While the visual environment contains massive amounts of information, we should not and cannot pay attention to all events. Instead, we need to direct attention to those events that have proven to be important in the past and suppress those that were distracting and irrelevant. Experiences molded through a learning process enable us to extract and adapt to the statistical regularities in the world. While previous studies have shown that visual statistical learning (VSL) is critical for representing higher order units of perception, here we review the role of VSL in attentional selection. Evidence suggests that through VSL, attentional priority settings are optimally adjusted to regularities in the environment, without intention and without conscious awareness.

虽然视觉环境包含大量信息，但我们不应该也不可能关注所有事件。相反，我们需要将注意力集中在那些过去被证明很重要的事件上，并压制那些分散注意力和无关紧要的事件。通过学习过程塑造的经验使我们能够提取和适应世界上的统计规律。虽然以前的研究表明，视觉统计学习（VSL）对于表示更高阶的感知单位至关重要，但我们在这里回顾了 VSL 在注意力选择中的作用。有证据表明，通过 VSL，注意力优先级设置可以根据环境的规律进行最佳调整，而无需有意和有意识的意识。