Humans can extract co-occurrence regularities from their environment, and use them for learning. This statistical learning ability (SL) has been studied extensively as a way to explain how we learn the structure of our environment. These investigations have illustrated the impact of various distributional properties on learning. However, almost all SL studies present the regularities to be learned in uniform frequency distributions where each unit (e.g., image triplet) appears the same number of times: While the regularities themselves are informative, the appearance of the units cannot be predicted. In contrast, real-world learning environments, including the words children hear and the objects they see, are not uniform. Recent research shows that word segmentation is facilitated in a skewed (Zipfian) distribution. Here, we examine the domain-generality of the effect and ask if visual SL is also facilitated in a Zipfian distribution. We use an existing database to show that object combinations have a skewed distribution in children's environment. We then show that children and adults showed better learning in a Zipfian distribution compared to a uniform one, overall, and for low-frequency triplets. These results illustrate the facilitative impact of skewed distributions on learning across modality and age; suggest that the use of uniform distributions may underestimate performance; and point to the possible learnability advantage of such distributions in the real-world.

人类可以从其环境中提取共现规律，并将其用于学习。这种统计学习能力（SL）已被广泛研究，以解释我们如何学习环境结构。这些调查说明了各种分布特性对学习的影响。但是，几乎所有的SL研究都以均匀的频率分布显示了要学习的规律，其中每个单位（例如，图像三重态）出现相同的次数：虽然规律本身是有益的，但无法预测单位的外观。相反，现实世界中的学习环境（包括儿童听到的单词和他们看到的对象）并不统一。最近的研究表明，在偏斜（Zipfian）分布中有利于分词。这里，我们检查了效果的领域一般性，并询问是否在Zipfian分布中也促进了视觉SL。我们使用现有数据库显示对象组合在儿童环境中的分布偏斜。然后，我们证明，相比于统一的，整体的和低频的三胞胎，儿童和成年人在Zipfian分布中表现出更好的学习。这些结果说明了偏态分布对跨模态和年龄学习的促进作用；建议使用均匀分布可能会低估性能；并指出这种分布在现实世界中可能具有的学习优势。的环境。然后，我们证明，相比于统一的，整体的和低频的三胞胎，儿童和成年人在Zipfian分布中表现出更好的学习。这些结果说明了偏态分布对跨模态和年龄学习的促进作用；建议使用均匀分布可能会低估性能；并指出这种分布在现实世界中可能具有的学习优势。的环境。然后，我们证明，相比于统一的，整体的和低频的三胞胎，儿童和成年人在Zipfian分布中表现出更好的学习。这些结果说明了偏态分布对跨模态和年龄学习的促进作用；建议使用均匀分布可能会低估性能；并指出这种分布在现实世界中可能具有的学习优势。