NCS-FO: Using fMRI to revise psychological variables

理解人类行为和认知的可变性，无论是在健康还是疾病方面，最大的挑战之一是科学心理学和神经生物学知识之间的相对脱节。人们对性格、智力和其他变量之间的关系知之甚少，我们也不知道它们是如何从大脑活动中产生的。这个NSF-EAGER项目的首要的、高风险的目标是利用神经科学数据来发现大脑的结构：我们能从对大脑的了解中建立一种新的心理学吗？这项工作的出发点是，如果这些心理变量以数据驱动的方式从神经成像数据中推导出来，那么人们可能会得到一个非常不同的清单，而不是人格、智力、积极情绪、注意力和其他当前的心理结构，这些都不是基于神经科学数据得出的。这个项目是探索性的，因为没有现存的途径来解决这个问题。该项目将使用因果发现领域的方法，主要是询问我们是否可以识别神经成像数据中的特征，这些特征最好地解释了新的、修订的心理变量。结果将证明这种方法可以产生新的心理结构，如果成功，将为这个新领域的发展提供一个初步的方向。该项目还将在整个活动中使用开放科学实践。这个EAGER项目调查了心理学和大脑功能的个体差异，以解决一个高风险、高回报的问题：我们能否利用神经科学来修正心理学？它将专注于探索在现实世界中发挥作用最重要的两个领域：人格和智力。在从神经成像数据预测人格维度和智力的经典衍生方面已经取得了成功，但这种方法本质上受到最初使用的特定心理结构的限制。下一步是根据静息状态fMRI数据估计的因果图，根据神经可预见性对测试结果进行最佳组合。该项目还将使用一种全新的、完全由数据驱动的方法——因果特征学习（CFL），从功能磁共振成像（fMRI）测量中自动导出候选因果变量，这些变量可能解释用于确定个性和智力的行为和心理测量。CFL已经成功地以这种方式用于分析气候数据，但它从未应用于神经科学。该项目旨在对心理变量和认知架构的哲学和心理学概念化做出实质性贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

One of the greatest challenges to understanding variability in human behavior and cognition, in both health and disease, is the relative disconnection between scientific psychology and neurobiological knowledge. Not much is known of how personality, intelligence, and other variables are related, and we do not know how they arise from brain activity. The overarching, high-risk goal of this NSF-EAGER project is to use neuroscience data to discover the architecture of the mind: can we build a new psychology from knowledge about the brain? The starting point of this work is the notion that instead of personality, intelligence, positive mood, attention and other current psychological constructs, none of which were derived based on neuroscience data, one might end up with a very different inventory if these psychological variables were derived in a data-driven fashion from neuroimaging data. This project is exploratory, since there are no extant paths for approaching this issue. The project will use approaches from the field of causal discovery, essentially asking if we can identify features in neuroimaging data that best explain new, revised psychological variables. The results will be a proof-of-principle that such an approach could yield new psychological constructs, and if successful will provide an initial direction in which this new field could evolve. The project will also use open science practices throughout its activities. This EAGER project investigates individual differences in psychology and brain function to address a high-risk, high-payoff question: can we use neuroscience to revise psychology? It will focus on probing two domains of utmost importance to functioning in the real world: personality and intelligence. There has been success in predicting classical derivations of personality dimensions and intelligence from neuroimaging data, but this approach is inherently limited by the particular psychological constructs used in the first place. The further step taken here is to optimally combine test results in light of neural predictability, based on causal graphs that are estimated from resting-state fMRI data. The project will also use a novel, entirely data-driven approach, Causal Feature Learning (CFL), to automatically derive candidate causal variables from the fMRI measurements that may explain the behavioral and psychometric measures used to determine personality and intelligence. CFL has been used successfully in this fashion to analyze climate data, but it has never been applied to neuroscience. The project intends to make substantial contributions to philosophical and psychological conceptualizations of mental variables and cognitive architecture.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.