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

目标导向行为（GDB）涉及专注于相关信息，忽略基于特定目标的干扰因素。然而，目标导向的行动并不总能带来预期的结果。人们认为影响GDB的两种人格特征是冲动性和强迫性，前者是行动的发起者，后者是行动的终结者。重要的是，冲动性和强迫性在短时间内和一生中都有所不同，尽管这两者主要是在特征水平上进行研究。现实生活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的影响也尚不清楚。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在整个生命周期中的关系[10]。该项目将使用智能手机应用程序收集现实世界中三个发展时期的冲动性、强迫性和GDB的生态有效测量：青春期晚期[18-24岁]、成年中期[35-45岁]和成年晚期[55-65岁]。来自大样本的纵向测量将捕获个体之间和个体内部的差异，数据将使用网络分析进行分析。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，我们建立了一个巴甫洛夫条件反射任务，通过眼球追踪来测量目标追踪行为。静息状态fMRI数据将用于评估在整个生命周期中状态和特质冲动和强迫的差异如何与静息状态内和静息状态之间的网络结构相关。数据将使用计算建模方法进行分析。首席导师将领导行为神经科学，包括使用眼球追踪对冲动性、强迫性和GDB进行实验室测量。副主管将领导计算模型、纵向经验抽样和核磁共振成像。两位导师为这个跨学科项目提供不同的理论知识，主要导师提供有关行为神经科学的输入，次要导师提供有关计算神经科学的输入。

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.