Visual statistical learning in human infants

描述(申请人提供)：本研究项目的目标是确定发育中的人类婴儿如何形成视觉世界的表征。研究的重点是一类强大的学习机制，首席研究员和他的同事们已经证明，这种机制可以从听觉刺激序列中快速提取形成连贯单元(例如，单词和旋律)的统计特性。将对视觉统计学习进行研究，以确定在听力以外的其他通道中是否存在一套类似的强大机制。从3个月到12个月大的婴儿，以及成年人，都将接受各种统计学习任务的测试，在这些任务中，小的视觉形状被安排到场景中。争论的焦点是婴儿和成年人如何学会这些形状中的一些在许多不同的场景中一起出现(共同出现)，形成在记忆中表示这些场景的基本构件。四种不同的技术将用于婴儿。主要技术包括重复呈现由3-6个不同形状组成的16-28个不同场景的序列。在观看这些显示的时间(习惯性)减少之后，婴儿将看到包含嵌入在场景中的连贯(高度统计相关性)和不连贯(不相关)形状的测试显示。其他三种技术包括强制选择优先观看、自动角膜反射眼球跟踪和预期眼球运动到学习类别。这些技术将被用来确定新学习的特征是否在混乱的场景中激活注意力，当场景的低级别属性竞争注意力时如何学习这些特征，输入统计中的变化如何影响特征学习的准确性，以及其他知觉限制如何影响特征学习。Pl的统计方法的一个关键假设将得到检验--学习者代表复杂元素阵列中最大的连贯单元，而不是代表与这个更大单元冗余的所有嵌入元素。综上所述，这些拟议的研究将揭示婴儿和成年人如何从复杂的视觉场景中学习新信息，并以高效的计算方式表示这些信息。不能有效地学习可能会导致婴儿期学习的早期阶段出现缺陷，并对更高水平类别的形成产生负面影响。

DESCRIPTION (provided by applicant): The goal of this program of research is to determine how the developing human infant forms representations of the visual world. The focus of the research is on a class of powerful learning mechanisms that have been shown by the Principal Investigator and his colleagues to rapidly extract from sequences of auditory stimuli the statistical properties that form coherent units (e.g., words and melodies). Visual statistical learning will be studied to determine whether a similarly powerful set of mechanisms is present in a modality other than audition. Infants ranging in age from 3- to 12-months of age, as well as adults, will be tested on a variety of statistical learning tasks in which small visual shapes are arranged into scenes. At issue is how infants and adults learn that some of these shapes appear together (co-occur) across many different scenes, forming the basic building blocks for representing those scenes in memory. Four different techniques will be used with infants. The primary technique involves the repeated presentation of a sequence of 16-28 different scenes composed of 3-6 different shapes. After a decline in looking time (habituation) to these displays, infants will be presented with test displays containing coherent (high statistical relatedness) and incoherent (unrelated) shapes that were embedded in the scenes. The other three techniques involve forced-choice preferential looking, automated corneal reflection eye-tracking, and anticipatory eye-movements to learned categories. These techniques will be used to determine whether newly learned features activate attention in cluttered scenes, how these features are learned when low-level properties of the scenes compete for attention, how variations over time in the input statistics affect the accuracy of feature learning, and how other perceptual constraints affect feature learning. A key hypothesis of the Pl's statistical approach will be tested -- that learners represent the largest coherent unit in a complex array of elements, rather than also representing all of the embedded elements that are redundant with this larger unit. Taken together, these proposed studies will reveal how infants and adults learn new information from complex visual scenes and represent that information in a computationally efficient manner. Failure to learn efficiently could lead to deficits in the early phases of learning in infancy and negatively affect the formation of higher level categories.