Multi-Utterance Language Production

多话语语言生成

• 批准号: 10440438
• 负责人: Fernanda Ferreira
• 金额: $ 32.05万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-13 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10440438
• 关键词: Address; Adolescence; Adult; Attention; Benchmarking; Canis familiaris; Characteristics; Child; Childhood; Clutterings; Cognitive; Complex; Cues; Development; Disease; Environment; Event; Eye; Eye Movements; Face; Felis catus; Head Start Program; Human Development; Image; Knowledge; Language; Lead; Left; Life; Linguistics; Link; Location; Measures; Methods; Mission; Process; Production; Property; Psycholinguistics; Public Health; Research; Research Personnel; Semantics; Speech; Speech Delay; Stuttering; Symptoms; System; Techniques; Testing; Thinking; Vision; Visual; Visual Fields; Visual system structure; base; cognitive system; computerized tools; design; developmental disease; extrastriate visual cortex; innovation; language processing; sample fixation; skills; theories; tool; visual cognition

To tell a story, give directions, or describe the layout of a house, speakers must generate multiple utterances in sequence. Because most psycholinguistic research on speaking is based on paradigms designed to elicit single utterances, little is known about multi-utterance language production in children and adults. Linked to this empirical focus on single utterances is the widespread use of a method in which subjects describe simple visual images containing just a handful of objects shown in straightforward spatial arrangements. In contrast, real world scenes contain multiple objects in various relationships that can be described in numerous ways, and so the attentional and language systems face a challenging set of decisions concerning where to begin the description, how to cluster objects and capture their relations, and what information to include or omit. This project uses complex, real-world scenes as a tool to examine the linearization of complex thoughts into sequenced utterances, focusing on adults at this investigate stage in order to establish developmental benchmarks. Image and semantic characteristics of complex scenes will be precisely quantified and used to generate predictions about the allocation of attention as a scene is viewed and described. The project examines the conditions under which scene image features exogenously draw the eyes to specific visual areas which the linguistic system then describes, and under what conditions the cognitive system guides the eyes to meaningful regions of the scene, allowing the language system to prepare a description even before the relevant object or region has been fixated. On this latter view, the language and cognitive systems use scene meaning interactively to formulate a linearization plan for coordinating the production of multiple utterances. To address these theoretical issues, the project focuses on three Specific Aims: (1) To use computational tools from the field of visual cognition to measure image and meaning properties of complex scenes, which will permit the precise quantification of features controlling attention during speaking tasks as well as the selection and sequencing of linguistic content. (2) To determine the extent to which viewers predict the presence of objects and their locations and use those predictions to get a head-start on linguistic encoding even before an object is attended. (3) To extend our approach to the production of utterances describing events by applying the same methods for quantifying scene image and meaning properties that have been developed for nonevent scenes to scenes depicting events with and without animate agents. The project tests an innovative theory of multi-utterance language production which assumes that speakers formulate a linearization plan to guide the allocation of attention and linguistic decisions concerning inclusion and ordering of information. This approach will lead to a deeper understanding of language production, which will lead to identification of testable, rigorous hypotheses concerning the emergence of these processes across development.

要讲故事、指路或描述房子的布局，说话者必须在不同的环境中发出多个话语。 顺序因为大多数关于口语的心理语言学研究都是建立在旨在引出 单话语，很少有人知道儿童和成人的多话语语言生产。链接到 这种对单一话语的实证关注是一种广泛使用的方法，在这种方法中，受试者描述简单的 视觉图像只包含以简单的空间排列显示的少数对象。与此相反， 真实的世界场景包含可以以多种方式描述的各种关系中的多个对象， 因此，注意力和语言系统面临着一系列关于从何处开始 描述，如何聚类对象并捕获它们的关系，以及包含或省略哪些信息。这 一个项目使用复杂的，真实世界的场景作为工具，以检查线性化的复杂思想， 有序的话语，专注于成年人在这一调查阶段，以建立发展 基准。复杂场景的图像和语义特征将被精确量化并用于 当观看和描述场景时，生成关于注意力分配的预测。项目 研究了场景图像特征外生地将眼睛吸引到特定视觉区域的条件 然后语言系统描述了什么，在什么条件下认知系统引导眼睛去看 场景中有意义的区域，允许语言系统甚至在 相关物体或区域被固定。根据后一种观点，语言和认知系统使用场景 这意味着交互地制定用于协调多个话语的产生的线性化计划。到 为了解决这些理论问题，该项目集中在三个具体目标：（1）使用计算工具 从视觉认知领域来衡量复杂场景的图像和意义属性， 允许在说话任务期间控制注意力的特征的精确量化以及选择 和语言内容的排序。(2)为了确定观众在多大程度上预测 对象及其位置，并使用这些预测来获得语言编码的领先优势， 对象被关注。(3)为了扩展我们的方法，通过应用 同样的方法量化场景图像和意义属性已开发的nonevent 场景到场景描绘有和没有动画代理的事件。该项目测试了一种创新的 一种多话语语言产生理论，假设说话者制定线性计划， 指导注意力的分配以及关于信息的收录和排序的语言决定。这 这种方法将导致对语言产生的更深入的理解，这将导致识别 关于这些过程在整个发展过程中出现的可检验的严格假设。