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CAREER: Validating and applying a new class of drift-diffusion models for investigating individual differences in executive control

CAREER: Validating and applying a new class of drift-diffusion models for investigating individual differences in executive control
职业:验证和应用一类新型漂移扩散模型来研究执行控制的个体差异
批准号:
1650438
负责人:
Corey White
金额:
$56.61万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-06-01 至 2022-05-31

项目摘要

项目成果

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中文摘要
翻译
执行功能是指控制和指导自己的心理过程的能力,这对日常行为至关重要。对于行为科学和认知科学的许多领域的研究人员来说,了解某些人是如何以及为什么会出现注意力集中和抑制行动等执行能力障碍的,是很重要的。这些功能通常是通过测量一个人完成不同心理任务的速度和准确性来研究的。然而,这类任务中的行为可能会受到执行功能之外的多个过程的影响,这使得测量这些功能中的个体差异变得困难。这一问题在目前的研究中使用认知模型来解决。这些模型可以将行为分解成不同的心理成分,从而更深入地了解个体在控制其心理过程的能力方面如何以及为什么会有所不同。这项研究将开发和测试新的执行功能模型。这些模型将提供一类新的工具,用于研究认知功能及其在不同个体之间的差异。研究人员将提供公开可用的统计程序包,允许其他研究人员在他们自己的研究中使用这些程序包。由于执行功能任务被广泛应用于心理学和神经科学的不同领域,这些模型将通过提供经过测试的分析行为数据的工具来广泛影响我们对执行控制的理解。这项研究的主要目的是确定现有的或将要开发的计算模型对执行功能任务中的加工提供最好的理论解释。竞争模型将根据一系列执行功能任务的行为和模拟数据进行测试,这些任务旨在衡量冲突处理、抑制控制和选择性注意。每项任务将包括对不同认知成分的有针对性的操作,以提供对模型的严格测试。然后,将根据它们将行为数据分解为其组成部分认知成分的能力来评估成功的模型,特别是在可以收集有限行为数据的现实世界中。一旦确定了最好的模型,它们将被开发成一个统计包,供公众分发。最后,将提供教程和示例数据集,使研究人员更容易将基于模型的分析纳入他们自己的研究。总体而言,这项研究将1)确定哪些模型最适合分析执行功能任务的数据,2)确定可以使用这些模型的实验条件,3)为研究人员在他们自己的研究中使用这些模型提供软件包和说明。
英文摘要
Executive function refers to the ability to control and direct one's own mental processes, which is crucial for everyday behavior. Understanding how and why certain individuals show impairments in executive abilities like focusing attention and inhibiting actions is important for researchers in many areas of behavioral and cognitive sciences. These functions are typically studied by measuring how quickly and accurately an individual can complete different psychological tasks. However, behavior in such tasks can be affected by multiple processes outside of executive function, making the measurement of individual differences in these functions difficult. This problem is addressed in the current research using cognitive models. These models can break down behavior into different psychological components, allowing a deeper understanding of how and why individuals differ in the ability to control their mental processes. This research will develop and test new models of executive function. These models will provide a new class of tools for investigating cognitive function and how it differs across individuals. The researcher will provide publicly-available statistical packages that allow other researchers to use them in their own studies. As executive function tasks are widely used across different domains in psychology and neuroscience, these models will broadly affect our understanding of executive control by providing tested tools for analyzing behavioral data. The primary objective of this research is to determine which computational models, existing or to be developed, provide the best theoretical account of processing in executive function tasks. Competing models will be tested against behavioral and simulated data from a range of executive function tasks meant to measure conflict processing, inhibitory control, and selective attention. Each task will include targeted manipulations of different cognitive components to provide rigorous tests of the models. Successful models will then be evaluated in terms of their ability to decompose behavioral data into its constituent cognitive components, especially in real-world situations where limited behavioral data can be collected. Once the best models have been identified, they will be developed into a statistical package for public distribution. Finally, tutorials and example data sets will be provided to make it easier for researchers to incorporate the model-based analysis into their own research. Overall the research will 1) identify which models are best for analyzing data from executive function tasks, 2) determine the experimental conditions under which the models can be used, and 3) provide packages and instructions for researchers to use the models in their own studies.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1177/09637214221077060
发表时间: 2022-05
期刊: Current Directions in Psychological Science
影响因子: 7.2
作者: [C. White;Kiah N. Kitchen]
通讯作者: C. White;Kiah N. Kitchen
DOI: 10.1007/s42113-018-0004-6
发表时间: 2018-03
期刊: Computational Brain & Behavior
影响因子: --
作者: [C. White;R. Curl]
通讯作者: C. White;R. Curl
海外基金