Prediction in Language Across the Lifespan

整个生命周期的语言预测

• 批准号: 10291114
• 负责人: Rachel Ryskin
• 金额: $ 45.48万
• 依托单位: UNIVERSITY OF CALIFORNIA, MERCED
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10291114
• 关键词: Age; Age-Years; Aging; Alzheimer' s Disease; Brain; Cognitive; Communication; Complex; Comprehension; Data; Dementia; Detection; Deterioration; Disease; Early Diagnosis; Elderly; Exposure to; Friends; Future; Health; Human; Impaired cognition; Investigation; Knowledge; Language; Language Disorders; Lead; Light; Linguistics; Longevity; Measurement; Measures; Modeling; Nature; Nerve Degeneration; Neurobehavioral Manifestations; Pattern; Persons; Population; Probability; Production; Property; Quality of life; Research; Resources; Sampling; Semantics; Series; Short-Term Memory; Source; Stimulus; Structure; Testing; Time; Training; Vocabulary; Work; adjudicate; age effect; age group; age related; base; behavior test; behavioral study; cognitive function; design; effective intervention; executive function; expectation; experience; experimental study; healthy aging; improved; insight; language comprehension; language impairment; language processing; neuroimaging; novel strategies; preservation; programs; statistics; support network; syntax; young adult

Cognitive functioning fluctuates over the lifespan, across myriad domains. Though many aspects of language comprehension are preserved and/or continue to improve with age (e.g., vocabulary), older adults (OA) do not appear to predict upcoming linguistic input to the same extent as younger adults (YA). This finding is surprising given current views that prediction is a fundamental aspect of human language comprehension. On a Resource-based account, this alleged decline in linguistic prediction is due to a general age-related decline in executive functions. However, the evidence for this account is mixed. The present work tests key predictions of a competing Experience-based Account account whereby linguistic prediction is intact in OA, but the nature of the predictions differs from those of YA. This proposal builds on recent information-theoretic accounts of language processing, according to which comprehenders derive linguistic predictions using the local context (e.g., preceding words) along with their long-term knowledge of the language, accumulated across their lifespan. The critical observation at the core of the Experience-based proposal is that OA may make different predictions given the many additional years of language exposure over different time periods (over which language statistics fluctuate). In order to test the Resource-based and Experience-based accounts, the current research proposes two complementary approaches: Aim 1: Reveal how language statistics change over a person’s lifespan. Large-scale, diachronic language corpora will be used to characterize the changes in semantic and syntactic co-occurrences over the course of several decades. Language models (n-gram, RNN) will then be used to test the hypothesis that the predictability of a word/sentence changes depending on the source of language experience (e.g., the time period from which training data are sampled). Aim 2: Generate a large-scale stimulus set for the investigation of predictability across the lifespan. A series of behavioral studies will be conducted online, in order to compare predictions generated by OA and YA given the same contexts and develop age-specific norms for a large set of stimuli which can be used for designing future experiments on semantic and syntactic predictability. Critically, these norms will be collected in a large age-continuous sample (age 18-80) and will provide a direct measurement of contextual language statistics across the lifespan. The research proposed here will shed critical new light on the effects of aging on language processing. The findings will inform a) early detection of dementia by delineating the effects of healthy aging and neurodegeneration on language comprehension and b) strategies for supporting cognitive health across the lifespan (e.g., through linguistic or executive function-based training).

认知功能在一生中的各个领域都会发生波动，尽管语言理解的许多方面都随着年龄的增长而得以保留和/或持续改善（例如词汇量），但老年人 (OA) 似乎无法以相同的程度预测即将到来的语言输入。鉴于当前的观点认为预测是人类语言理解的一个基本方面，这一发现令人惊讶。 在基于资源的账户中，这种所谓的语言预测下降是由于与年龄相关的执行功能普遍下降，但是，目前的工作测试了基于经验的账户账户的关键预测。因此，OA 中的语言预测是完整的，但预测的性质与 YA 的预测不同。该提议建立在最近的语言处理信息理论基础上，根据该理论，理解者使用本地上下文（例如前面的单词）得出语言预测。连同他们的基于经验的提案核心的关键观察是，鉴于不同时期（语言统计数据波动）的额外年数接触语言，OA 可能会做出不同的预测。 ）为了测试基于资源和基于经验的账户，当前的研究提出了两种互补的方法： 目标 1：揭示语言统计数据在人的一生中如何变化 大规模、历时语言语料库将用于描述几十年来语义和句法共现的变化（n-gram、RNN）。然后将用于测试以下假设：单词/句子的可预测性根据语言经验的来源（例如，采样训练数据的时间段）而变化。 目标 2：生成大规模刺激集，用于研究整个生命周期的可预测性。将在线进行一系列行为研究，以便比较 OA 和 YA 在相同背景下生成的预测，并制定特定年龄的规范。一组可用于设计未来语义和句法可预测性实验的大量刺激，重要的是，这些规范将在一个大的年龄连续样本（18-80 岁）中收集，并将提供上下文语言的直接测量。整个生命周期的统计数据。 这里提出的研究将为衰老对语言处理的影响提供重要的新线索，这些发现将通过描述健康衰老和神经退行性变对语言理解的影响来早期发现痴呆症，以及支持整个认知健康的策略。寿命（例如，通过基于语言或执行功能的培训）。

项目成果

Prediction during language comprehension: what is next?

语言理解过程中的预测：接下来会发生什么？

• 发表时间: 2023-11
• 影响因子: 19.9
• 作者: Ryskin, Rachel;Nieuwland, Mante S
• 通讯作者: Nieuwland, Mante S