Time series models of intraindividual variability

个体内变异性的时间序列模型

• 批准号: 7093739
• 负责人: DANIEL H SPIELER
• 金额: $ 6.54万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7093739
• 关键词: age difference; aging; attention; behavioral /social science research tag; clinical research; cognition; data collection methodology /evaluation; human old age (65+); human subject; longitudinal human study; mathematical model; memory; model design /development; psychoanalysis; psychological models; statistics /biometry; time resolved data; young adult human (21-34)

DESCRIPTION (provided by applicant): Measures of cognitive performance are subject to age-related changes. In addition to changes in the level of these measures, there are also consistent increases in intraindividual variability. Understanding the meaning of these age-related changes in intraindividual variability is important both for a basic understanding of cognitive aging and for understanding of the diagnostic utility of variability measures. Theoretical accounts for age-related increases in intraindividual variability often refer to time dependent processes such as fluctuations or lapses of attention. Data in this context are rarely analyzed as time series despite this being the most appropriate form to reveal such processes. We propose the application of time series analyses to explore the sequential relations in performance for young and older adults. We show preliminary evidence that time series data from both young and older adults are long range dependent (LRD). LRD refers to the fact that relations can be observed in time series data spanning many trials. Temporal aggregation of component cognitive processes can produce LRD and the strength of LRD is influenced by the operation of longer time scale processes that include executive control processes that act to maintain performance over periods of time. Thus, LRD may be useful for understanding how these processes are influenced by variations in processing demands and the aging process. However, tools that have been used to model these dependencies called ARFIMA models make assumptions of stationarity that do not hold for behavioral data. We propose SEMIFAR models as better suited to measuring LRD and are robust in the face of nonstationarity of behavioral data. We also propose the application of GARCH/ARCH models that capture sequential relations in variability. Preliminary data suggests the time series for young and older adults show volatility clustering. In other words, high and low variability periods cluster together within an individual's time series. Together, these models provide robust methods for exploring the contribution of time dependent processes to intraindividual variation. Four experiments will be analyzed using these models to account for age differences in these components of intraindividual variabilty. These experiments explore manipulations designed to influence sequential dependencies within young and older adults and extend our understanding of sources of intraindividual variability.

描述（由申请人提供）：认知表现的度量应遵守与年龄相关的变化。除了这些度量水平的变化外，个体内变异性也有一致的增加。了解这些与年龄相关的内部变异性变化的含义对于对认知衰老的基本理解以及对可变性度量的诊断效用的理解既重要。关于年龄相关的内部变异性增加的理论上，通常是指随时间依赖的过程，例如波动或关注的失误。尽管这是揭示此类过程的最合适形式，但在这种情况下，很少将数据分析为时间序列。我们提出了时间序列分析的应用，以探索年轻人和老年人的绩效顺序关系。我们表明了初步证据表明，来自年轻人和老年人的时间序列数据都是长范围依赖性的（LRD）。 LRD指的是，可以在跨越许多试验的时间序列数据中观察到关系。组件认知过程的时间聚集可以产生LRD，LRD的强度受到较长的时间尺度过程的运行的影响，这些过程包括包括执行控制过程，这些过程在时间上保持绩效。因此，LRD可能有助于理解这些过程如何受处理需求和衰老过程的变化影响。但是，已用于模拟这些依赖关系的工具称为Arfima模型，这使得平稳性的假设不适合行为数据。我们提出的半比例模型更适合于测量LRD，并且面对行为数据的非平稳性。我们还提出了在可变性中捕获顺序关系的GARCH/ARCH模型的应用。初步数据表明，年轻人和老年人的时间序列显示出波动性聚类。换句话说，高和低的可变性周期聚集在个人的时间序列中。这些模型共同提供了强大的方法，用于探索时间依赖过程对内部变化的贡献。将使用这些模型分析四个实验，以说明这些内部变化的这些组成部分的年龄差异。这些实验探索了旨在影响年轻人和老年人内的顺序依赖性的操纵，并扩展了我们对内部变异性来源的理解。