Neurocomputational approaches to individual differences in `virtuous' decision-making

神经计算方法解决“良性”决策中的个体差异

• 批准号: RGPIN-2019-04329
• 负责人: Tusche, Anita
• 金额: $ 2.4万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764164
• 关键词: Neurocomputational; approaches; individual; differences; virtuous

What computations in the human brain determine whether we fail or succeed to make `virtuous' choices? Eat healthier, save for retirement, or reduce the ecological footprint are a few examples of choices widely considered as `good' due to their long-term benefits for decision-makers and/or society. Yet, empirical data and personal experience alike point to dramatic differences in peoples' tendencies to align choices with virtuous goals, as this often requires forgoing direct, immediate rewards in favor of rewards that are more indirect, abstract or delayed (e.g. `health'). My trainees and I study the neural underpinnings that drive differences in `virtuous' decision-making across people, contexts, and choice domains, using the following framework. Most choices require weighing competing considerations. A popular computational model assumes that this is done by computing a decision value (DV) for each choice option (e.g. pizza or salad for lunch) as the weighted sum of choice-relevant considerations (or attributes) (DV=?iwi(Attributei), and comparing DVs. Yet, not all attributes are weighted equally, leading to biases in choices - often in favor of immediate hedonistic rewards. Adopting this framework, my research program structures three related aims to understand the neural basis of variance in `good' decision-making. Overlapping participant samples across aims permit synergistic cross talk among their key questions, measures, and methodological tools. -AIM 1. Differences across people. Why do some people seem to have an easier time making the `right' choice than others? We explore the role of brain anatomy and connectivity of nodes of the brain's decision network for differences in computations and choices across people. -AIM 2. Malleability. How malleable are attribute-specific computations in the brain? To what degree can contextual and attention manipulations towards `virtuous' attributes alter choices? Analyses of brain data utilizes advanced multivariate pattern analyses (MVPA) that will be a crucial element of HQP training. -AIM 3.  Generalisability and ecological validity. Are people that have difficulties incorporating `virtuous' attributes (linked to abstract, indirect rewards) in one choice domain (e.g. dietary choice) also more likely to struggle with this in other areas of their life (e.g. saving for retirement)? To what degree do model estimates of `virtuous choice' obtained in the lab generalize to behaviors in the real world? My trainees and I will use these findings to build a neurally informed model of human decision-making, a long-term goal of this research program. This work is transformational for our understanding of basic cognitive processes of choices and can inform policy makers. Understanding the underlying computations, distinct neural substrates, and their malleability might ultimately inform targeted, process-specific interventions to effectively increase `virtuous' behaviors in various domains of our daily life.

人类大脑中的哪些计算决定了我们是否会成功或失败地做出“善良”的选择？健康饮食、为退休储蓄或减少生态足迹是被广泛认为是“好的”选择的几个例子，因为它们对决策者和/或社会有长期利益。然而，经验数据和个人经验都指出，人们倾向于将选择与良性目标结合在一起时存在巨大差异，因为这通常需要放弃直接、即时的奖励，而选择更间接、抽象或延迟的奖励。“健康”)。我和我的学员们使用以下框架研究驱动不同人、环境和选择领域的“良性”决策差异的神经基础。大多数选择都需要权衡各种竞争因素。一个流行的计算模型假设这是通过计算每个选择选项（例如午餐吃披萨或沙拉）的决策值（DV）作为与选择相关的考虑因素（或属性）的加权和来完成的(DV=？iwi(Attributei)，比较DVs。然而，并非所有属性的权重都是相等的，这就导致了选择上的偏见——通常倾向于直接的享乐主义回报。采用这个框架，我的研究项目构建了三个相关的目标，以理解“好”决策中方差的神经基础。跨目标的重叠参与者样本允许在他们的关键问题、措施和方法工具之间进行协同串扰。目的1。人与人之间的差异。为什么有些人似乎比其他人更容易做出“正确”的选择？我们探讨了大脑解剖学的作用和大脑决策网络节点的连通性在计算和选择上的差异。目标2。延展性。大脑中特定属性的计算有多大的可塑性？情境和注意力对“美德”属性的操纵能在多大程度上改变选择？脑数据分析利用先进的多变量模式分析（MVPA），这将是HQP训练的关键要素。目标3。概括性和生态有效性。在一个选择领域（如饮食选择）中难以融入“美德”属性（与抽象的、间接的奖励相关）的人，是否也更有可能在生活的其他领域（如为退休储蓄）中挣扎？在实验室中获得的“良性选择”的模型估计在多大程度上可以推广到现实世界中的行为？我和我的学员将利用这些发现建立一个人类决策的神经信息模型，这是这个研究项目的长期目标。这项工作对我们对选择的基本认知过程的理解是革命性的，可以为政策制定者提供信息。了解潜在的计算、不同的神经基质及其可塑性，最终可能会为有针对性的、特定于过程的干预提供信息，从而有效地增加我们日常生活中各个领域的“良性”行为。