Error-driven learning: Connecting language and cognition

错误驱动学习：连接语言和认知

• 批准号: 8066360
• 负责人: Alexa Ruth Romberg
• 金额: $ 1.12万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8066360
• 关键词: Address; Adult; Area; Behavior; Behavioral; Child; Cognition; Cognitive; Comprehension; Cues; Data; Development; Elements; Event; Eye Movements; Future; Generations; Goals; Hearing; Human; Impaired cognition; Infant; Knowledge; Language; Language Development; Language Disorders; Learning; Linguistics; Literature; Measures; Methods; Neurobiology; Outcome; Participant; Performance; Placebo Effect; Play; Population; Probability Learning; Process; Psychology; Reaction Time; Relative (related person); Research; Research Design; Research Personnel; Rewards; Risk; Role; Signal Transduction; Source; Specificity; Speech; Stimulus; Stream; Testing; Time; Toddler; Update; Visual; Work; expectation; infancy; language processing; novel; programs; research study; skills; theories; young adult

DESCRIPTION (provided by applicant): It is well known that the speech stream contains rapidly unfolding, locally ambiguous information. Human infants and adults are highly sensitive to statistical regularities in linguistic input. Recent research has demonstrated that skilled language users capitalize on myriad sources of information, including statistical regularities, to make specific, real-time predictions about upcoming linguistic events. Work in other domains, such as probability learning, has shown that predictions are a rich substrate for learning: Mismatches between predicted and actual outcomes generate error signals, which can then be used to refine future predictions. Little is known, however, about how this process of refinement takes place, how it applies to predictions made during language processing, and how it changes over development. The proposed research is a novel set of studies exploring the hypothesis that error-driven learning is a domain-general learning mechanism that subserves linguistic and nonlinguistic processing and learning. The three aims of the proposal are 1) to determine whether error signals are incorporated into behavior immediately in linguistic and nonlinguistic tasks, 2) to determine whether behavioral change driven by error signals is sensitive to the magnitude of the expectation that led to the signal and 3) compare behavioral change driven by error signals in infants and adults. We will use a probability learning framework, in which adult and infant participants will learn probabilistic associations between (linguistic or nonlinguistic) elements. We will use reaction times (adults) and anticipatory eye movements (infants and adults) to measure participants' learning and adaptation to unexpected elements. RELEVANCE: The goal of the proposed research is to investigate error-driven learning processes in linguistic and nonlinguistic contexts in typically developing infants and young adults. Error-driven learning is a basic, domain-general mechanism that could help learners track statistical regularities in language, a skill important for multiple components of language acquisition and use. Understanding the role of error-driven learning in language processing and acquisition is essential to a complete picture of language learning mechanisms, and will allow researchers to determine how disruption of those mechanisms leads to atypical language development and use.

描述（由申请人提供）：众所周知，语音流包含快速展开的、局部模糊的信息。人类婴儿和成人对语言输入中的统计错误高度敏感。最近的研究表明，熟练的语言使用者利用无数的信息来源，包括统计数据，对即将发生的语言事件做出具体的实时预测。在其他领域的工作，如概率学习，已经表明预测是学习的丰富基础：预测和实际结果之间的不匹配产生错误信号，然后可以用来改进未来的预测。然而，人们对这个细化过程如何发生、它如何应用于语言处理过程中做出的预测以及它如何在发展过程中发生变化知之甚少。本研究是一组新的研究，探讨了错误驱动学习是一种有助于语言和非语言加工和学习的领域一般学习机制的假设。该提案的三个目的是：1）确定错误信号是否立即纳入语言和非语言任务中的行为，2）确定错误信号驱动的行为变化是否对导致信号的期望值的大小敏感，3）比较婴儿和成人错误信号驱动的行为变化。我们将使用一个概率学习框架，其中成人和婴儿参与者将学习（语言或非语言）元素之间的概率关联。我们将使用反应时间（成人）和预期眼球运动（婴儿和成人）来衡量参与者对意外因素的学习和适应。相关性：本研究的目的是调查典型发育中的婴儿和年轻人在语言和非语言环境中错误驱动的学习过程。错误驱动学习是一种基本的、领域通用的机制，可以帮助学习者跟踪语言中的统计错误，这是一种对语言习得和使用的多个组成部分都很重要的技能。理解错误驱动学习在语言处理和习得中的作用对于全面了解语言学习机制至关重要，并将使研究人员能够确定这些机制的破坏如何导致非典型语言的发展和使用。