Effects of Motion Vision and Neural Efficiency on Target Capture in Basketball Players

After long-time training and competition, excellent basketball players accumulate rich experience and can accurately capture the trajectory of balls and react quickly and timely to block and shoot. In complex sports scenarios, athletes complete relevant techniques. Most of the current studies focus on sports biomechanics, and there are fewer studies on psychological and neurological aspects; however, some studies focus on the characteristics of athletes' electro-encephalogram (EEG) signals and neural mechanisms [1]. Wei et al. [2] compared the morphological characteristics of the brains of short-track speed skaters and the general population by magnetic resonance imaging and found that athletes had significantly increased cortical thickness in the left precuneus, left inferior parietal, and right superior frontal lobes. Park et al. [3] compared the differences in mental stress between athletes and nonathletes and found that athletes had more stable hemodynamic response variability. Krizman et al. [4] compared athletes and nonathletes by frequency following response (FFR) measurements and found that athletes had a greater response to sound. They concluded that exercise could increase the gain of auditory signals by reducing background noise. Gong et al. [5] studied the EEG characteristics of high-level shooting athletes and found that athletes had higher connectivity in the left temporal, left posterior temporal, left frontal, left middle, and right parietal regions and tighter coupling of brain functions than nonathletes. In ball sports, target capture is an important element. Only when the trajectory of the ball is captured quickly and accurately can the subsequent movement be carried out smoothly. Therefore, this paper took basketball players as an example, studied the motion vision and neural efficiency of target capture during sports, and compared them with nonathletes to understand their visual and neural differences. This work aims to provide some reliable bases for improving the athletic performance of basketball players.

The research subjects were 200 males randomly selected from the college students interested in participating in this research in Chengdu. There were 100 basketball players and 100 nonathletes. Physical examination showed that they had normal visual acuity or corrected visual acuity. The right hand was their dominant hand. They had good health, no history of psychiatric disorders, traumatic brain injury, neurological disorders, and bad hobbies. They understood the experimental content and signed the informed consent form. The general information on the subjects is shown in Table 1.

Six hundred pictures of basketball games were obtained from the Internet, including 300 with balls and 300 without balls. The example pictures are shown in Fig. 1. Every picture had 1024 × 710 pixels and the same brightness and saturation.

The experimental task was to determine whether the picture contained a ball or no ball to understand the target capture ability of the research subjects. The stimuli were presented using the e-prime psychological experiment system (Psychology Software Tools, Inc., Sharpsburg, MD). The subjects made keystroke responses after looking at the pictures. The experiment adopted the block design pattern. The subjects practiced ten times before the formal experiment and repeated 600 times in the formal experiment. The experimental stimuli were presented randomly. During the experiment, the screen first presented a 500 ms attention point (i.e., a white “+” in the center of the black screen), then a 600 ms experimental stimulus, and finally an 800 ms blank screen (Fig. 2).

The experimental program was written on e-prime software. Keystroke responses were recorded through e-prime V1.1. The experimental stimuli were presented in a notebook. In the motion vision test, the research subject sat on a chair 80 cm away from the notebook. In the neural efficiency test, the experimental stimuli were projected to the research subject through a reflector.

Before the experiment, the experimental content and precautions were explained to the research subjects. The eye movement data were recorded automatically by the eye tracker SMI i View X (Model No. RED, Germany). After the research subjects were ready, whether the instrument could normally operate was checked. The experimenter explained the experimental process to the subjects. The subjects fully practiced after they understood the experiment process. Then, the eye-movement software was opened. The camera and screen were adjusted to ensure that the binocular images could be accurately presented. Fast five-point eye calibration was performed, i.e., calibration points appeared on the specific positions of the screen to control deviations X and Y within 1.0 deg.

After the formal experiment started, the attention point, experimental stimulus, and blank screen were displayed on the screen in turn. The research subject quickly determined whether there was a ball in the picture and responded with a keystroke.

The data to be recorded include:

1. reaction time: the time from stimulus presentation to keystroke response;

2. accuracy: whether a judgment on pictures is right or not;

3. fixation time: duration of the fixation point, reflecting the degree of processing of the material by the research subject; and

4. fixation frequency: fixation times per unit time, reflecting the fixation stability of the research subject.

The data were processed and analyzed in spss 20.0 software. An independent samples t-test was done. The significance level was 0.05.

The experiments were conducted in a Siemens 3 T magnetic resonance imager (Model No. Magnetom Trio 3.0 T, Germany). Before the experiment, the experimenter explained the procedure and precautions to the subject. The subject kept his head completely still during the experiment. After wearing earplugs and fixing the head, the subject lay flat in a magnetic resonance imager and made a keystroke response by observing the experimental stimuli on the screen. The scanning parameters of the magnetic resonance imager are shown in Table 2.

The functional magnetic resonance imaging (fMRI) data were processed in matlab using dparsf software. After head-motion correction, noises were removed by high-pass filters (0.1 Hz), followed by smoothing by Gaussian kernels (full width at half maximum = 6 mm). The smoothed data were spatially normalized.

The comparison between the two groups in reaction time is shown in Fig. 3.

In the motion vision test, the reaction time was 526.78 ± 75.36 ms in group A and 549.37 ± 59.88 ms in group B. It was seen from Fig. 3 that the reaction time of group B was significantly longer than group A (p < 0.05). The result indicated that the nonathletes took a longer time to respond to the experimental stimuli.

The comparison of the correct rate between the two groups is shown in Fig. 4.

In the motion vision test, the correct rate was 94.12 ± 3.45% in group A and 80.78 ± 8.24% in group B. It was seen from Fig. 4 that the correct rate of group B was significantly lower than that of group A (p < 0.05). The result indicated that when making judgments about the experimental stimuli, the athletes had a higher probability of making correct judgments, i.e., they could more accurately capture the spheres in the pictures and make better judgments about the pictures with or without balls.

The comparison of the fixation duration between the two groups is shown in Fig. 5.

In the motion vision test, the fixation duration was 204.77 ± 40.23 ms in group A and 212.36 ± 51.64 ms in group B. It was seen from the comparison in Fig. 5 that the fixation duration of group B was longer than that of group A (p < 0.05). The result indicated that before making a keystroke response, the nonathletes stayed on the fixation point longer, but the athletes could make a judgment within a short fixation time.

In the motion vision test, the fixation frequency was 1.67 ± 0.41 times in group A and 2.49 ± 0.52 times in group B. It was seen from the comparison in Fig. 6 that the fixation frequency of group B was significantly higher than that of group A (p < 0.05). The athletes had a low fixation frequency during the experiment, indicating that the athletes had better fixation stability than nonathletes.

The brain activation areas in the two groups in making judgments about the experimental stimuli are shown in Table 3.

Table 3 shows that when target capture was performed, the brain areas activated in group A were mainly the precentral gyrus, the lateral inferior occipital lobe, the middle frontal gyrus, and the middle occipital gyrus, and the brain areas activated in group B were different from those in group A except for the lateral inferior occipital lobe. In comparison, group B activated more brain areas than group A, indicating that group B consumed more functional brain activities to process visual information before making keystroke responses and group A activated fewer brain areas. In combination with the results of motion vision, group A made a faster response with less activation of brain areas, indicating that group A had higher neurological efficiency.

During sports, athletes capture the target' location in a very short time by visual search [6] and then make quick decisions through the brain. Therefore, understanding athletes' neural mechanisms of is of significance to help athletes improve their athletic performance and reduce sports injuries [7]. Currently, most research on the structure and function of the athlete's brain uses noninvasive and noninjurious methods, such as neuroimaging [8] and neurophysiology [9]. Neurophysiology includes EEG [10] and electromyography (EMG) [11], while neuroimaging is dominated by fMRI [12].

Neurological efficiency refers to better operational performance and a lower degree of neural activation in an individual. The neurological efficiency hypothesis [13] suggests that professional athletes may have undergone some changes in the structure and function of their brains after long-term training, resulting in improved neurological efficiency in athletes. After long-term, repetitive sports training, the cognitive processing of visual and auditory sensations of athletes may have neuroplastic adaptation [14], which is absent in nonathletes. With neuroplastic adaptation, athletes consume fewer neural resources than nonathletes when performing the same action, i.e., the individual is more rational and efficient in completing the operation. This paper investigated the differences between athletes and nonathletes by testing motion vision and neurological efficiency.

The results of the motion vision test demonstrated that athletes showed a shorter reaction time and higher accuracy than nonathletes when performing target capture. Faced with the presented experimental stimuli, group A could find useful information from the interested fixation points faster and process it quickly and accurately based on their experience to make a quick keystroke response; but the nonathletes in group B could not extract the information effectively and consumed more fixation time in the noninterest area due to the lack of sports experience, and they collected every information on the picture and then extracted the useful information from it, so they consumed a lot of attention resources. It was concluded that athletes with better motion vision during target capture could make better captures of key information and reduce the waste of attention.

The results of the neurological efficiency test showed that the athletes activated fewer brain areas during target capture. During long-term learning and training, athletes accumulated rich experience and could react quickly in complex scenes based on their experience. Determining whether a ball was in a picture could be regarded as a process of picture information processing. The brain compared and processed the information collected from binocular vision according to the information in the memory and finally made a decision. In this process, nonathletes mobilized more functional brain activities to process the visual information, eliminating useless information while looking for useful information, and then made keystroke responses. Therefore, athletes had less brain area activation and higher neurological efficiency in target capture.

This paper analyzed the motion vision and neurological efficiency of basketball players and processed and compared the eye-movement data and fMRI data of the research subjects through the task of determining whether there was a ball in the picture. The results showed that:

1. group A had a shorter response time and a higher correct rate;

2. group A had a shorter fixation time and a lower fixation frequency; and

3. group A activated fewer brain areas than group B.

The experimental results showed that the athletes could acquire useful information and respond faster, with less wasted attention, and the accuracy of target capture was higher, indicating that athletes have higher neurological efficiency than nonathletes.