Dynamic visual attention characteristics and their relationship to match performance in skilled basketball players

Participants

Sample size was estimated by the software G*Power3.1.9.2 (Faul et al., 2007). considering the effect size 0.90 based on the similar literature (Qiu et al., 2019), an alpha level of 0.05,the power 0.80, two tails (Cohen, 1992). It resulted in a sample size of 21 per group, to allow for dropout, we selected 24 participants per group, ending with a total of 48 participants. A total of 24 basketball players from two China University Basketball Association (CUBA) teams with more than 10 years of experience per person (mean, 10.59 years; SD, 2.39 years) from 18 to 22 years of age (mean, 20.45; SD, 2.16 years) comprised the basketball player group. All players participated the experiment. The control group was composed of 24 undergraduate students 17–21 years of age (mean, 18.73; SD, 1.64 years) with non-sport activity. All participants reported normal or corrected-to-normal vision. Each participant who completed the study was compensated for their time. The experimental protocol was approved by the regional ethics committee of the Shanghai University of Sport (No. 2017044SUS). All participants provided written informed consent prior to the start of the experiment.

Procedure

The experiment was conducted 7–10 days after the 19th CUBA Southeast Division Tournament. We used the MOT task to explore both individual and group differences in dynamic visual attention characteristics. The task assessed two different conditions: the ability to track an increasing number of targets moving at a given speed, and the ability to track a set number of targets moving at various speeds. The tracking load task consisted of five blocks (with 2, 3, 4, 5 and 6 targets) of 30 trials each for a total of 150 trials, with a rest period of 2 min between each block. Thus, this experimental task condition lasted approximately 45 min. After a 10-min break, the tracking speed task began. This task consisted of three blocks (5°/s, 10°/s and 15°/s) of 30 trials each block for a total of 90 trials, with a rest period of 2 min between blocks. Thus, this experimental task condition lasted approximately 30 min. The order the blocks were presented was counterbalanced across participants during the two tasks.

Stimuli and apparatus

The experiment was conducted using a Dell Inspiron laptop with MATLAB 2016a and Psychtoolbox 3. Visual stimuli were displayed on a 15.6-inch monitor. The monitor had 1,920 × 1,080 pixels and a refresh rate of 60 Hz. Participants were seated approximately 50 cm from the computer screen in a quiet room. At the beginning of the experiment, the preparation screen was presented (press the left mouse button to start the test). The preparation screen was presented in the beginning of each block only. Then a white fixation cross (+) was presented first for 1,000 ms on a gray background (37.98° × 21.0°), followed by the presentation of 12 white circular dots (diameter 0.65°) for 1,000 ms. In the tracking load task, a subset of the dots (2, 3, 4, 5, or 6) were highlighted in blue and flickered three times for 2 s to designate them as the targets. Next, all 12 dots moved in random directions at a constant speed (5°/s). They changed their directions randomly and when a dot reached the edge of the screen border. There were no additional constraints in the dots’ trajectories so there was the possibility that they occluded one another for an instant. In the tracking speed task, three dots were highlighted in blue and flickered three times for 2 s to designate them as targets for this task. Next, all 12 dots moved in random directions at a speed of 5°/s, 10°/s, or 15°/s, After 10 s, the dots stopped moving. Pressing a mouse button was required to point out the targets and to then start the next trial (Fig. 1).

Statistical analysis

The data were recorded and collected by MATLAB 2016a software. SPSS, version 21.0, software was used to calculate the accuracy and to conduct statistical analyses. In the tracking load experiment, a mixed experimental design of 2 (group) × 5 (target number) was used. A Linear mixed effect model (LMM) was used to assess the impact of the group (basketball players, nonathletic college students), tracking load (2, 3, 4, 5 and 6 targets) and the group-by-load interaction. In the tracking speed experiment, a mixed experimental design of 2 (group) × 3 (target speed) was used. A LMM was used to assess the impact of the group (basketball players, nonathletic college students), tracking speed (5/s, 10°/s and 15°/s) and the group-by-speed interaction. Using a random intercept to account for the repeated measures of each participant. Further analyses were conducted using the simple effects test for any significant interaction. Pearson’s correlation coefficient was used to test the linear relation between visual attention and game-related performance. An alpha level of 0.05 was pre-selected for all statistical comparisons. The accuracy rate was calculated by determining the percentage of correctly selected targets across all experimental times at different target numbers for each participant.

Match-related performance statistical analysis

All performance statistical data were obtained from the official website of China University Basketball League. Five statistics (seven games in total) were selected for match performance, including points scored, steals, assists, turnovers and fouls. Over the course of the season, the plays average 219.04 ± 32.13 minutes played. To normalize the data for individual differences in playing time, these statistics were analyzed per 100 min played. The standardized data were 7.32 ± 3.32 assists per 100 min, 4.67 ± 2.07 steals per 100 min, 8.31 ± 3.38 turnovers per 100 min, 9.68 ± 3.79 fouls per 100 min and 34.01 ± 12.13 points scored per 100 min.

Visual tracking load assessment

Linear mixed effect model revealed a significant main effect of target load (F = 1371.842, 95% CI [−0.17 to −0.15], P < 0.001), indicating that tracking performance decreased significantly as target number increased. Tracking accuracy also significantly decreased with increasing target number (2 vs. 3, mean difference (MD) = 0.126, 95% CI [0.08–0.17], P < 0.001, 3 vs. 4, MD = 0.181, 95% CI [0.12–0.24], P < 0.001; 4 vs. 5, MD = 0.189, 95% CI [0.12–0.26], P < 0.001; 5 vs. 6, MD = 0.128, 95% CI [0.07–0.19], P < 0.001). However, there was no significant main effect of group (F = 1.649, 95% CI [−0.10 to −0.02], P = 0.204). There was no significant interaction between the group and target load (F = 1,371.842, 95% CI [−0.01 to −0.03], P = 0.256) (see Fig. 2). Thus, these results did not support our hypothesis that basketball players would have better tracking performance than nonathletic college students when the number of targets increased (up to six). This lack of a significant effect indicated that basketball players and nonathletic college students have the same level of accuracy for simultaneously tracking at least six objects.

VTS assessment

Linear mixed effect model revealed a significant interaction between the group and target speed (F = 19.346, 95% CI [0.04–0.11], P < 0.001). The results of a simple effects test showed that when the target speed was 5°/s, there was no significant difference in visual tracking accuracy between basketball players and college students (F = 0.339, 95% CI [−0.10 to 0.05], P = 0.456). However, when the target speed was 10°/s, there was a significant difference in visual tracking accuracy between basketball players and nonathletic college students (F = 24.731, 95% CI [0.10–0.24], P < 0.001), the accuracy of basketball players was significantly greater than that of nonathletic college students; when the target speed was 15°/s, there was a significant difference in visual tracking accuracy between basketball players and nonathletic college students (F = 13.763, 95% CI [0.10–0.24], P < 0.001), the accuracy of basketball players was significantly greater than that of nonathletic college students (see Fig. 3). Consistent with our hypothesis, the results of the tracking speed task indicated that with increasing target speed, basketball players had a significantly greater ability to visually track targets compared with nonathletic college students.

Relationship between accuracy of visual tracking speed trials and match performance statistics

Pearson correlations between the accuracy of visual tracking speed trials (AVTS) and match statistics were calculated. (The magnitude of each correlation was assessed using the following scale: <0.1 = trivial; 0.1–0.3 = small; 0.3–0.5 = moderate; and 0.5–0.7 = large) (Hopkins et al., 2009). The analyses revealed that for AVTST at 10°/s and at 15°/s there was a positive, large, and statistically significant correlation between the AVTS and the number of assists (for 10°/s: r = 0.557, P = 0.005; for 15°/s r = 0.621, P = 0.001), suggesting that greater AVTS was significantly correlated with a higher number of assists (Table 1). In addition, the number of steals also had a positive, large and statistically significant correlation at the same tracking speeds (for 10°/s: r = 0.600, P = 0.002; for 15°/s: r = 0.687, P < 0.001), indicating that greater AVTS was correlated with a higher number of steals. A positive, small correlation was found between the AVTS at all three target speeds and the number of turnovers (for 5°/s: r = 0.257, P = 0.225; for 10°/s: r = 0.113, P = 0.599; for 15°/s: r = 0.269, P = 0.203). However, no significant relationships were observed between the AVTS at 5°/s and the match performance statistics of assists (r = 0.273, P = 0.195), steals (r = 0.345, P = 0.099), fouls (r = −0.01, P = 0.997), or points scored (r = 0.126, P = 0.559); no significant relationships were observed between the AVTS at 10°/s and the match performance statistics of fouls (r = −0.119, P = 0.351) and points scored (r = 0.227, P = 0.287); no significant relationships were observed between the AVTS at 15°/s and the match performance statistics of fouls (r = 0.178, P = 0.405) and points scored(r = 0.105, P = 0.625).

Visual tracking performance for the number of targets

The results of the tracking number task showed that as the number of targets visually tracked increased, the accuracy in tracking the targets decreased for all participants. There was a significant main effect of target number, such that tracking accuracies were markedly better when participants had to track only two targets compared with when they had to keep track of three to six targets. The flexible-resource model suggests that the capacity of attention resources is limited: with an increase in the number of targets, fewer resources are allocated to each target, which leads to a decrease in tracking accuracy (Alvarez & Franconeri, 2007). We found no difference in the tracking performance between skilled basketball players and nonathletic college students on the MOT task. These results are inconsistent with those observed in another study using a MOT task that found that team ball players have better visual attention than non-athletes (Qiu et al., 2018). These discrepant results are likely attributable to the target speed being too slow to assess this skill for both skilled basketball players and nonathletic college students. The objects moving at a speed of 10°/s in the Qiu’s study, but the objects moving at a speed of 5°/s in our experiment. That is, the attention resources of the participants in both groups were adequate for dealing with the level of load difficulty in the present study; Pylyshyn (2004) has shown that people can typically track four to five targets and maintain a good level of accuracy. A second potential reason for our findings is that because the targets in the MOT task have no special characteristics, the results cannot reflect the attention advantage of athletes in the process of a skilled operation. Memmert, Simons & Grimme (2009) stated that experts and novices do not differ on any basic abilities, and that all the differences observed are attributable to domain-specific or sport-specific advantages. Chase & Simon (1973) found that chess players do not have a better memory for pieces on the board in general, they only exhibit such an advantage when they form a meaningful chess pattern. The work by Furley & Memmert (2010) also support this view. Visual attention is composed of working memory storage capacity and visual information processing speed (Wiegand et al., 2013). In the classic MOT task, work memory storage capacity may be assessed more than visual attention. The discrepancy between studies that have indicated that athletes are faster than non-athletes at processing visual information suggests that using target speed as a characterization of individual differences in the ability to track targets may be a better measure of visual attention in the MOT task.

Visual tracking performance associated with target speed

Consistent with previous research (Huff et al., 2010; Tombu & Seiffert, 2011), all participants in the present study showed that visual tracking ability declines as target speed increases in the MOT task (the main effect of tracking target speed was statistically significant). When the speed increased, it always goes along with increasing the number of close encounters between target and distractors, the close encounters model indicates that the reduction in tracking performance as speed increases is due to the increased number of close encounters (Franconeri, Jonathan & Scimeca, 2010); due to accelerations, the target/distractors crossed more often with high speed than with low speed (Bae & Flombaum, 2012). Meyerhoff et al. (2016) showed that increasing object speed, which in return increases average crowding, more dynamically changing inter-object spacing provide the major source for tracking errors. On other hand, with the acceleration of the target, accuracy was gradually reduced. This is likely because the attention resources required by each target tracked gradually increased, while the total amount of attention resources possessed by each participant remained unchanged; thus, the participants could not effectively track the moving targets (Furley & Memmert, 2010). Of primary interest to us was the interaction between the group and target speed indicating whether basketball players differed from nonathletic college students. Our results showed that the accuracy of basketball players in tracking the targets was significantly greater than that of nonathletic college students at target speeds of 10°/s and 15°/s. This finding indicates that basketball players have a marked advantage over nonathletic college students in attention to processing speed in multitarget tracking tasks. Martín et al. (2017) investigated attention between rugby players who played different positions in the game and non-athletes by adjusting the target speed in the MOT task. Their results showed that the visual tracking performance of rugby players in defender positions is significantly better than that of both players in forward positions and in non-athletes. When the ability of NBA players at different positions in the game to track multiple targets was investigated, the result showed that the target tracking speed of guards was significantly better than that of forwards and the center and that target tracking speed ability can predict and evaluate the performance of a match (Mangine et al., 2014).

Many literature indicated that sport training improves visual attention performance, (Qiu et al., 2018; Heppe et al., 2016; Verburgh et al., 2014). In most sports, athletes must complete the established technical actions as rapidly as possible (capture effective information in a short period of time) and as accurately as possible (showing a strong spatial positioning ability). Basketball requires the players to have a strong ability to process the visual information of fast movement to make judgments and decisions within a short time. Therefore, the ability to track fast targets is important for basketball players, and this skill is in line with the requirements of the sport. Thus, expertise in this sport is related to high levels of performance on measures of processing speed and visual attention. The three-dimensional MOT speed thresholds of soccer players have been shown to be qualitatively similar to those previously obtained in professional and elite amateur athletes showing superior capacity for processing a complex and dynamic visual scene task (Romeas, Guldner & Faubert, 2016). According to the previous work, there are sudden direction changes which challenge the visual system (Horowitz & Cohen, 2010), unpredictable change of the motion direction of targets is sufficient to impair tracking performance (Meyerhoff et al., 2013). Basketball involves not only monitoring the speed and direction of the ball but also tracking the positions of teammates and opponents on the court. These tracking processes are very similar to those in the MOT task. Our results support the hypothesis that a basis for the transfer of the basketball players’ skills is that training and transfer tasks recruit overlapping cognitive processes (Dahlin et al., 2008; Jonides, 2004).

The relationship between tracking speed and match performance

It is widely accepted that expertise in sports is related to high levels of performance on measures of processing speed and visual attention (Alves et al., 2013; Faubert, 2013; Faubert & Sidebottom, 2012). But there is a dearth of research investigating the relationship between the visual attention of basketball players in the MOT task and their basketball match performance. The present study found several relationships between measures of visual attention and match performance variables. Mangine et al. (2014) examined the assessment of AVTS in NBA players and game-related measures of productivity. Consistent with our findings, their results indicated that greater AVTS may contribute to more positive plays as reflected by greater rates of assists, steals, and turnovers. In another team ball sport, Poltavski & Biberdorf (2015) showed that faster response time and recognition speed of visual stimulation and a better visual shifting ability are significantly related to sports performance among collegiate hockey players. The results of the present study showed that greater AVTS is highly and positively correlated with the number of assists, indicating that athletes with good dynamic visual attention will have more assists in competition. In basketball, players monitor the movements and positions of several players (teammates and opponents) to make good passes rapidly, which is similar to tracking multiple objects simultaneously in the MOT task. Players with fast visual information processing will make more assists. Similarly, we found that steals also had a positive, large, and statistically significant correlation with greater tracking speed in the MOT task. In a basketball match, the skill and ability to steal the ball are signs of a quality defense. A player in a defense position has to rapidly judge and analyze key visual information to make an interception in time. Previous research has suggested that experienced basketball players reflect better visual perceptual processing to extract critical information in basketball than other sports-specific skills (Millslagle, 2002). However, good visual attention is the basis for and execution of a steal. The present study did not detect any other relationships between AVTS and the other game-related statistics examined.

Our findings supported our hypotheses in part. Basketball players with good dynamic visual attention characteristics were likely to contribute to more assists and steals. However, individual assist and steal performances are affected by many factors, including core stability and physical agility (Mcgill, Andersen & Horne, 2012). Thus, a direct one to one relationship is not expected. Nevertheless, our results do suggest that good visual attention in complex and unpredictable dynamic contexts is a critical component during match performance of elite athletes.

Limitations

Study limitations to the present study to be pointed. It is just a correlational study, not a Randomized Controlled trial. It is impossible for our paradigm to assign subjects randomly between the conditions, it is always possible that basketball players are not better in tracking faster objects, but that people who were good at tracking fast objects continued playing basketball. Second, previous studies suggested that different positions have different visual attention characteristics because of their responsibilities on the basketball court, future research can explore the relationship between visual attention characteristics and sports performance in different positions.