Visual statistical learning in human infants

人类婴儿的视觉统计学习

• 批准号: 7005389
• 负责人: Richard Aslin
• 金额: $ 23.72万
• 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/7005389
• 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）：这个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，这些机制已经被首席研究员和他的同事们展示出来，可以从听觉刺激序列中快速提取出形成连贯单位（例如，单词和旋律）的统计特性。将研究视觉统计学习，以确定是否存在类似的强大的一套机制，而不是听力的模式。3到12个月大的婴儿以及成年人将接受各种统计学习任务的测试，这些任务是将小的视觉形状排列成场景。争论的焦点是婴儿和成人是如何学会这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的。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。在婴儿对这些显示的观看时间减少（习惯化）之后，他们将看到包含场景中嵌入的连贯（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注视、自动角膜反射眼球追踪和预期的眼球运动。这些技术将用于确定新学习的特征是否在混乱的场景中激活注意力，当场景的低级属性争夺注意力时如何学习这些特征，输入统计数据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。Pl的统计方法的一个关键假设将被测试——学习者代表一个复杂元素数组中最大的连贯单元，而不是代表所有与这个更大的单元冗余的嵌入元素。综上所述，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，并以有效的计算方式表示这些信息。如果不能有效地学习，可能会导致婴儿早期阶段的学习缺陷，并对更高水平类别的形成产生负面影响。