Method
Fifty-four participants were recruited by advertisement from universities in Taipei. This sample size was determined based on an a priori power analysis (alpha = 0.05, power = 0.80), and the effect size, f = 0.44 ( Cohen, 1992 ), and similar to that seen in ERP studies investigating visuospatial attention and working memory in athletes ( Chueh et al., 2017 ). They were divided into three groups based on their sport level: eighteen ETT players, eighteen ATT players, and eighteen nonathletes. ETT athletes (age 20.2 ± 2.4) were those who competed at the national division 1 level (top 3), and international competition experience ( n = 10). ATT athletes (age 22.2 ± 2.2) had no experience competing at national division 1 level, but with national division 2 level experience (top 8). Nonathletes (age 20.7 ± 1.2) were those who had not engaged in regular physical activity as measured by the International Physical Activity Questionnaire (IPAQ) ( Liou et al., 2008 ). Both levels of table tennis players maintained regular training for at least 6 months preceding their participation in the experiment. There were nine male and nine female participants in each group ( Table 1 ). All the participants met the following criteria: (a) normal or corrected-to-normal visual acuity, (b) nonsmokers, (c) right-handed dominance, and (d) individuals did not report diagnosed psychiatric or neurological disorders, and take central nervous system medication. Prior to the start of the experiment, all participants were required to sign an informed consent approved from the Human Research Ethics Committee of National Taiwan Normal University (201602HM005).

Note:

MET, Metabolic Equivalent. **Group effect at p < 0.01. Table tennis (TT) training reflects experience in the past 6 months.

The current study employed a modified nondelayed and delayed match-to-sample tesk adapted from Müller & Knight (2002) , Chueh et al. (2017) to assesses visuospatial attention (nondelayed condition) and visuospatial memory (delayed condition) ( Figure 1 ). The task was programmed with STIM 2.0 software (Neuroscan Ltd., El Paso, TX, USA). All stimuli were displayed on a 17-inch computer monitor with a black background positioned 60 cm in front of the subject. Similar to Chueh et al. (2017) , the stimuli consisted of a red dot (0.5° × 0.5°) randomly presented within a 3.8° × 7.4° gray rectangle. A red dot could appear in any one of nine locations ( i.e ., center, center right, center left, upper center, upper right corner, upper left corner, lower center, lower right corner, and lower left corner) within its rectangle. Within the two rectangles, participants were required to determine whether the red dots were in the same or different spatial position. In the visuospatial attention condition (nondelayed condition), two rectangles were presented simultaneously, one in the centre and the other to the left or right of it. To avoid the potential effect of unwanted saccades, the two rectangle stimuli were presented for 180 ms, a duration typically shorter than voluntary saccade ( Müller & Knight, 2002 ; Chueh et al., 2017 ). In the visuospatial memory condition (delayed condition), stimulus 1 (S1) was presented for 180 ms to the left or right of the central fixation (0.5° × 0.5°) with an equal probability of appearing on both sides, followed by a 3 s delay. Stimulus 2 (S2) was then presented for 500 ms in the centre of the screen. During the 3 s delay, participants were required to memorise the position of the S1 red dot and determine whether the position of the S2 red dot was the same or different. Before beginning the task, participants were reminded that both speed and accuracy were essential. When the red dot appeared in the same position, the left thumb pressed the “Yes” button, and when the red dot appeared in a different position, the right thumb pressed the “No” button. For the non-delayed and delayed conditions, the response time window was 2,000 ms, after which feedback (correct or incorrect) was provided based on the participant’s response. For the practise trials, each participant had to achieve a response accuracy of 80%. A total of 240 trials were divided into four blocks, with the non-delayed and delayed conditions being presented at random and with equal probability. Each block was separated by a 3–5 min rest period.

The experimenter concealed the aim of this study from the participants to preclude any psychological expectations that could introduce bias or distortion into the data. Stringent guidelines were enforced, such as prohibiting strenuous exercise, and consumption of food and drink (excluding water) for 1.5 h before the commencement of the experiment. Testing sessions were scheduled during daytime hours, and all experiments were concluded within an approximate 3-week period. Participants were required to complete the Socioeconomic Status of the Family (SES) inventory ( Hollingshead & Redlich, 1958 ) and a non-verbal IQ test using Raven’s Progressive Matrices: SPM Plus Sets. This test, comprising a total of 60 items across five sets (with 12 items per set) ( Styles, Raven & Raven, 1998 ), was overseen by researchers to evaluate abstract reasoning ability, ensuring that all participants possessed a normal IQ. They also completed the International Physical Activity Questionnaire (IPAQ) ( Liou et al., 2008 ) and signed the informed consent form. Before the formal recording, participants were briefed on the cognitive task and were required to achieve an accuracy rate of 80 percent on practice trials. Upon completion of the experiments, participants were compensated with USD $30.

The electroencephalographic (EEG) recording procedure was similar to previous studies ( Chueh et al., 2017 ). EEG activity was recorded at 30 electrode sites using an elastic electrode cap (Quick-Cap, Compumedics Neuroscan, Inc., 200 Charlotte, NC, USA). The electrode sites were mounted according to the modified International 10-10 System ( Chatrian, Lettich & Nelson, 1985 ). Electrooculographic (EOG) activity is largely detected by monitoring both vertical and horizontal (HEOG and VEOG) activity, which is measured in the outer canthus and above and below the left orbit. The reference electrode was set to the mastoid average (A1, A2), and the ground electrode was set to FPz. All electrode impedances were below 5 kΩ. The EEG data acquisition was performed with a sampling rate of 1,000 Hz, using a DC-to 200-Hz filter and a 60-Hz notch filter.

The algorithm using ( Semlitsch et al., 1986 ) was corrected the EOG activity and data reduction. The ERP analysis time epochs was defined from −100 ms of prestimulus to 1,000 ms of poststimulus onset, with the prestimulus 100 ms as baseline. The ERP data acquisition employed were bandpass filtered a bandpass filter (0.1–30 Hz cutoffs; 12 dB/oct). Epochs were exclusion of any electrode with an amplitude exceeding the ±50 μV criterion. Visual inspection of individual grand average ERP waveforms through inspection results and final peak detection analyses. In the nondelayed condition, the P3 amplitude was defined as the maximal positive peak (300 to 600 ms) after the stimulus onset, and as the maximal positive peak (300 to 600 ms) after the onset of the S2 stimulus in delayed condition. The P3 latency was defined according to the time point of the maximal P3 peak. P3 amplitude was distinguished across the midline electrodes ( i.e ., Fz, Cz, and Pz) ( Johnson, 1993 ).

One-way ANOVAs were separately computed to test homogeneity of the following demographic variables (DV): height, weight, non-verbal IQ, SES, levels of physical activity and video game experience among groups. An independent t-test was conducted to compare training experience and daily training hours, number of training sessions per week in the past 6 months and number of years in training between the two athlete groups.

Behavioral data analysis, 3 group (ETT players, ATT players, and nonathletes) × 2 condition (Nondelayed and Delayed) repeated measures ANOVAs were separately performed on behavioral data ( i.e ., response time, response accuracy, accuracy-adjusted response time) to examine group differences in behavioral performance. In addition, DV, such as IQ and physical activity, rather than a sport-specific training effect, were found to be nonhomogeneous ( Green & Bavelier, 2003 ; Hackman & Farah, 2009 ). We hypothesize that the group effect might be influenced by IQ and physical activity. To investigate this, we conducted a Pearson correlation analysis to examine the relationship between task performance, DV ( e.g ., IQ, physical activity), accuracy-adjusted response time, and sport training. We measured overall task performance (accuracy and RT) using accuracy-adjusted response time (mean response time/accuracy) ( Townsend & Ashby, 1983 ). This approach has been proposed to help rule out any possible effects of behavioral strategies on task performance (such as the speed-accuracy trade-off) ( Getzmann, Falkenstein & Gajewski, 2013 ).

ERP data analysis, 3 group (ETT players, ATT players, and nonathletes) × 2 condition (Nondelayed and Delayed) × 3 electrode sites (Fz, Cz, and Pz) repeated measures ANOVAs were performed on the P3 amplitude and latency data separately. Post hoc comparisons were conducted using Tukey HSD significant difference tests. In this analysis, we conducted a covariate analysis of physical activity and IQ to test whether the physical activity and IQ is a confounding variables that may potentially bias the physical activity and IQ effect on P3 ( Jaušovec & Jaušovec, 2000 ; McDowell et al., 2003 ). In addition, to test whether the P3 effect was due to a benefit of sport training (year, weekly frequency and hours), we conducted a Pearson correlation analysis to examine the relationship between the P3 amplitude/latency and sport training (ST). An alpha = 0.05 was set as the level of statistical significance for all analyses. Eta-squared effect sizes (η 2 ) were reported for significant main effects and interactions, and a Greenhouse-Geisser correction was used to adjust for violations of the sphericity assumption. All data analyses was completed using IBM SPSS Statistics 23 (SPSS, Chicago, IL, USA).