视觉系统功能与精英足球运动员视觉认知技能之间的相关性
Jorge Jorge1, João Pedro Jorge2, Sandra Medrano Muñoz3
1Clinical and Experimental Optometry Research Laboratory (CEORLab), Physics Centre of Minho and Porto Universities (CF-UM-UP), University of Minho, Braga, Portugal.
Clinical & experimental optometry
|January 22, 2026
概括
视觉系统参数,如双眼视力和折射误差影响精英运动员的视觉认知能力. 饮食失调是一种眼睛失调,与足球运动员表现较差有关.
科学领域:
- 眼科和体育科学 眼科和体育科学
- 视觉神经科学是一种神经科学.
- 人类表现的人类表现.
背景情况:
- 了解视觉系统参数和视觉认知能力之间的关系对于优化视觉性能至关重要.
- 关键的视觉参数包括视觉敏度,折射误差和双眼视觉指标.
- 评估的视觉认知能力是感知范围,多重对象跟踪和视觉反应时间.
研究的目的:
- 研究精英足球运动员特定视觉系统参数与视觉认知能力之间的关系.
- 确定视力敏度,折射误差和双眼视力等因素如何影响与体育相关的认知视觉功能.
主要方法:
- 对218名精英男性足球运动员进行了视力敏度,折射误差和双眼视力的全面评估.
- 用测试来评估视觉认知能力,测试了感知跨度,多重对象跟踪和反应时间.
- 用皮尔森的相关性测试来分析视觉和视觉认知参数之间的关系,显著性设置为p < 0.05.
主要成果:
- 感知跨度显示了与视敏度的正相关性,以及与无视度和近食的负相关性.
- 多重物体跟踪性能与水平焦虑症和立体焦虑症有负面关联,特别是在食欲焦虑症患者中.
- 视觉反应时间与吸血和距离水平光度正相关.
结论:
- 双眼视觉,和光显著影响精英足球运动员的视觉认知表现.
- 良好的双眼协调和最小的折射误差是有益的,而食与性能下降有关.
- 这些发现强调了体育背景下视觉认知功能的复杂多因素性质.
相关概念视频
Correlations
35.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.8K
Cognitive Dissonance
36.8K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
36.8K
Correlation and Causation
42.3K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.3K
Correlation
14.8K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
14.8K
Correlation and Regression
3.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.2K
Coefficient of Correlation
8.5K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.5K


