001, reflecting lower accuracy at lag1 (mean = 91.6, SD = 9.7) than at the other lags (mean = 98.29, SD = 3.76). This effect is equivalent to backward masking. Fig. 2A shows the proportion of trials in which T1 had been correctly identified as a function of lag for meditators, age-matched controls and young controls, respectively. In the analysis of T2 accuracy, only trials in which T1 had been correctly identified were considered. An ANOVA conducted on T2 accuracy with group as a between-subjects factor and lag as a within-subjects factor, revealed a main effect of group F(2, 48) = 5.71, p < .01 and a main effect of lag F(6, 43) = 19.92, p < .001. This analysis also revealed a significant group lag interaction F(12, 88) = 2.05, p < .01. Mean T2 accuracy followed a classical AB pattern (see Fig. 2B). Mean T2 accuracy decreased from lag1 (95%) to lag2 (75%) and then increased again reaching 88% at lag7. Accuracy in the RSVP task was significantly higher for the meditators as compared to the age-matched controls (F(1, 33) = 11.05, p < .01 mean 89.3 and 78.3, respectively). Accuracy was also significantly higher for the young controls as compared to the age-matched controls (F(1, 33) = 4.1, p < .05 mean 86.4 and 78.3, respectively). No difference was found between the meditator group and the young control group (F(1, 33) = 2.84, p > .05). The groups did not perform differently on lag1 (all p > .05). A one way ANOVA on lag2 revealed a main effect of group (F(2, 50) = 5.28, p < .01). Paired t-tests showed that performance on lag2 differed between the meditation group and the young control group (F(1, 33) = 4.48, p < .05, mean 84.9 and 70.8, respectively), and also between the meditation group and the age-matched control group (F(1, 33) = 7.2, p < .05, mean 84.9 and 68.3, respectively). No other differences between
Fig. 2. Average detection accuracy for the first target digit (A) and second target digit (B) in a rapid serial visual stream of letters. Results are presented separately for each group (meditators, age-matched controls and young controls). Error bars denote standard errors of the means.
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groups were significant for lag2. For lag3 there was no significant difference between groups. Lag4 showed a main effect of group (F(2, 50) = 3.94, p < .05). Paired t-tests showed a significant difference between the age-matched group and the young control group only (F(1, 33) = 4.4, p < .05, mean 75.6 and 87.5, respectively). Lag5, Lag6 and Lag7 all showed a significant effect of group (F(2, 50) = 3.57, p < .05, F(2, 50) = 5.57, p < .01, and F(2, 50) = 10.87, p < .001 respectively). For Lag5, the agematched control group differed significantly from the young control group (F(1, 33) = 4.3, p < .05, mean 81.4 and 91.1, respectively). For Lag6 and Lag7 performance differed significantly between both the meditator group and the age-matched control group as well as between the age-matched control group and the young control group (lag6 F(1, 33) = 5.8, < .05, mean 90.3 and 78,4, respectively, and F(1, 33) = 6.7, < .05, mean 78.4 and 91.1, respectively; for lag7 F(1, 33) = 13.03, p < .01, mean 92.7 and 79,4, respectively, and F(1, 33) = 12.2, p < .01, mean 79.4 and 92.7, respectively).
4. Discussion Our results suggest that the practice of meditation aids in overcoming age-related attentional deficits in the temporal domain. Two pieces of evidence support this conclusion. First, the meditation group outperformed the age-matched control group showing a reduction in attentional blink. Not only was the magnitude of the blink larger for the age-matched control group as compared to the meditation group but their blink also persisted for a longer period of time, outlasting that of both the meditation and the young control group. Second, the meditation group, stemming from an older population, performed even better than the young control group even though performance on the attentional blink has been shown to drop with age (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003). All together, this strongly suggests that meditation practice can counteract age-related deterioration in performance on the AB task. In line with previous research, we also found a significant difference in attentional blink performance between the agematched control group and the young control group. A further investigation of this reduction in performance showed that attentional blink performance in the age-matched control group was significantly reduced in both early and later lags (4–7) as compared to the young control group. That is, as participants’ age increases, performance on the AB task decreases. Our findings are in line with previous studies (Georgiou-Karistianis et al., 2007; Maciokas & Crognale, 2003), showing that the blink is larger in magnitude and also longer lasting in older populations. Presumably, this deficit is due to the fact that older participants show increased distractedness as a result of reduced inhibitory control and have more difficulties sustaining attention for longer periods of time (Chao & Knight, 1997). Our results suggest that the practice of meditation does more than mitigate these aging effects. The meditators performed significantly better than the age-matched controls on lags 2, 6 and 7 but they also performed significantly better than the young controls on lag2. Accordingly, there is evidence of a neuroprotective effect of meditation. Lazar et al. reported a difference between meditators and controls in cortical thickness over prefrontal areas and Pagnoni and Cekic (2007) reported a reduced age related decline in both global and regional grey matter volume. Due to the cross-sectional design of this study it is conceivable that differences in performance between the meditation group and control group are due to a different factor i.e., individual variability, and not due to meditation. People who choose to practice meditation could already be different from those who do not. Likewise, in a longitudinal design in which participants are tested before and after a meditation retreat, differences could also be attributed to a different factor. They could, for instance, be attributed to the fact that the participants have just spent an extended period of time in a much less stressful environment. Given that both experimental designs – cross-sectional and longitudinal – have clear limitations, replicating findings with different designs is crucial in validating the hypothesis that meditation itself causes alterations in attention. Previous evidence has shown that meditation alters performance in the attentional blink task. Slagter et al. (2007) carried out a longitudinal AB study on meditators and found a significant reduction in AB after participants had taken part in a threemonth meditation retreat. Thus, this and the current findings provide evidence that the practice of meditation leads to a reduction in AB. However, they failed to observe differences in AB performance between controls and meditators before the meditators took part in the retreat. This might be explained, as Slagter et al. themselves suggest, by the differences across the practitioners who took part in their study and by the styles and traditions of the previously learned meditation (Slagter et al., 2007). It is known that meditation practice can differ significantly across traditions; some laying more emphasis on the training of attention than others. Different types of meditation have also recently been linked to distinct patterns of neural activity depending on the emphasis of the type of meditation, proving that differences in styles of meditation are not just subjective (Lehmann et al., 2001). These differences might have introduced undesired sources of variability leading to a null effect. In contrast to the study of Slagter et al. (2007) the participants in this study belonged to a rather homogeneous meditation group whose practice laid ample emphasis on the training of attention in the form of both FA and OM meditation practice. Our study shows that long-term meditation practitioners who have not just partaken in an intense three-month meditation retreat also show a reduction in AB and therefore an improvement in the ability to more efficiently distribute limited attentional resources. Furthermore, in contrast to the Slagter et al. study, here we tested a population of older participants and show that the practice of meditation could overcome the effect of aging on attentional processes to such an extent that older practitioners show a reduction in blink as compared to young control participants. In addition, meditators had an increased ability to sustain attention at longer lags as compared to the age-matched control group. Our study was able to reveal this difference in AB performance due to the fact that our meditators stemmed from an older population, whereas the participants of the Slagter et al. study stemmed from a younger population.
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It is worthwhile to consider the mechanisms by which the type of meditation under investigation here may lead to a reduced blink of shorter duration. Both the participants of the Slagter et al. (2007) study and the practitioners of this study had gained practice in OM meditation. In the first case by participating in a 3 months intensive OM meditation retreat and in the second by practicing both FA and OM meditation more gradually over a period of years. OM meditation is thought to cultivate non-reactive awareness of experience, without focus on an object. By non-reactive is meant that the practitioner does not react to a thought or emotion when it appears but simply allows it to be and then allows it to pass without evaluating or interpreting it and without allowing a chain reaction of thoughts to take effect (Lutz et al., 2008). The current and previous results suggest that practice in OM meditation does indeed allow practitioners to more efficiently register the first target by not ‘over-investing’ in it and disengaging attention from it relatively quickly. This leaves practitioners with sufficient resources to process the second target upon detection of the first, more often than is the case for non-practitioners (Lutz et al., 2008; Slagter et al., 2007). Support for this hypothesis is found in a reduction of the amplitude of the P3b component related to the first target (Slagter et al., 2007). However, as previously mentioned, in addition to having practiced OM meditation, the meditation group in our experiment had also engaged in extensive FA meditation training. The ability to sustain attention over longer periods of time is fundamental to FA meditation practice. Presumably, this is why our meditation group showed an enhanced ability to sustain attention during the AB task in addition to a reduction in AB as compared to the agematched controls. This is reflected in the fact that the meditation group outperformed the age-matched control group at longer lags. As expected, Zen meditation practitioners, who had ample practice in FA meditation, also showed improved performance on a sustained attention task (Pagnoni & Cekic, 2007). It may be argued that motivation and not necessarily differences between groups could explain the difference in performance between the meditators and the age-matched controls. This is very unlikely because even if the meditation group found it more rewarding to take part in the experiment, performance in the AB has been found to be impervious to manipulations of motivation. Olivers and Nieuwenhuis (2005) introduced a reward condition in their AB experiment in which participants were financially rewarded according to their performance. There was no significant difference in the results between the reward group and the standard group. Only at the longest lag (corresponding to lag5 in our study) was there a slight trend towards better performance for the reward group. The authors conclude that the duration, but not the magnitude, of the AB may be reduced under conditions of higher motivation. It is however unlikely that motivation is the sole explanation of the present finding that meditators performed better at longer lags than did controls. Firstly, differences in performance between groups at longer lags reached high levels of significance in this study. Secondly, further support for the finding that meditation helps to overcome age-related cognitive decline in the ability to sustain attention can be found in the literature (Chambers, Lo, & Allen, 2008; Pagnoni & Cekic, 2007). Pagnoni and Cekic (2007) measured the individual capacity of sustained attention by means of a rapid visual information processing task and found that meditators showed virtually no age-related deterioration in performance of the task. In comparison, the control group did show an age-related reduction in target sensitivity and quickness to respond. The fact that this study and that of Slagter et al. (2007) indicate that the practice of meditation leads to a reduction in AB does not stipulate how much practice one must have undertaken in order to obtain this reduction in blink. Previous studies suggest that attentional processing begins to change relatively early in the training process (Jha et al., 2007; Tang et al., 2007). However, the course of this change in attentional processing is still rather unclear. The change could take place rather gradually or quite abruptly during the practice. We found no correlation between years of meditation experience and performance on the task (data not shown). This could be due to the small sample size of this study. Also, ideally we would have been able to correlate the amount of hours of meditation practice with performance on the task. This would have been a more exact method of correlating experience with performance. However, estimating total hours of experience proved to be rather impossible. At times participants meditated for 90 min and at other times only 15 min. There were times when they meditated only twice a week and times when they meditated every day of the week. For this reason we could not make a meaningful calculation of total hours of experience. In the future, carefully controlled longitudinal studies, with larger sample sizes will be needed in order to investigate the change in attentional processing as a function of hours of meditation practice, how this can vary between participants and meditation practices and how long this change in processing is retained after training. The present results leave these questions unanswered and should be tackled in future research. Until such data is made available, the current results should be taken with caution due to the cross-sectional design of the study. Research on perceptual learning has shown that learning can occur throughout almost the entire life span (Goldstone, 1998). Nonetheless, those forms of learning are typically fixed to the specific task trained. They require a lot of practice and have shown little generalization to novel contexts. For example, the dynamics of perceptual switches have been shown to be altered through task specific, binocular rivalry training. Binocular rivalry is a perceptual phenomenon that occurs when two different images are presented to each eye. Rather than seeing a blend of the two images participants see either the one or the other and this fluctuates randomly at a rate between 2 and 5 s. When observers experienced binocular rivalry repeatedly over many days, the rate of perceptual switches increased (Suzuki & Grabowecky, 2007). In contrast to this type of task-specific training, task unspecific training in the form of meditation can generalize to other tasks. For example, Buddhist monks showed an alteration in the inherent fluctuations in conscious state induced by binocular rivalry, both during and after meditation. These individuals, highly trained in meditation, could sustain attention on a percept for long periods of time even though in the normal population it is known to fluctuate at a rate between 2 and 5 s. Three of the tested monks even reported complete perceptual stability throughout the entire 5 min meditation period (Carter et al., 2005). The results presented here and other studies (Carter et al., 2005; Green and Bavelier, 2003; Jha et al., 2007; Slagter et al., 2007; Tang et al., 2007) further support the notion that a
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task unspecific form of training, such as meditation or video game playing, could have a general impact on a higher cognitive function such as attention. Specifically, this study supports the notion that mental training in the form of meditation leads to a reduction in AB, showing that it alters the efficiency with which attention processes elements in time. This mental training in the form of meditation seems to counteract an age-related increase in the magnitude of the AB, allowing practitioners to overcome age-related attentional cognitive decline in the temporal domain. The present findings, despite the limitations of small sample size and cross-sectional design, provide valuable evidence that internally driven mental training in the form of meditation is affecting attentional processing in some way, rendering attentional processing a plastic process and therefore subject to training effects e.g. (Green and Bavelier, 2003; Slagter et al., 2007). Acknowledgments We would like to acknowledge the Grants that financed this research: The DFG Mu 1364/2, BMBF 01GO0507, the Dr. Paul and Cilli Weill-Stiftung and the August Scheidel-Stiftung. We are grateful to Prof. Dr. Wolf Singer for giving feedback on the manuscript, to Arjen Alink for helping to program the experiment and to Verena Krupp for helping us in testing participants. We also thank the reviewers for their constructive comments. References Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B., & Davidson, R. J. (2007). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11483–11488. Carter, O. L., Presti, D. E., Callistemon, C., Ungerer, Y., Liu, G. B., & Pettigrew, J. D. (2005). Meditation alters perceptual rivalry in Tibetan Buddhist monks. Current Biology, 15(11), R412–413. Chambers, R., Lo, B. C. Y., & Allen, N. B. (2008). 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