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Regular physical activity improves executive function during task switching in young adults
International Journal of Psychophysiology 75 (2010) 304–311
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International Journal of Psychophysiology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / i j p s y c h o
Regular physical activity improves executive function during task switching in young adults Keita Kamijo ⁎, Yuji Takeda National Institute of Advanced Industrial Science and Technology (AIST), Japan
a r t i c l e
i n f o
Article history: Received 6 March 2009 Received in revised form 9 December 2009 Accepted 4 January 2010 Available online 15 Janaury 2010 Keywords: Physical activity Executive function Working memory Task switching Young adults P3 ERPs
a b s t r a c t The relationship between physical activity levels and executive control functioning was examined in 40 young adults (mean age = 21.4 yrs; 19 females) who were grouped on the basis of their regular physical activity level. Participants performed a task switching paradigm with two conditions. The pure task condition required repeated performance on a single task (e.g., AAAAAA…); the mixed-task condition required participants to change rapidly between different tasks (e.g., AABBAA…). The mixed-task condition created greater executive control requirements due to working memory demands for the maintenance of multiple task sets in memory (compared to the pure task; i.e., mixing cost) and due to requisite inhibition of a task set on switch trials (versus non-switch trials; i.e., switch costs). Task performance measures of reaction times (RTs) and the P3 component of an event-related brain potential were collected. Results show a smaller mixing cost on RTs and P3 amplitudes and a smaller switch cost on RTs for the active group relative to the sedentary group. These data suggest that when the task requires greater amounts of executive control, the physically active group demonstrates a more efficient executive functioning than the sedentary group. Thus, this research presents evidence that regular physical activity selectively improves executive function, as represented by the task switching paradigm, even during young adulthood. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Over the last two decades, electrophysiological studies examining event-related brain potentials (ERPs), and in particular the P3 component, have demonstrated cognitive benefits associated with regular physical activity for older adult populations (see Kramer and Hillman, 2006 for a review). However, ERP findings for younger adult populations have been divided regarding whether physical activity improves cognitive function (Kamijo and Takeda, 2009; Themanson and Hillman, 2006; Themanson et al., 2006; Themanson et al., 2008) or not (e.g., Dustman et al., 1990; Hillman et al., 2002; Magnié et al., 2000). Of the studies demonstrating a beneficial relationship Kamijo and Takeda (2009), Themanson and Hillman (2006), Themanson et al. (2006) and Themanson et al. (2008), show the possibility that regular physical activity selectively improves executive control processes (i.e., those processes involving working memory, inhibition, and mental flexibility; Diamond, 2006). It has been suggested that this occurs for both older and younger adults alike. Of the studies exhibiting a null relationship (Dustman et al., 1990; Hillman et al., 2002; Magnié et al., 2000), the cognitive tasks examined entailed minimal executive control for successful completion such as an odd-ball paradigm. Thus, ⁎ Corresponding author. Department of Kinesiology & Community Health, C306 Louise Freer Hall, 906 South Goodwin Avenue, University of Illinois, Urbana, IL 61801, United States. Tel.: + 1 217 333 3893; fax: + 1 217 244 7322. E-mail address: [email protected] (K. Kamijo). 0167-8760/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpsycho.2010.01.002
the effects of physical activity on cognitive function for younger adults may depend on the nature of the specific cognitive task and cognitive processes required for successful execution of the task. To date, the literature has found that the largest benefits of physical activity occur for tasks with larger executive control requirements (Colcombe and Kramer, 2003). Accordingly, task switching paradigms have been used extensively to examine executive control functions, because they require working memory, inhibition, and mental flexibility (see Monsell, 2003 for a review). Based on the finding that physical activity affects executive control, it can be assumed that the benefits of physical activity would be observed in the task switching paradigm. However, two ERP studies (Hillman et al., 2006; Scisco et al., 2008) that used this paradigm have reported contradictory findings regarding the influence of physical activity on the executive control in young adults. In the present study, we examined sources of the discrepancies between these studies. Below, the typical paradigm used in these studies is described briefly. A typical task switching paradigm consists of pure task and mixedtask conditions. In the pure task condition, participants repeatedly perform a single task (i.e., AAAAAA… or BBBBBB…). In the mixed-task condition, participants rapidly switch between two or more tasks. That is, aspects of the task require the individual to change from one task to another (e.g., ABABAB…), while other aspects of the task require the individual to repeat the same task on successive trials (e.g., AAAAAA…). Within the mixed-task condition, switch trials often cause longer reaction time latencies (RTs) compared to non-switch
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trials and the difference in RTs between switch and non-switch trials has been termed the switch cost. The switch cost is thought to reflect “task set reconfiguration” that involves the inhibition of the task set that was relevant on the previous trial as well as the activation of the currently relevant task (Monsell et al., 2003; Rogers and Monsell, 1995). Although RTs recover rapidly after a switch trial, RTs on nonswitch trials during the mixed-task condition often remain slower relative to the pure task condition (Monsell et al., 2003; Rogers and Monsell, 1995). We defined the difference in RTs between the pure task and non-switch trials in the mixed-task condition as the mixing cost in the present study. The mixing cost is believed to reflect demands of maintaining two or more task/response sets active in working memory (Kray and Lindenberger, 2000; Rogers and Monsell, 1995). Note that the computation of this type of cost (known as; mixing, global switch, general switch, or alternation cost) has varied slightly among different studies. For example, some researchers have defined this type of cost as the RT differences between the pure task and mixed-task conditions that includes both switch and non-switch trials (Kray and Lindenberger, 2000). In these cases, the difference in RT involves the switch cost as well as the mixing cost that we have described in the present study (Meiran, 2000). We have used the term, alternation cost to discriminate this type of cost calculation from the mixing cost. Previous ERP studies investigating the effects of physical activity have not measured the mixing cost, but only the alternation cost (Hillman et al., 2006; Scisco et al., 2008). We wanted to avoid latency jitter of the P3 (variations in latency from trial to trial), which might arise from differences in P3 latency between switch and non-switch trials, if the alternation cost was used (see below). Therefore, we examined the mixing cost (i.e., the difference between the pure task and non-switch trials). Hillman et al. (2006) investigated the effects of physical activity on the P3 component using the task switching paradigm in both older and younger adults. They found that overall physically active participants had shorter RTs and larger P3 amplitudes relative to sedentary participants. More importantly, P3 latencies in active individuals were shorter than in sedentary individuals only in the mixed-task condition, but not in the pure task condition (i.e., alternation cost decreased in active participants). This implies that physical activity selectively improves executive control. For this account, Scisco et al. (2008) suggested that the findings from Hillman et al. were not conclusive relative to the younger adults, because younger and older groups were collapsed to examine the selective effects on executive control. To clarify this issue, Scisco et al. investigated physical activity (i.e., cardiovascular fitness) only in younger adults and observed no effect of physical activity on RT, and P3 amplitudes, or the latency during the task switching paradigm. They concluded that the benefits of physical activity on cognitive functioning might emerge after early adulthood (Scisco et al.). Because the two studies reported inconsistent findings, it remains unclear whether physical activity, particular in young adults, influences the executive function during the task switching paradigm and if this effect is task dependent. Note that Hillman et al. examined the alternation and switch costs (global and local switch cost in Hillman et al.'s term) separately, and reported effects of physical activity on the alternation cost but not on the switch cost. As stated above, the alternation cost consists of the mixing and switch costs (Meiran, 2000). Thus, it is speculated that the effects on the mixing cost, but not on the switch cost, might have been caused by the effects of physical activity on alternation cost in Hillman et al. On the other hand, no physical activity effect on the alternation cost was observed in Scisco et al., which implies that physical activity influenced neither the mixing nor the switch costs. In summary, the physical activity effects on the switch cost were not observed in the both studies, but the effect on mixing cost remains controversial. The purpose of the present study is to clarify sources of the discrepancies between the previous P3 studies (Hillman et al., 2006; Scisco et al., 2008). It is possible that these studies included
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methodologies that underestimate effects of physical activity on executive control. If so, this may explain these contradictory findings. The first methodological point requiring consideration concerns averaging across different tasks to obtain a P3 component. Hillman et al. used two tasks involving single digit numbers (digits 1–9, excluding 5) requiring a distinction between whether the number was greater or lesser than 5 (low/high task) or, whether the number was odd or even (odd/even task). Scisco et al. used four tasks involving double digits numbers (digits 11–99). Participants were instructed to discern whether numbers were greater or lesser than 50, whether the numbers were odd or even, whether the sum of two digits was greater or lesser than 10, or whether the sum of the two digits was odd or even. In both studies the resulting ERPs were averaged across these different tasks, in spite of the fact that task difficulty could vary among the presented judgment tasks. Moreover, as mentioned above, in the both studies, the alternation cost without differentiating between switch and non-switch trials was used instead of the mixing cost. It has been extensively shown that P3 amplitude and latency are affected by task difficulty (e.g., McCarthy and Donchin, 1981; Polich, 1987). Latency jitter arises from differences in task difficulty (Kok, 2001). With regard to latency jitter, trials for each task (e.g., low/high vs. odd/even) and each switch condition (i.e., switch vs. non-switch) should be averaged separately, because P3 latencies would likely differ across tasks and switch conditions as a function of difficulty. Thus, in ERP studies, ideally, each task should be analyzed separately. Moreover, the mixing cost may be a better measure than the alternation cost due to the aforementioned ERP component latency jitter. For this reason, the methodology and analyses inherent in the task switching paradigm used in Hillman et al. and Scisco et al. could underestimate the physical activity affects on executive function. The second methodological point concerns counter-balanced order. In both of the previous P3 studies, participants always began the pure task condition before the mixed-task condition to learn each task rule in the previous P3 studies (Hillman et al., 2006; Scisco et al., 2008). The practice effects by the non-counter-balanced order between conditions (i.e., pure task condition to mixed-task condition) could also underestimate the mixing cost. The reduction in mixing cost might lead to a failure to detect the significant relation of physical activity. In the present study, we investigated whether physical activity improves executive function during task switching in young adults using behavioral measures and the P3 component. We used a task switching paradigm with two tasks (low/high vs. odd/even) based on Hillman et al. (2006), and performed separate analyses for each task. In addition, we used the mixing cost instead of the alternation cost and counterbalanced the order of conditions (i.e., pure task and mixed-task). If the aforementioned methodologies of prior studies (Hillman et al.; Scisco et al., 2008) contributed to underestimation of effects of physical activity on executive control, then we should observe stronger effects of physical activity in the present study. In such a case, the mixing cost, revealed by RT and P3 amplitude and/or latency, should be smaller in the active group than in the sedentary group (i.e. indicating selective effects on executive function).
2. Methods 2.1. Participants Forty undergraduate and graduate students (mean age = 21.4± 0.3 yrs; 19 females, 21 males) were recruited from the University of Tsukuba. All participants reported being free of neurological disorders, cardiovascular disease, any medications that influenced central nervous system function, and had (corrected- to-) normal vision. None of the participants exhibited signs of depression, assessed by the Beck Depression Inventory (Beck, et al., 1961; see Table 1). The participants gave informed consent to participate in the experiment, which was
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Table 1 Group means for participant characteristics.
Female/male Age (years)* Height (cm) Weight (kg) Beck Depression Inventory IPAQ total PA score (kcal/week)* IPAQ leisure-time domain sub-score (kcal/week)* IPAQ vigorous-intensity sub-score (kcal/week)*
Active
Sedentary
10/10 20.4 ± 0.3 168.6 ± 1.6 60.1 ± 1.8 4.9 ± 0.8 9229.3 ± 1047.4 6592.1 ± 928.7
9/11 22.3 ± 0.4 165.8 ± 2.0 58.1 ± 2.2 6.7 ± 1.2 1698.2 ± 246.2 582.4 ± 153.1
5796.0 ± 870.4
336.0 ± 127.2
Values are mean±SE. The maximum obtainable score on the Beck Depression Inventory is 63. *Significant difference, unpaired Student's t-test between groups, p < .05.
approved by the Institutional Human Research Committee (National Institute of Advanced Industrial Science and Technology). Participants were differentiated into two groups on the basis of their regular level of physical activity: active and sedentary. The participants' regular physical activity levels were evaluated using the International Physical Activity Questionnaire (IPAQ) long form. The IPAQ was developed as an instrument for cross-national monitoring of physical activity levels, and has been validated in different sociocultural settings (Craig et al., 2003; Hagströmer et al., 2006). IPAQ assesses physical activity undertaken across a comprehensive set of domains including: leisure-time physical activity, domestic and gardening (yard) activities, work-related physical activity, and transport-related physical activity (http://www.ipaq.ki.se). Furthermore, walking, moderate-intensity, and vigorous-intensity sub-scores can be also calculated using the IPAQ. The characteristics of the active and sedentary groups are summarized in Table 1. In Table 1, the leisure-time domain and vigorousintensity sub-scores, which are considered influential factors for cognitive improvement (Hillman et al., 2004), are indicated in addition to the total physical activity score. These IPAQ scores (i.e., physical activity levels) were significantly different between the active and sedentary groups. Lastly, participants' age was significantly different between the groups (mean difference = 1.9 yrs). It is known that from the third to the eighth decade of life, the P3 amplitude decreases with age at a rate of about 0.2 µV per year, whereas the P3 latency increases with age at a rate of about 1.4 ms per year (Picton et al., 1984). In the present study, the age difference between the groups was considered negligible small. Indeed, no significant correlation between age and RT (r = .25, p = .13), age and P3 amplitude (r =−.11, p = .48), or age and P3 latency (r = .11, p = .51) was observed. 2.2. Procedure After providing informed consent, the participant completed the IPAQ and Beck Depression Inventory. Individual participants were seated in a comfortable chair and prepared for neuroelectric measurement in accordance with the Society for Psychophysiological Research guidelines (Picton et al., 2000). Electroencephalograms (EEGs) were measured from the following nine electrode sites of the International 10–20 system: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, with AFz as the ground electrode, and impedances were kept below 5 kΩ. To monitor possible artifacts due to eye movements, vertical electrooculogram (EOG) was recorded using electrodes placed above and below the right eye, and a horizontal EOG was recorded from the outer left and right canthi. 2.3. Task switching paradigm Based on Hillman et al. (2006), the task switching paradigm included two tasks. A white numeric digit (digits 1–9, excluding 5) was presented in the center of the computer screen on a black background. The viewing
distance was approximately 57.3 cm. Each digit was surrounded by a solid or dashed square. Each square subtended vertical and horizontal visual angles of 4.6°. The participants made judgments as to whether the digit was lesser or greater than 5 to the digit surrounded by a solid square (low/high task), or whether the digit was odd or even to the digit surrounded by a dashed square (odd/even task). The participants were instructed to press, as quickly and accurately as possible, one of two keys on the computer keyboard corresponding to the digit. The participants used their index fingers of each hand to make response on the keys X (low/odd) and M (high/even) of a standard keyboard. Digits were presented for 200 ms, with a 1500 ms response-stimulus interval. If no response was made, the trial terminated 3000 ms after the onset of stimuli. Participants received two pure task conditions and a mixed-task condition. In the pure task conditions, only one task was performed (i.e., AAAAAA… BBBBBB…). In the mixed-task condition, the alternatingruns paradigm (Rogers and Monsell, 1995) was used (AABBAA…). That is, the task changed predictably every two trials. Increases in predictability by using the alternation-runs are thought to have increased working memory demands, because participants had to keep track of the current and upcoming tasks. Therefore, alternatingruns may be more sensitive for detecting subtle differences in cognitive functions between groups, compared to random cuing procedure used in previous ERP studies (Hillman et al., 2006; Scisco et al., 2008). Participants completed 64 trials in each of the pure task condition and 256 trials (64 trials × 4 blocks) in the mixed-task condition. The first trial in each block for both pure task and mixed-task condition was discarded from the analyses. All 8 digits appeared with equal probability in a random order. The order of conditions (i.e., pure task or mixed-task) and tasks (i.e., low/high or odd/even in the pure task condition) were counterbalanced among the participants to minimize potential practice effects. The participant was given the task instructions and allowed 64 practice trials before the each experimental task. The task duration of 1 block (64 trials) was about 2.5 min. 2.4. Data reduction The EEG data were re-referenced to mathematically averaged earlobes (A1–A2) offline. The bandpass filter was set at 0.1 Hz to 30 Hz. Data were converted from 100 ms pre-stimulus to 1200 ms post-stimulus were digitized at a sampling rate of 1000 Hz. Trials with eye blinks, eye movements (rejection levels: ± 80 μV) and response errors were excluded from the analyses. Based on visual inspection, trials were rejected due to artifacts during offline analysis, in addition to the automatic rejection. On average, about 35% of trials were discarded due to artifacts and response errors (details in Table 2). ERP potentials were obtained for separate averages for each task (i.e., low/ high or odd/even). ERP components were measured relative to a 100 ms pre-stimulus baseline. The P3 latency was measured at the most dominant site (i.e., Pz). The amplitude of the corresponding peak latency was measured at midline electrode sites (Fz, Cz, and Pz). The time window for peak detection of the P3 component was 250– 600 ms. 2.5. Statistical analysis Separate analyses were conducted to examine mixing and switch effects. Mixing effect analyses examined differences between the pure task condition and non-switch trials in the mixed-task condition, and switch effect analyses examined differences between switch and nonswitch trials in the mixed-task condition. A mixed-model analysis of variance (ANOVA) was used to analyze RT, error rate, and P3 latency with the following factors: 2 (Group: active vs. sedentary) × 2 (Condition: non-switch vs. pure for mixing effect, or switch vs. nonswitch for switch effect) × 2 (Task: low/high vs. odd/even). Angular transformation was applied to the error rate data before the analysis. P3 amplitude was analyzed using a 2 (Group) × 2 (Condition) × 2 (Task)×
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Table 2 Group means for reaction time, error rate, P3 amplitude and latency, and number of trials for ERP averaging during each condition and task. Pure
Mixed Non-switch
Reaction time (ms) Error rate (%) P3 amplitude (μV) P3 latency (ms) Number of trials for ERP averaging (n)
Active Sedentary Active Sedentary Active Sedentary Active Sedentary Active Sedentary
Switch
Low/high
Odd/even
Low/high
Odd/even
Low/high
Odd/even
447.4 ± 10.7 471.4 ± 17.4 3.6 ± 0.6 2.2 ± 0.5 11.8 ± 1.2 9.5 ± 1.1 398.0 ± 13.9 399.0 ± 10.3 39.7 ± 2.3 41.9 ± 2.1
494.3 ± 11.4 566.4 ± 31.4 5.3 ± 0.8 4.4 ± 1.2 11.5 ± 1.1 8.5 ± 1.1 423.7 ± 13.8 402.5 ± 12.7 40.4 ± 2.0 40.4 ± 2.3
508.6 ± 23.9 610.5 ± 35.9 4.1 ± 0.8 3.5 ± 0.8 12.1 ± 1.4 7.7 ± 0.9 406.1 ± 13.7 415.5 ± 12.4 37.6 ± 2.4 40.0 ± 2.3
561.7 ± 23.0 660.8 ± 44.1 5.6 ± 0.8 4.8 ± 1.2 11.2 ± 1.3 8.0 ± 1.2 428.4 ± 17.4 423.9 ± 10.2 37.4 ± 2.4 39.9 ± 2.2
609.1 ± 30.5 772.7 ± 50.8 6.0 ± 0.9 4.8 ± 1.1 9.3 ± 1.3 5.7 ± 0.7 392.7 ± 17.8 416.6 ± 20.3 37.6 ± 2.0 39.0 ± 2.2
667.5 ± 33.2 848.3 ± 67.6 7.9 ± 1.1 8.0 ± 1.6 9.6 ± 1.2 5.6 ± 0.9 394.0 ± 19.3 420.2 ± 17.2 42.2 ± 1.7 42.6 ± 2.0
Values are mean ± SE. The Region factor was collapsed for the P3 amplitudes in this table. The P3 latency was measured at the most dominant site (Pz).
3 (Region: Fz vs. Cz vs. Pz) mixed-model ANOVA. The reported significances for the F values were those obtained after Greenhouse– Geisser correction as appropriate and this is indicated by a correction coefficient epsilon (ε). Post hoc comparisons were conducted using univariate ANOVA and Tukey's HSD multiple-comparison test. The participant characteristics shown in Table 1 were analyzed using unpaired Student's t-tests. The significance level was set at .05. 3. Results Table 2 shows group means for RT, error rate, P3 amplitude and latency, and number of trials for ERP averaging during each condition and task. The results of the omnibus ANOVAs are shown in Table 3. The follow up post hoc analyses are reported below. 3.1. Mixing effect 3.1.1. Behavioral measures The RT analysis revealed a Group × Condition × Task interaction [F(1, 38) = 5.7, p = .02, η2p = .13]. Breaking down the 3-way interaction by examining Group × Condition for each task (i.e., low/high or odd/even) revealed a significant Group × Condition interaction during the low/high task [F(1, 38) = 8.0, p = .01, η2p = .17]. Post hoc analyses indicated that the group difference was only observed during the mixed-task condition [t(1, 38) = 2.4, p = .02], whereas no such effect was found during the pure task condition [t(1, 38) = 1.2, p = .25]. No such interaction was observed during the odd/even task [F(1, 38)= 1.1, p = .31, η2p = .03] (see Fig. 1).
3.1.2. P3 Component Fig. 2 shows the grand averaged ERP waveforms across groups and tasks for each condition. The P3 amplitude analysis revealed a Group × Condition × Task interaction [F(1, 38) = 5.1, p = .02, η2p = .12]. Breaking down the 3-way interaction by examining Group × Condition for each task (i.e., low/high or odd/even) revealed a significant Group × Condition interaction during the low/high task [F (1, 38) = 5.6, p = .02, η2p = .13]. Post hoc analyses indicated that P3 amplitude during the pure task condition was larger compared to the mixed-task condition only for the sedentary group [t(1, 19) = 3.2, p = .01], whereas no such effect was found for the active group [t(1, 19) = .5, p = .65]. No such interaction was observed during the odd/even task [F(1, 38) = .01, p = .91, η2p < .001] (see Fig. 3). A main effect for Region was also observed [F(2, 76) = 43.3, p < .001, ε = .75, η2p = .23] with follow up Tukey's HSD post hoc analyses indicating that P3 amplitude at Pz (M = 12.2 ± 0.9 μV) was larger than Fz (M = 7.0 ± 0.7 μV) and Cz (M = 10.8 ± 1.0 μV), and Cz was larger than Fz [ts(1, 39) ≥ 2.7, ps ≤ .05]. This main effect was superseded by a Condition × Region interaction [F(2, 76) = 6.3, p = .01, ε = .84, η2p = .14]. Post hoc analyses indicated that P3 amplitude during the pure task condition was larger than the mixed-task condition at Pz [t(1, 39) = 2.3, p = .03]. No such difference was observed at Fz and Cz. 3.2. Switch effect 3.2.1. Behavioral measures The RT analysis revealed a Group × Condition interaction [F(1, 38)= 7.2, p = .01, η2p = .16]. Post hoc analyses indicated that the condition
Table 3 ANOVA table (F-value) for reaction time, error rate, and P3 amplitude and latency. Mixing effect
Group (1, 38) Condition (1, 38) Task (1, 38) Region (2, 76) G × C (1, 38) G × T (1, 38) G × R (2, 76) C × T (1, 38) C × R (2, 76) T × R (2, 76) G × C × T (1, 38) G × C × R (2, 76) G × T × R (2, 76) C × T × R (2, 76) G × C × T × R (2, 76)
Switch effect
Reaction time
Error rate
P3 amplitude
P3 latency
Reaction time
Error rate
P3 amplitude
P3 latency
4.5* 53.8*** 55.2*** – 4.5* N.S. – N.S. – – 5.7* – – – –
N.S. N.S. 11.0** – N.S. N.S. – N.S. – – N.S. – – – –
4.5* N.S. N.S. 43.3*** N.S. N.S. N.S. N.S. 6.3** N.S. 5.1* N.S. N.S. N.S. N.S.
N.S. N.S. 4.2* – N.S. N.S. – N.S. – – N.S. – – – –
6.1* 108.4*** 34.6*** – 7.2* N.S. – N.S. – – N.S. – – – –
N.S. 27.5*** 8.3** – N.S. N.S. – N.S. – – N.S. – – – –
6.2* 38.7*** N.S. 34.3*** N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S.
N.S. N.S. N.S. – N.S. N.S. – N.S. – – N.S. – – – –
*p < 0.05, **p < 0.01, and ***p < 0.001.
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Fig. 1. Mean RTs across groups and conditions for each task in the mixing (upper) and the switch (lower) effects. Error bars indicate SEs.
difference (i.e., switch cost) was observed for both active and sedentary groups [ts(1, 19) ≥ 7.6, ps< .001]; however, the condition difference was larger for the sedentary group (see Fig. 1). 3.2.2. P3 component The P3 amplitude analysis revealed a Region main effect [F(2, 76) = 34.3, p < .001, η2p = .48] with a follow up Tukey's HSD post hoc analyses indicating that P3 amplitude at Pz (M = 10.6± 0.9 μV) was larger than Fz (M = 6.2 ± 0.7 μV) and Cz (M = 9.2 ± 1.0 μV); with Cz larger than Fz [ts(1, 39) ≥ 3.0, ps≤ .03]. 4. Discussion The present study was designed to clarify whether physical activity influences executive control function in young adults during a task switching paradigm. To this end, separate analyses for each two types of judgment tasks (i.e., low/high or odd/even) were conducted. The results showed shorter RTs and lower error rates during the low/high task compared to the odd/even task, indicating that task difficulty differed between the two tasks with the low/high task characterized as being easier than the odd/even task. During the pure task, amplitude of the P3 ERP component was larger than during the mixed-task condition only for the sedentary
group, no parallel differences were evident in ERP data of the active group (see Fig. 3). The P3 component is believed to represent the updating of working memory (Donchin, 1981). The amplitude of this component is proportional to the amount of attentional resources devoted to a given task (Kramer and Strayer, 1988; Wickens et al., 1983). In addition, it is well known that P3 amplitude is sensitive to mental workload, with amplitude decreases as a function of increases in memory load (see Kok, 2001 for a review). In ERP studies using the task switching paradigm, it was shown that P3 amplitude during the pure task condition was larger than during the mixed-task condition (e.g., Jost et al., 2008; Goffaux et al., 2006), suggesting that greater requirement of working memory demands in the mixed-task condition leads to a lesser amount of available attentional resources compared to the condition with lower working memory load (i.e., pure task condition). Accordingly, in the present study, we believe that the smaller P3 amplitude during the mixed-task relative to the pure task condition in the sedentary group is resulting from greater working memory demands (i.e., fewer available resources) during the mixed-task condition. In contrast, the active individuals may better allocate available attentional resources compared to the sedentary individuals even in the mixed-task condition, thereby no differences in P3 amplitude between conditions were observed.
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Fig. 2. Grand averaged ERP waveforms across groups and tasks for each condition.
Interestingly, the effect of physical activity on the P3 amplitude for the mixing cost was selective in that it was only observed with the low/high task, but not for the odd/even task (see Fig. 3). These differences in P3 amplitudes, due to physical activity level and task conditions, are consistent with the RT results. That is, a selective effect of physical activity for the mixing cost was also observed for RT only with the low/high task (see Fig. 1). One possible explanation for the task-dependent effects is that working memory load (i.e., executive control requirements) might be different between the tasks. It is plausible that the more difficult task required greater amounts of executive control. For simplicity, assume that the working memory load for maintaining the odd/even task (i.e., more difficult task) was 2 and that for maintaining the low/high task (i.e., easier task) was 1. That is, the working memory load in the mixed-task condition was 3 (i.e., 2 + 1). Based on this assumption, the additional working memory load in the mixed-task condition, when compared to the pure task condition (i.e., mixing cost), would be greater in the low/ high task trials (i.e., 3–1 = 2) than in the odd/even task trials (i.e., 3– 2 = 1), and therefore the selective effects of physical activity on the mixing cost were only observed in the low/high task. In other words, if the difference in working memory load between tasks were relatively small, the selective effects of physical activity might be minimized at least in young adults. Thus, working memory demands might influence the relationship of physical activity in young adults as well as older adults (Hillman et al., 2008; Kramer and Hillman, 2006). As mentioned previously, the contradictory findings regarding the relationship between physical activity and task switching in prior research reporting P3 data (Hillman et al., 2006; Scisco et al., 2008)
may be the result of combined averaging across tasks (i.e., the latency jitter). We hypothesized that this could result in underestimation of the effects of different levels of physical activity on the P3 component. In the present study, P3 latencies were different between tasks for the mixing effect, with shorter P3 latencies during the low/high task compared to the odd/even task. Smaller P3 peak amplitude could have resulted from the variability in the P3 latency (Kok, 2001). Indeed, even in the present study, a P3 amplitude analysis of combined averaging with collapsed task difficulty (i.e., low/high and odd/even) for the mixing effect revealed no significant Group main effect (p = .06) or Group × Condition interaction (p = .23). Thus, collapsing analyses across tasks with variable amounts of difficulty (Hillman et al.; Scisco et al.) might result in an underestimation of the physical activity effect on cognition and therefore account for such contradictory findings. With regard to the switch effect (i.e., a difference between switch and non-switch trials within the mixed-task), this effect on RTs was smaller for the active group than for the sedentary group irrespective of tasks. The switch cost is also considered to reflect executive control including inhibition and mental flexibility (Monsell et al., 2003; Rogers and Monsell, 1995). That is, our findings with respect to switch cost may provide further evidence that physical activity selectively influences executive control. The switch cost may function better than mixing cost as an index for detecting effects of physical activity on executive control, because it has been proposed that the mixing cost results from differences between the task conditions that involve not only differential working memory demands, but also differences in arousal (Rogers and Monsell) and task strategy (De Jong, 1995).
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Fig. 3. Mean P3 amplitudes across groups and conditions for each task in the mixing (upper) and the switch (lower) effects. The Region factor is collapsed in this figure. Error bars indicate SEs.
As mentioned in the Introduction, Hillman et al. (2006) did not demonstrate the physical activity effect on the switch cost. The main difference between the present study and Hillman et al. related to the mixed-task condition was the predictability of the subsequent task. That is, in the present study, the two tasks were presented as an alternatingruns sequence (i.e., AABBAA...), whereas in Hillman et al.'s study, the task randomly changed between trials. Lorist et al. (2000) indicated that contingent negative variation (CNV), a negative slow-wave cortical potential of an ERP reflecting the task preparation processes, was enhanced in the frontal region for preceding expected switch trials, compared to non-switch trials using the alternating-runs sequence, suggesting that switch trials require more demanding task preparation if the subsequent task is predictable. Further, Kamijo et al. (in press) investigated the relation of physical activity to task preparation using CNV, which indicated that task preparation could be accomplished more efficiently in active individuals than less active individuals. Taken together, one possible explanation for the physical activity effect on the switch cost might be that task set preparation becomes more efficient in switch trials because of the knowledge of the subsequent task during the alternating-runs sequence. Note that, for reason not clearly understood, the physical activity effect on switch cost (i.e., Group× Condition interaction) was observed only for RTs, but not for P3 measures. Thus,
further studies are needed to understand how physical activity affects the switch cost. It is well known that the P3 can be separated into two subcomponents: P3a and P3b. P3a exhibits a front-central scalp distribution compared to the more parietal distribution of P3b, and P3a has shorter peak latency (see Polich, 2007 for review). Scisco et al. (2008) investigated the effects of physical activity using both P3a and P3b during the task switching paradigm, indicating no effects for either subcomponent. In the present study, P3 amplitude reached its maximum at Pz and no positive peak was observed at front-central region during the possible latency of P3a, indicating that P3a did not appear or was negligible. Consequently, we only focused on the P3 component with maximum at Pz, which corresponds to the P3b. This inconsistency in the P3 subcomponents between the present study and Scisco et al. may be due to the differences in task difficulty and the stimulus duration (200 ms or until response, respectively), as the P3 component is sensitive to both task difficulty (McCarthy and Donchin, 1981; Polich, 1987) and stimulus duration (Polich, 1996). Several previous ERP studies have used a pre-cuing paradigm, in which cue indicating relevant tasks (i.e., switch or non-switch) were presented in advance of a target stimulus. The pre-cuing paradigm allows us to examine the processes of task set updating by the cues,
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separately from target identification. For example, Jost et al. (2008) indicated that cue-locked P3 amplitude was larger for the mixed-task condition than the pure task condition. Meanwhile, target-locked P3 amplitude is generally larger during the pure task condition than during the mixed-task condition (Jost et al.; Goffaux et al., 2006). In the present study, we did not use the pre-cuing paradigm to conform previous P3 studies that investigated the physical activity effect (Hillman et al., 2006; Scisco et al., 2008), because the present purpose was to elucidate sources of discrepancies between these studies. It may be that the physical activity effects are different depending on the processes of task set updating, or target identification (i.e., cue-locked P3 or target-locked P3). Further studies are needed to discern the physical activity effects on these processes using the pre-cuing paradigm. In conclusion, regular physical activity improves executive control function as represented by the task switching paradigm in young adulthood. As suggested by Hillman et al. (2008), there is little room for physical activity related improvement to cognitive function during young adulthood. Therefore, we propose that cognitive tasks evaluating the influence of physical activity should be selected with caution, especially for young adults, to avoid underestimation of the beneficial effects. Acknowledgements The authors thank Dr. Charles Hillman for his helpful comments on the earlier drafts of this paper. References Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J., 1961. An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561–571. Colcombe, S., Kramer, A.F., 2003. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychol. Sci. 14, 125–130. Craig, C.L., Marshall, A.L., Sjostrom, M., Bauman, A.E., Booth, M.L., Ainsworth, B.E., Pratt, M., Ekelund, U., Yngve, A., Sallis, J.F., Oja, P., 2003. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35, 1381–1395. De Jong, R., 1995. Strategical determinants of compatibility effects with task uncertainty. Acta Psychol. 88, 187–207. Diamond, A., 2006. The Early Development of Executive Functions. In: Bialystok, E., Craik, F.I.M. (Eds.), Lifespan Cognition: Mechanisms of Change. Oxford University Press, New York, pp. 70–95. Donchin, E., 1981. Presidential address, 1980. Surprise!...Surprise? Psychophysiology 18, 493–513. Dustman, R.E., Emmerson, R.Y., Ruhling, R.O., Shearer, D.E., Steinhaus, L.A., Johnson, S.C., Bonekat, H.W., Shigeoka, J.W., 1990. Age and fitness effects on EEG, ERPs, visual sensitivity, and cognition. Neurobiol. Aging 11, 193–200. Goffaux, P., Phillips, N.A., Sinai, M., Pushkar, D., 2006. Behavioural and electrophysiological measures of task switching during single and mixed-task conditions. Biol. Psychol. 72, 278–290. Hagströmer, M., Oja, P., Sjöström, M., 2006. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 9, 755–762. Hillman, C.H., Weiss, E.P., Hagberg, J.M., Hatfield, B.D., 2002. The relationship of age and cardiovascular fitness to cognitive and motor processes. Psychophysiology 39, 303–312.
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International Journal of Psychophysiology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / i j p s y c h o
Regular physical activity improves executive function during task switching in young adults Keita Kamijo ⁎, Yuji Takeda National Institute of Advanced Industrial Science and Technology (AIST), Japan
a r t i c l e
i n f o
Article history: Received 6 March 2009 Received in revised form 9 December 2009 Accepted 4 January 2010 Available online 15 Janaury 2010 Keywords: Physical activity Executive function Working memory Task switching Young adults P3 ERPs
a b s t r a c t The relationship between physical activity levels and executive control functioning was examined in 40 young adults (mean age = 21.4 yrs; 19 females) who were grouped on the basis of their regular physical activity level. Participants performed a task switching paradigm with two conditions. The pure task condition required repeated performance on a single task (e.g., AAAAAA…); the mixed-task condition required participants to change rapidly between different tasks (e.g., AABBAA…). The mixed-task condition created greater executive control requirements due to working memory demands for the maintenance of multiple task sets in memory (compared to the pure task; i.e., mixing cost) and due to requisite inhibition of a task set on switch trials (versus non-switch trials; i.e., switch costs). Task performance measures of reaction times (RTs) and the P3 component of an event-related brain potential were collected. Results show a smaller mixing cost on RTs and P3 amplitudes and a smaller switch cost on RTs for the active group relative to the sedentary group. These data suggest that when the task requires greater amounts of executive control, the physically active group demonstrates a more efficient executive functioning than the sedentary group. Thus, this research presents evidence that regular physical activity selectively improves executive function, as represented by the task switching paradigm, even during young adulthood. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Over the last two decades, electrophysiological studies examining event-related brain potentials (ERPs), and in particular the P3 component, have demonstrated cognitive benefits associated with regular physical activity for older adult populations (see Kramer and Hillman, 2006 for a review). However, ERP findings for younger adult populations have been divided regarding whether physical activity improves cognitive function (Kamijo and Takeda, 2009; Themanson and Hillman, 2006; Themanson et al., 2006; Themanson et al., 2008) or not (e.g., Dustman et al., 1990; Hillman et al., 2002; Magnié et al., 2000). Of the studies demonstrating a beneficial relationship Kamijo and Takeda (2009), Themanson and Hillman (2006), Themanson et al. (2006) and Themanson et al. (2008), show the possibility that regular physical activity selectively improves executive control processes (i.e., those processes involving working memory, inhibition, and mental flexibility; Diamond, 2006). It has been suggested that this occurs for both older and younger adults alike. Of the studies exhibiting a null relationship (Dustman et al., 1990; Hillman et al., 2002; Magnié et al., 2000), the cognitive tasks examined entailed minimal executive control for successful completion such as an odd-ball paradigm. Thus, ⁎ Corresponding author. Department of Kinesiology & Community Health, C306 Louise Freer Hall, 906 South Goodwin Avenue, University of Illinois, Urbana, IL 61801, United States. Tel.: + 1 217 333 3893; fax: + 1 217 244 7322. E-mail address: [email protected] (K. Kamijo). 0167-8760/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpsycho.2010.01.002
the effects of physical activity on cognitive function for younger adults may depend on the nature of the specific cognitive task and cognitive processes required for successful execution of the task. To date, the literature has found that the largest benefits of physical activity occur for tasks with larger executive control requirements (Colcombe and Kramer, 2003). Accordingly, task switching paradigms have been used extensively to examine executive control functions, because they require working memory, inhibition, and mental flexibility (see Monsell, 2003 for a review). Based on the finding that physical activity affects executive control, it can be assumed that the benefits of physical activity would be observed in the task switching paradigm. However, two ERP studies (Hillman et al., 2006; Scisco et al., 2008) that used this paradigm have reported contradictory findings regarding the influence of physical activity on the executive control in young adults. In the present study, we examined sources of the discrepancies between these studies. Below, the typical paradigm used in these studies is described briefly. A typical task switching paradigm consists of pure task and mixedtask conditions. In the pure task condition, participants repeatedly perform a single task (i.e., AAAAAA… or BBBBBB…). In the mixed-task condition, participants rapidly switch between two or more tasks. That is, aspects of the task require the individual to change from one task to another (e.g., ABABAB…), while other aspects of the task require the individual to repeat the same task on successive trials (e.g., AAAAAA…). Within the mixed-task condition, switch trials often cause longer reaction time latencies (RTs) compared to non-switch
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trials and the difference in RTs between switch and non-switch trials has been termed the switch cost. The switch cost is thought to reflect “task set reconfiguration” that involves the inhibition of the task set that was relevant on the previous trial as well as the activation of the currently relevant task (Monsell et al., 2003; Rogers and Monsell, 1995). Although RTs recover rapidly after a switch trial, RTs on nonswitch trials during the mixed-task condition often remain slower relative to the pure task condition (Monsell et al., 2003; Rogers and Monsell, 1995). We defined the difference in RTs between the pure task and non-switch trials in the mixed-task condition as the mixing cost in the present study. The mixing cost is believed to reflect demands of maintaining two or more task/response sets active in working memory (Kray and Lindenberger, 2000; Rogers and Monsell, 1995). Note that the computation of this type of cost (known as; mixing, global switch, general switch, or alternation cost) has varied slightly among different studies. For example, some researchers have defined this type of cost as the RT differences between the pure task and mixed-task conditions that includes both switch and non-switch trials (Kray and Lindenberger, 2000). In these cases, the difference in RT involves the switch cost as well as the mixing cost that we have described in the present study (Meiran, 2000). We have used the term, alternation cost to discriminate this type of cost calculation from the mixing cost. Previous ERP studies investigating the effects of physical activity have not measured the mixing cost, but only the alternation cost (Hillman et al., 2006; Scisco et al., 2008). We wanted to avoid latency jitter of the P3 (variations in latency from trial to trial), which might arise from differences in P3 latency between switch and non-switch trials, if the alternation cost was used (see below). Therefore, we examined the mixing cost (i.e., the difference between the pure task and non-switch trials). Hillman et al. (2006) investigated the effects of physical activity on the P3 component using the task switching paradigm in both older and younger adults. They found that overall physically active participants had shorter RTs and larger P3 amplitudes relative to sedentary participants. More importantly, P3 latencies in active individuals were shorter than in sedentary individuals only in the mixed-task condition, but not in the pure task condition (i.e., alternation cost decreased in active participants). This implies that physical activity selectively improves executive control. For this account, Scisco et al. (2008) suggested that the findings from Hillman et al. were not conclusive relative to the younger adults, because younger and older groups were collapsed to examine the selective effects on executive control. To clarify this issue, Scisco et al. investigated physical activity (i.e., cardiovascular fitness) only in younger adults and observed no effect of physical activity on RT, and P3 amplitudes, or the latency during the task switching paradigm. They concluded that the benefits of physical activity on cognitive functioning might emerge after early adulthood (Scisco et al.). Because the two studies reported inconsistent findings, it remains unclear whether physical activity, particular in young adults, influences the executive function during the task switching paradigm and if this effect is task dependent. Note that Hillman et al. examined the alternation and switch costs (global and local switch cost in Hillman et al.'s term) separately, and reported effects of physical activity on the alternation cost but not on the switch cost. As stated above, the alternation cost consists of the mixing and switch costs (Meiran, 2000). Thus, it is speculated that the effects on the mixing cost, but not on the switch cost, might have been caused by the effects of physical activity on alternation cost in Hillman et al. On the other hand, no physical activity effect on the alternation cost was observed in Scisco et al., which implies that physical activity influenced neither the mixing nor the switch costs. In summary, the physical activity effects on the switch cost were not observed in the both studies, but the effect on mixing cost remains controversial. The purpose of the present study is to clarify sources of the discrepancies between the previous P3 studies (Hillman et al., 2006; Scisco et al., 2008). It is possible that these studies included
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methodologies that underestimate effects of physical activity on executive control. If so, this may explain these contradictory findings. The first methodological point requiring consideration concerns averaging across different tasks to obtain a P3 component. Hillman et al. used two tasks involving single digit numbers (digits 1–9, excluding 5) requiring a distinction between whether the number was greater or lesser than 5 (low/high task) or, whether the number was odd or even (odd/even task). Scisco et al. used four tasks involving double digits numbers (digits 11–99). Participants were instructed to discern whether numbers were greater or lesser than 50, whether the numbers were odd or even, whether the sum of two digits was greater or lesser than 10, or whether the sum of the two digits was odd or even. In both studies the resulting ERPs were averaged across these different tasks, in spite of the fact that task difficulty could vary among the presented judgment tasks. Moreover, as mentioned above, in the both studies, the alternation cost without differentiating between switch and non-switch trials was used instead of the mixing cost. It has been extensively shown that P3 amplitude and latency are affected by task difficulty (e.g., McCarthy and Donchin, 1981; Polich, 1987). Latency jitter arises from differences in task difficulty (Kok, 2001). With regard to latency jitter, trials for each task (e.g., low/high vs. odd/even) and each switch condition (i.e., switch vs. non-switch) should be averaged separately, because P3 latencies would likely differ across tasks and switch conditions as a function of difficulty. Thus, in ERP studies, ideally, each task should be analyzed separately. Moreover, the mixing cost may be a better measure than the alternation cost due to the aforementioned ERP component latency jitter. For this reason, the methodology and analyses inherent in the task switching paradigm used in Hillman et al. and Scisco et al. could underestimate the physical activity affects on executive function. The second methodological point concerns counter-balanced order. In both of the previous P3 studies, participants always began the pure task condition before the mixed-task condition to learn each task rule in the previous P3 studies (Hillman et al., 2006; Scisco et al., 2008). The practice effects by the non-counter-balanced order between conditions (i.e., pure task condition to mixed-task condition) could also underestimate the mixing cost. The reduction in mixing cost might lead to a failure to detect the significant relation of physical activity. In the present study, we investigated whether physical activity improves executive function during task switching in young adults using behavioral measures and the P3 component. We used a task switching paradigm with two tasks (low/high vs. odd/even) based on Hillman et al. (2006), and performed separate analyses for each task. In addition, we used the mixing cost instead of the alternation cost and counterbalanced the order of conditions (i.e., pure task and mixed-task). If the aforementioned methodologies of prior studies (Hillman et al.; Scisco et al., 2008) contributed to underestimation of effects of physical activity on executive control, then we should observe stronger effects of physical activity in the present study. In such a case, the mixing cost, revealed by RT and P3 amplitude and/or latency, should be smaller in the active group than in the sedentary group (i.e. indicating selective effects on executive function).
2. Methods 2.1. Participants Forty undergraduate and graduate students (mean age = 21.4± 0.3 yrs; 19 females, 21 males) were recruited from the University of Tsukuba. All participants reported being free of neurological disorders, cardiovascular disease, any medications that influenced central nervous system function, and had (corrected- to-) normal vision. None of the participants exhibited signs of depression, assessed by the Beck Depression Inventory (Beck, et al., 1961; see Table 1). The participants gave informed consent to participate in the experiment, which was
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Table 1 Group means for participant characteristics.
Female/male Age (years)* Height (cm) Weight (kg) Beck Depression Inventory IPAQ total PA score (kcal/week)* IPAQ leisure-time domain sub-score (kcal/week)* IPAQ vigorous-intensity sub-score (kcal/week)*
Active
Sedentary
10/10 20.4 ± 0.3 168.6 ± 1.6 60.1 ± 1.8 4.9 ± 0.8 9229.3 ± 1047.4 6592.1 ± 928.7
9/11 22.3 ± 0.4 165.8 ± 2.0 58.1 ± 2.2 6.7 ± 1.2 1698.2 ± 246.2 582.4 ± 153.1
5796.0 ± 870.4
336.0 ± 127.2
Values are mean±SE. The maximum obtainable score on the Beck Depression Inventory is 63. *Significant difference, unpaired Student's t-test between groups, p < .05.
approved by the Institutional Human Research Committee (National Institute of Advanced Industrial Science and Technology). Participants were differentiated into two groups on the basis of their regular level of physical activity: active and sedentary. The participants' regular physical activity levels were evaluated using the International Physical Activity Questionnaire (IPAQ) long form. The IPAQ was developed as an instrument for cross-national monitoring of physical activity levels, and has been validated in different sociocultural settings (Craig et al., 2003; Hagströmer et al., 2006). IPAQ assesses physical activity undertaken across a comprehensive set of domains including: leisure-time physical activity, domestic and gardening (yard) activities, work-related physical activity, and transport-related physical activity (http://www.ipaq.ki.se). Furthermore, walking, moderate-intensity, and vigorous-intensity sub-scores can be also calculated using the IPAQ. The characteristics of the active and sedentary groups are summarized in Table 1. In Table 1, the leisure-time domain and vigorousintensity sub-scores, which are considered influential factors for cognitive improvement (Hillman et al., 2004), are indicated in addition to the total physical activity score. These IPAQ scores (i.e., physical activity levels) were significantly different between the active and sedentary groups. Lastly, participants' age was significantly different between the groups (mean difference = 1.9 yrs). It is known that from the third to the eighth decade of life, the P3 amplitude decreases with age at a rate of about 0.2 µV per year, whereas the P3 latency increases with age at a rate of about 1.4 ms per year (Picton et al., 1984). In the present study, the age difference between the groups was considered negligible small. Indeed, no significant correlation between age and RT (r = .25, p = .13), age and P3 amplitude (r =−.11, p = .48), or age and P3 latency (r = .11, p = .51) was observed. 2.2. Procedure After providing informed consent, the participant completed the IPAQ and Beck Depression Inventory. Individual participants were seated in a comfortable chair and prepared for neuroelectric measurement in accordance with the Society for Psychophysiological Research guidelines (Picton et al., 2000). Electroencephalograms (EEGs) were measured from the following nine electrode sites of the International 10–20 system: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, with AFz as the ground electrode, and impedances were kept below 5 kΩ. To monitor possible artifacts due to eye movements, vertical electrooculogram (EOG) was recorded using electrodes placed above and below the right eye, and a horizontal EOG was recorded from the outer left and right canthi. 2.3. Task switching paradigm Based on Hillman et al. (2006), the task switching paradigm included two tasks. A white numeric digit (digits 1–9, excluding 5) was presented in the center of the computer screen on a black background. The viewing
distance was approximately 57.3 cm. Each digit was surrounded by a solid or dashed square. Each square subtended vertical and horizontal visual angles of 4.6°. The participants made judgments as to whether the digit was lesser or greater than 5 to the digit surrounded by a solid square (low/high task), or whether the digit was odd or even to the digit surrounded by a dashed square (odd/even task). The participants were instructed to press, as quickly and accurately as possible, one of two keys on the computer keyboard corresponding to the digit. The participants used their index fingers of each hand to make response on the keys X (low/odd) and M (high/even) of a standard keyboard. Digits were presented for 200 ms, with a 1500 ms response-stimulus interval. If no response was made, the trial terminated 3000 ms after the onset of stimuli. Participants received two pure task conditions and a mixed-task condition. In the pure task conditions, only one task was performed (i.e., AAAAAA… BBBBBB…). In the mixed-task condition, the alternatingruns paradigm (Rogers and Monsell, 1995) was used (AABBAA…). That is, the task changed predictably every two trials. Increases in predictability by using the alternation-runs are thought to have increased working memory demands, because participants had to keep track of the current and upcoming tasks. Therefore, alternatingruns may be more sensitive for detecting subtle differences in cognitive functions between groups, compared to random cuing procedure used in previous ERP studies (Hillman et al., 2006; Scisco et al., 2008). Participants completed 64 trials in each of the pure task condition and 256 trials (64 trials × 4 blocks) in the mixed-task condition. The first trial in each block for both pure task and mixed-task condition was discarded from the analyses. All 8 digits appeared with equal probability in a random order. The order of conditions (i.e., pure task or mixed-task) and tasks (i.e., low/high or odd/even in the pure task condition) were counterbalanced among the participants to minimize potential practice effects. The participant was given the task instructions and allowed 64 practice trials before the each experimental task. The task duration of 1 block (64 trials) was about 2.5 min. 2.4. Data reduction The EEG data were re-referenced to mathematically averaged earlobes (A1–A2) offline. The bandpass filter was set at 0.1 Hz to 30 Hz. Data were converted from 100 ms pre-stimulus to 1200 ms post-stimulus were digitized at a sampling rate of 1000 Hz. Trials with eye blinks, eye movements (rejection levels: ± 80 μV) and response errors were excluded from the analyses. Based on visual inspection, trials were rejected due to artifacts during offline analysis, in addition to the automatic rejection. On average, about 35% of trials were discarded due to artifacts and response errors (details in Table 2). ERP potentials were obtained for separate averages for each task (i.e., low/ high or odd/even). ERP components were measured relative to a 100 ms pre-stimulus baseline. The P3 latency was measured at the most dominant site (i.e., Pz). The amplitude of the corresponding peak latency was measured at midline electrode sites (Fz, Cz, and Pz). The time window for peak detection of the P3 component was 250– 600 ms. 2.5. Statistical analysis Separate analyses were conducted to examine mixing and switch effects. Mixing effect analyses examined differences between the pure task condition and non-switch trials in the mixed-task condition, and switch effect analyses examined differences between switch and nonswitch trials in the mixed-task condition. A mixed-model analysis of variance (ANOVA) was used to analyze RT, error rate, and P3 latency with the following factors: 2 (Group: active vs. sedentary) × 2 (Condition: non-switch vs. pure for mixing effect, or switch vs. nonswitch for switch effect) × 2 (Task: low/high vs. odd/even). Angular transformation was applied to the error rate data before the analysis. P3 amplitude was analyzed using a 2 (Group) × 2 (Condition) × 2 (Task)×
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Table 2 Group means for reaction time, error rate, P3 amplitude and latency, and number of trials for ERP averaging during each condition and task. Pure
Mixed Non-switch
Reaction time (ms) Error rate (%) P3 amplitude (μV) P3 latency (ms) Number of trials for ERP averaging (n)
Active Sedentary Active Sedentary Active Sedentary Active Sedentary Active Sedentary
Switch
Low/high
Odd/even
Low/high
Odd/even
Low/high
Odd/even
447.4 ± 10.7 471.4 ± 17.4 3.6 ± 0.6 2.2 ± 0.5 11.8 ± 1.2 9.5 ± 1.1 398.0 ± 13.9 399.0 ± 10.3 39.7 ± 2.3 41.9 ± 2.1
494.3 ± 11.4 566.4 ± 31.4 5.3 ± 0.8 4.4 ± 1.2 11.5 ± 1.1 8.5 ± 1.1 423.7 ± 13.8 402.5 ± 12.7 40.4 ± 2.0 40.4 ± 2.3
508.6 ± 23.9 610.5 ± 35.9 4.1 ± 0.8 3.5 ± 0.8 12.1 ± 1.4 7.7 ± 0.9 406.1 ± 13.7 415.5 ± 12.4 37.6 ± 2.4 40.0 ± 2.3
561.7 ± 23.0 660.8 ± 44.1 5.6 ± 0.8 4.8 ± 1.2 11.2 ± 1.3 8.0 ± 1.2 428.4 ± 17.4 423.9 ± 10.2 37.4 ± 2.4 39.9 ± 2.2
609.1 ± 30.5 772.7 ± 50.8 6.0 ± 0.9 4.8 ± 1.1 9.3 ± 1.3 5.7 ± 0.7 392.7 ± 17.8 416.6 ± 20.3 37.6 ± 2.0 39.0 ± 2.2
667.5 ± 33.2 848.3 ± 67.6 7.9 ± 1.1 8.0 ± 1.6 9.6 ± 1.2 5.6 ± 0.9 394.0 ± 19.3 420.2 ± 17.2 42.2 ± 1.7 42.6 ± 2.0
Values are mean ± SE. The Region factor was collapsed for the P3 amplitudes in this table. The P3 latency was measured at the most dominant site (Pz).
3 (Region: Fz vs. Cz vs. Pz) mixed-model ANOVA. The reported significances for the F values were those obtained after Greenhouse– Geisser correction as appropriate and this is indicated by a correction coefficient epsilon (ε). Post hoc comparisons were conducted using univariate ANOVA and Tukey's HSD multiple-comparison test. The participant characteristics shown in Table 1 were analyzed using unpaired Student's t-tests. The significance level was set at .05. 3. Results Table 2 shows group means for RT, error rate, P3 amplitude and latency, and number of trials for ERP averaging during each condition and task. The results of the omnibus ANOVAs are shown in Table 3. The follow up post hoc analyses are reported below. 3.1. Mixing effect 3.1.1. Behavioral measures The RT analysis revealed a Group × Condition × Task interaction [F(1, 38) = 5.7, p = .02, η2p = .13]. Breaking down the 3-way interaction by examining Group × Condition for each task (i.e., low/high or odd/even) revealed a significant Group × Condition interaction during the low/high task [F(1, 38) = 8.0, p = .01, η2p = .17]. Post hoc analyses indicated that the group difference was only observed during the mixed-task condition [t(1, 38) = 2.4, p = .02], whereas no such effect was found during the pure task condition [t(1, 38) = 1.2, p = .25]. No such interaction was observed during the odd/even task [F(1, 38)= 1.1, p = .31, η2p = .03] (see Fig. 1).
3.1.2. P3 Component Fig. 2 shows the grand averaged ERP waveforms across groups and tasks for each condition. The P3 amplitude analysis revealed a Group × Condition × Task interaction [F(1, 38) = 5.1, p = .02, η2p = .12]. Breaking down the 3-way interaction by examining Group × Condition for each task (i.e., low/high or odd/even) revealed a significant Group × Condition interaction during the low/high task [F (1, 38) = 5.6, p = .02, η2p = .13]. Post hoc analyses indicated that P3 amplitude during the pure task condition was larger compared to the mixed-task condition only for the sedentary group [t(1, 19) = 3.2, p = .01], whereas no such effect was found for the active group [t(1, 19) = .5, p = .65]. No such interaction was observed during the odd/even task [F(1, 38) = .01, p = .91, η2p < .001] (see Fig. 3). A main effect for Region was also observed [F(2, 76) = 43.3, p < .001, ε = .75, η2p = .23] with follow up Tukey's HSD post hoc analyses indicating that P3 amplitude at Pz (M = 12.2 ± 0.9 μV) was larger than Fz (M = 7.0 ± 0.7 μV) and Cz (M = 10.8 ± 1.0 μV), and Cz was larger than Fz [ts(1, 39) ≥ 2.7, ps ≤ .05]. This main effect was superseded by a Condition × Region interaction [F(2, 76) = 6.3, p = .01, ε = .84, η2p = .14]. Post hoc analyses indicated that P3 amplitude during the pure task condition was larger than the mixed-task condition at Pz [t(1, 39) = 2.3, p = .03]. No such difference was observed at Fz and Cz. 3.2. Switch effect 3.2.1. Behavioral measures The RT analysis revealed a Group × Condition interaction [F(1, 38)= 7.2, p = .01, η2p = .16]. Post hoc analyses indicated that the condition
Table 3 ANOVA table (F-value) for reaction time, error rate, and P3 amplitude and latency. Mixing effect
Group (1, 38) Condition (1, 38) Task (1, 38) Region (2, 76) G × C (1, 38) G × T (1, 38) G × R (2, 76) C × T (1, 38) C × R (2, 76) T × R (2, 76) G × C × T (1, 38) G × C × R (2, 76) G × T × R (2, 76) C × T × R (2, 76) G × C × T × R (2, 76)
Switch effect
Reaction time
Error rate
P3 amplitude
P3 latency
Reaction time
Error rate
P3 amplitude
P3 latency
4.5* 53.8*** 55.2*** – 4.5* N.S. – N.S. – – 5.7* – – – –
N.S. N.S. 11.0** – N.S. N.S. – N.S. – – N.S. – – – –
4.5* N.S. N.S. 43.3*** N.S. N.S. N.S. N.S. 6.3** N.S. 5.1* N.S. N.S. N.S. N.S.
N.S. N.S. 4.2* – N.S. N.S. – N.S. – – N.S. – – – –
6.1* 108.4*** 34.6*** – 7.2* N.S. – N.S. – – N.S. – – – –
N.S. 27.5*** 8.3** – N.S. N.S. – N.S. – – N.S. – – – –
6.2* 38.7*** N.S. 34.3*** N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S. N.S.
N.S. N.S. N.S. – N.S. N.S. – N.S. – – N.S. – – – –
*p < 0.05, **p < 0.01, and ***p < 0.001.
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Fig. 1. Mean RTs across groups and conditions for each task in the mixing (upper) and the switch (lower) effects. Error bars indicate SEs.
difference (i.e., switch cost) was observed for both active and sedentary groups [ts(1, 19) ≥ 7.6, ps< .001]; however, the condition difference was larger for the sedentary group (see Fig. 1). 3.2.2. P3 component The P3 amplitude analysis revealed a Region main effect [F(2, 76) = 34.3, p < .001, η2p = .48] with a follow up Tukey's HSD post hoc analyses indicating that P3 amplitude at Pz (M = 10.6± 0.9 μV) was larger than Fz (M = 6.2 ± 0.7 μV) and Cz (M = 9.2 ± 1.0 μV); with Cz larger than Fz [ts(1, 39) ≥ 3.0, ps≤ .03]. 4. Discussion The present study was designed to clarify whether physical activity influences executive control function in young adults during a task switching paradigm. To this end, separate analyses for each two types of judgment tasks (i.e., low/high or odd/even) were conducted. The results showed shorter RTs and lower error rates during the low/high task compared to the odd/even task, indicating that task difficulty differed between the two tasks with the low/high task characterized as being easier than the odd/even task. During the pure task, amplitude of the P3 ERP component was larger than during the mixed-task condition only for the sedentary
group, no parallel differences were evident in ERP data of the active group (see Fig. 3). The P3 component is believed to represent the updating of working memory (Donchin, 1981). The amplitude of this component is proportional to the amount of attentional resources devoted to a given task (Kramer and Strayer, 1988; Wickens et al., 1983). In addition, it is well known that P3 amplitude is sensitive to mental workload, with amplitude decreases as a function of increases in memory load (see Kok, 2001 for a review). In ERP studies using the task switching paradigm, it was shown that P3 amplitude during the pure task condition was larger than during the mixed-task condition (e.g., Jost et al., 2008; Goffaux et al., 2006), suggesting that greater requirement of working memory demands in the mixed-task condition leads to a lesser amount of available attentional resources compared to the condition with lower working memory load (i.e., pure task condition). Accordingly, in the present study, we believe that the smaller P3 amplitude during the mixed-task relative to the pure task condition in the sedentary group is resulting from greater working memory demands (i.e., fewer available resources) during the mixed-task condition. In contrast, the active individuals may better allocate available attentional resources compared to the sedentary individuals even in the mixed-task condition, thereby no differences in P3 amplitude between conditions were observed.
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309
Fig. 2. Grand averaged ERP waveforms across groups and tasks for each condition.
Interestingly, the effect of physical activity on the P3 amplitude for the mixing cost was selective in that it was only observed with the low/high task, but not for the odd/even task (see Fig. 3). These differences in P3 amplitudes, due to physical activity level and task conditions, are consistent with the RT results. That is, a selective effect of physical activity for the mixing cost was also observed for RT only with the low/high task (see Fig. 1). One possible explanation for the task-dependent effects is that working memory load (i.e., executive control requirements) might be different between the tasks. It is plausible that the more difficult task required greater amounts of executive control. For simplicity, assume that the working memory load for maintaining the odd/even task (i.e., more difficult task) was 2 and that for maintaining the low/high task (i.e., easier task) was 1. That is, the working memory load in the mixed-task condition was 3 (i.e., 2 + 1). Based on this assumption, the additional working memory load in the mixed-task condition, when compared to the pure task condition (i.e., mixing cost), would be greater in the low/ high task trials (i.e., 3–1 = 2) than in the odd/even task trials (i.e., 3– 2 = 1), and therefore the selective effects of physical activity on the mixing cost were only observed in the low/high task. In other words, if the difference in working memory load between tasks were relatively small, the selective effects of physical activity might be minimized at least in young adults. Thus, working memory demands might influence the relationship of physical activity in young adults as well as older adults (Hillman et al., 2008; Kramer and Hillman, 2006). As mentioned previously, the contradictory findings regarding the relationship between physical activity and task switching in prior research reporting P3 data (Hillman et al., 2006; Scisco et al., 2008)
may be the result of combined averaging across tasks (i.e., the latency jitter). We hypothesized that this could result in underestimation of the effects of different levels of physical activity on the P3 component. In the present study, P3 latencies were different between tasks for the mixing effect, with shorter P3 latencies during the low/high task compared to the odd/even task. Smaller P3 peak amplitude could have resulted from the variability in the P3 latency (Kok, 2001). Indeed, even in the present study, a P3 amplitude analysis of combined averaging with collapsed task difficulty (i.e., low/high and odd/even) for the mixing effect revealed no significant Group main effect (p = .06) or Group × Condition interaction (p = .23). Thus, collapsing analyses across tasks with variable amounts of difficulty (Hillman et al.; Scisco et al.) might result in an underestimation of the physical activity effect on cognition and therefore account for such contradictory findings. With regard to the switch effect (i.e., a difference between switch and non-switch trials within the mixed-task), this effect on RTs was smaller for the active group than for the sedentary group irrespective of tasks. The switch cost is also considered to reflect executive control including inhibition and mental flexibility (Monsell et al., 2003; Rogers and Monsell, 1995). That is, our findings with respect to switch cost may provide further evidence that physical activity selectively influences executive control. The switch cost may function better than mixing cost as an index for detecting effects of physical activity on executive control, because it has been proposed that the mixing cost results from differences between the task conditions that involve not only differential working memory demands, but also differences in arousal (Rogers and Monsell) and task strategy (De Jong, 1995).
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Fig. 3. Mean P3 amplitudes across groups and conditions for each task in the mixing (upper) and the switch (lower) effects. The Region factor is collapsed in this figure. Error bars indicate SEs.
As mentioned in the Introduction, Hillman et al. (2006) did not demonstrate the physical activity effect on the switch cost. The main difference between the present study and Hillman et al. related to the mixed-task condition was the predictability of the subsequent task. That is, in the present study, the two tasks were presented as an alternatingruns sequence (i.e., AABBAA...), whereas in Hillman et al.'s study, the task randomly changed between trials. Lorist et al. (2000) indicated that contingent negative variation (CNV), a negative slow-wave cortical potential of an ERP reflecting the task preparation processes, was enhanced in the frontal region for preceding expected switch trials, compared to non-switch trials using the alternating-runs sequence, suggesting that switch trials require more demanding task preparation if the subsequent task is predictable. Further, Kamijo et al. (in press) investigated the relation of physical activity to task preparation using CNV, which indicated that task preparation could be accomplished more efficiently in active individuals than less active individuals. Taken together, one possible explanation for the physical activity effect on the switch cost might be that task set preparation becomes more efficient in switch trials because of the knowledge of the subsequent task during the alternating-runs sequence. Note that, for reason not clearly understood, the physical activity effect on switch cost (i.e., Group× Condition interaction) was observed only for RTs, but not for P3 measures. Thus,
further studies are needed to understand how physical activity affects the switch cost. It is well known that the P3 can be separated into two subcomponents: P3a and P3b. P3a exhibits a front-central scalp distribution compared to the more parietal distribution of P3b, and P3a has shorter peak latency (see Polich, 2007 for review). Scisco et al. (2008) investigated the effects of physical activity using both P3a and P3b during the task switching paradigm, indicating no effects for either subcomponent. In the present study, P3 amplitude reached its maximum at Pz and no positive peak was observed at front-central region during the possible latency of P3a, indicating that P3a did not appear or was negligible. Consequently, we only focused on the P3 component with maximum at Pz, which corresponds to the P3b. This inconsistency in the P3 subcomponents between the present study and Scisco et al. may be due to the differences in task difficulty and the stimulus duration (200 ms or until response, respectively), as the P3 component is sensitive to both task difficulty (McCarthy and Donchin, 1981; Polich, 1987) and stimulus duration (Polich, 1996). Several previous ERP studies have used a pre-cuing paradigm, in which cue indicating relevant tasks (i.e., switch or non-switch) were presented in advance of a target stimulus. The pre-cuing paradigm allows us to examine the processes of task set updating by the cues,
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separately from target identification. For example, Jost et al. (2008) indicated that cue-locked P3 amplitude was larger for the mixed-task condition than the pure task condition. Meanwhile, target-locked P3 amplitude is generally larger during the pure task condition than during the mixed-task condition (Jost et al.; Goffaux et al., 2006). In the present study, we did not use the pre-cuing paradigm to conform previous P3 studies that investigated the physical activity effect (Hillman et al., 2006; Scisco et al., 2008), because the present purpose was to elucidate sources of discrepancies between these studies. It may be that the physical activity effects are different depending on the processes of task set updating, or target identification (i.e., cue-locked P3 or target-locked P3). Further studies are needed to discern the physical activity effects on these processes using the pre-cuing paradigm. In conclusion, regular physical activity improves executive control function as represented by the task switching paradigm in young adulthood. As suggested by Hillman et al. (2008), there is little room for physical activity related improvement to cognitive function during young adulthood. Therefore, we propose that cognitive tasks evaluating the influence of physical activity should be selected with caution, especially for young adults, to avoid underestimation of the beneficial effects. Acknowledgements The authors thank Dr. Charles Hillman for his helpful comments on the earlier drafts of this paper. References Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J., 1961. An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561–571. Colcombe, S., Kramer, A.F., 2003. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychol. Sci. 14, 125–130. Craig, C.L., Marshall, A.L., Sjostrom, M., Bauman, A.E., Booth, M.L., Ainsworth, B.E., Pratt, M., Ekelund, U., Yngve, A., Sallis, J.F., Oja, P., 2003. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35, 1381–1395. De Jong, R., 1995. Strategical determinants of compatibility effects with task uncertainty. Acta Psychol. 88, 187–207. Diamond, A., 2006. The Early Development of Executive Functions. In: Bialystok, E., Craik, F.I.M. (Eds.), Lifespan Cognition: Mechanisms of Change. Oxford University Press, New York, pp. 70–95. Donchin, E., 1981. Presidential address, 1980. Surprise!...Surprise? Psychophysiology 18, 493–513. Dustman, R.E., Emmerson, R.Y., Ruhling, R.O., Shearer, D.E., Steinhaus, L.A., Johnson, S.C., Bonekat, H.W., Shigeoka, J.W., 1990. Age and fitness effects on EEG, ERPs, visual sensitivity, and cognition. Neurobiol. Aging 11, 193–200. Goffaux, P., Phillips, N.A., Sinai, M., Pushkar, D., 2006. Behavioural and electrophysiological measures of task switching during single and mixed-task conditions. Biol. Psychol. 72, 278–290. Hagströmer, M., Oja, P., Sjöström, M., 2006. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 9, 755–762. Hillman, C.H., Weiss, E.P., Hagberg, J.M., Hatfield, B.D., 2002. The relationship of age and cardiovascular fitness to cognitive and motor processes. Psychophysiology 39, 303–312.
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