Constraints on Visual Statistical Learning in Infancy

描述（由申请人提供）：视觉统计学习是识别视觉特征之间概率共现模式的过程，这对我们感知世界可预测和稳定的能力至关重要。该实验将首次全面考察2 - 14个月婴儿对视觉序列和布局中复杂统计模式的检测能力，并描述婴儿对概率信息的计算。这是感知和认知发展的形成时期，其特征是对环境结构的感知和学习发生了快速的发展变化。特别是，实验将检验如何

DESCRIPTION (provided by applicant): Visual statistical learning is the process of identifying patterns of probabilistic co-occurrence among visual features, essential to our ability to perceivethe world as predictable and stable. The proposed experiments will provide the first comprehensive examination of infants' detection of complex statistical patterns in visual sequences and layouts b describing computation of probabilistic information by infants from 2 to 14 months. This is a formaive time in perceptual and cognitive development characterized by rapid developmental change in perception and learning of environmental structure. In particular, the experiments will examine how structural variability, cognitive load, and memory limitations affect learning, how contextual cues facilitate or impede this learning, and whether the products of such learning can be generalized toa different setting. In addition, the experiments will provide critical tests of domain-specificity b examining the specific contributions of spatial information to visual statistical learning. The shot-term objectives of the proposed research are to learn how developing perceptual and cognitive skills intract in early development to identify statistically defined patterns. The long-term goals are to clarifytheories of cognitive development that posit a central role for inductive learning by computing probabilitie of observations. The results of this research may have implications for understanding development in children who may be at risk for developmental disorders such as iron-deficiency anemia and autism spectrum disorders, both of which have been characterized as deficits in implicit learning. Such an understanding may lead to assessment tools more closely tailored to early diagnosis and treatment than are presently available.