Unpicking influences on goal-directed behaviour across the lifespan: a computational modelling approach

消除对整个生命周期目标导向行为的影响：计算建模方法

• 批准号: 2546798
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2546798
• 关键词: Unpicking; influences; goal; directed; behaviour

Goal-directed behaviour [GDB] involves focusing on relevant information and ignoringdistractors based on specific goals. Nonetheless, goal-directed actions do not always lead tothe desired outcome. Two personality traits thought to influence GDB are impulsivity andcompulsivity, with the former initiating actions and the latter terminating them. Importantly,impulsivity and compulsivity vary over short periods of time [1] and across the lifespan [2],though the two are predominantly studied at trait level [3]. The link between real-life statemeasures and physiological and neural markers remains unclear, and their effects on GDB isunknown. Using an accelerated longitudinal design, the aim of this computationaldevelopmental neuroscience project is to investigate the relationship between impulsivity,compulsivity, and GDB across the lifespan [4].This project will use a smartphone app to collect real-world, ecologically valid measures ofimpulsivity, compulsivity and GDB during three developmental periods: late adolescence[18-24yrs], middle adulthood [35-45yrs], and older adulthood [55-65yrs]. Longitudinalmeasurements from a large sample will capture inter- and intra-individual differences anddata will be analysed using network analysis. Laboratory measures of impulsivity,compulsivity and GDB will be collected in a subset of the sample. For example, we haveestablished a Pavlovian conditioning task with eye-tracking to measure goal-trackingbehaviour. Resting state fMRI data will be employed to assess how differences across thelifespan in state and trait impulsivity and compulsivity relate to within- and between-restingstatenetwork architecture. Data will be analysed using computational modelling approaches.The primary supervisor will lead on behavioural neuroscience, including laboratory measuresof impulsivity, compulsivity and GDB using eye-tracking. The secondary supervisor will leadon computational modelling, longitudinal experience sampling and rs-fMRI. The twosupervisors provide distinct theoretical knowledge for this interdisciplinary project, with theprimary supervisor providing input relating behavioural neuroscience and the secondarysupervisor concerning computational neuroscience.

目标导向行为[GDB]涉及关注相关信息，并基于特定目标排除干扰。尽管如此，目标导向的行动并不总是导致预期的结果。人们认为影响GDB的两种人格特质是冲动性和强迫性，前者是行动的发起者，后者是行动的终止者。重要的是，冲动性和强迫性在短时间内[1]和整个生命周期[2]都有变化，尽管这两个主要是在特质水平上研究的[3]。现实生活中的状态测量与生理和神经标记之间的联系尚不清楚，它们对GDB的影响也不清楚。使用加速纵向设计，这个计算发育神经科学项目的目的是调查冲动性，强迫性和GDB之间的关系在整个生命周期[4]。这个项目将使用智能手机应用程序来收集真实世界，在三个发展阶段的冲动性，强迫性和GDB的生态有效措施：青春期后期[18- 24岁]、中年[35- 45岁]和老年[55- 65岁]。大样本的纵向测量将捕获个体间和个体内的差异，并使用网络分析对数据进行分析。冲动性、强迫性和GDB的实验室测量将在样本的一个子集中收集。例如，我们已经建立了一个巴甫洛夫条件反射任务与眼动跟踪来衡量目标跟踪行为。静息状态的功能磁共振成像数据将被用来评估在状态和特质冲动性和强迫性方面的差异与静息状态内和静息状态间网络结构的关系。数据将使用计算模型方法进行分析。主要主管将领导行为神经科学，包括实验室测量冲动性，强迫性和GDB使用眼动追踪。二级主管将领导计算建模，纵向经验抽样和rs-fMRI。两名主管为这个跨学科项目提供了不同的理论知识，主要主管提供有关行为神经科学的输入，次要主管提供有关计算神经科学的输入。