Visual statistical learning in human infants

人类婴儿的视觉统计学习

• 批准号: 6698991
• 负责人: Richard Aslin
• 金额: $ 24.29万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-02-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6698991
• 关键词: attention; behavior test; behavioral /social science research tag; clinical research; eye movements; human subject; infant human (0-1 year); learning; psychometrics; statistics /biometry; visual perception; visual tracking

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.

描述（由申请人提供）：本研究计划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，主要研究者和他的同事已经证明，从听觉刺激序列中快速提取形成连贯单元的统计特性（例如，歌词和旋律）。视觉统计学习将被研究，以确定是否有一个类似的强大的机制集是目前在其他形式比听觉。年龄从3个月到12个月的婴儿以及成人将接受各种统计学习任务的测试，其中小的视觉形状被安排成场景。争论的焦点是婴儿和成人如何学习这些形状中的一些形状在许多不同的场景中一起出现（共同出现），形成记忆中代表这些场景的基本构建块。四种不同的技术将用于婴儿。主要技术涉及重复呈现由3-6个不同形状组成的16-28个不同场景的序列。在对这些显示的观看时间（习惯化）下降后，婴儿将被呈现包含嵌入场景中的连贯（高统计相关性）和不连贯（不相关）形状的测试显示。其他三种技术包括强迫选择优先观看，自动角膜反射眼动跟踪，以及预期的眼动学习的类别。这些技术将用于确定新学习的特征是否在杂乱场景中激活注意力，当场景的低级属性竞争注意力时如何学习这些特征，输入统计数据随时间的变化如何影响特征学习的准确性，以及其他感知约束如何影响特征学习。PI的统计方法的一个关键假设将被测试-学习者代表最大的连贯单元在一个复杂的元素阵列，而不是也代表所有的嵌入式元素是多余的这个更大的单位。总之，这些拟议的研究将揭示婴儿和成人如何从复杂的视觉场景中学习新信息，并以计算效率高的方式表示这些信息。未能有效地学习可能会导致婴儿早期学习阶段的缺陷，并对更高层次类别的形成产生负面影响。