NCS-FO: Collaborative Research - Human decision-making in complex environments

NCS-FO：协作研究 - 复杂环境中的人类决策

• 批准号: 1835323
• 负责人: Jorge Gonzalez-Martinez
• 金额: $ 36.42万
• 依托单位: Cleveland Clinic Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835323&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Human

Decision-making is one of the most central cognitive functions of importance at practically all levels of society. In many real-world decisions, which of the available alternatives is chosen is influenced by many different attributes. Such multi-attribute decisions are complex because they require the integration and comparison of many pieces of information. For instance, selecting the bundle of goods that maximizes value given a budget constraint in a supermarket that only stocks 100 different goods requires checking approximately 10^30 possible combinations. For this reason, humans do not use rational choice theory in all their decisions. In addition to having to combine the influence of all the different attributes, another complexity is that one alternative is often preferable on one set of attributes, but another is preferred on others. Making a choice then requires a trade-off, which further complicates the decision process. However, the cognitive and neural processes that are at the heart of preference formation are still poorly understood. This complexity is thought to tax limited cognitive resources in humans who therefore can pay attention only to a limited set of information, on which the decision is then based. In addition, task history often systematically changes decision biases. This research program takes advantage of the opportunity to obtain direct recordings from individual's brains while they perform such complex decision. It will study these activity patterns to determine whether they can be explained via mathematical models of decision making. Understanding which attributes are considered during decision making, and how they are weighted could explain decision making in typical and a-typical populations. Furthermore this integrative research program forms an opportunity to expose engineering students to dynamical systems and control theories in an interdisciplinary context.This project combines behavioral data, neural recordings in humans (patients undergoing epilepsy evaluation) implanted with multiple depth electrodes covering many cortical and subcortical brain areas, and computational approaches to develop a new theory of the neural mechanisms underlying multi-attribute decision-making in complex environments. This is a unique opportunity to study brain circuits simultaneously across multiple brain areas while humans make these decisions. The overall goal of the present proposal is to understand the neural circuit involved in (1) representing the relevant decision variables, (2) integrating these variables to form subjective values, and (3) selecting one of the options in multi-attribute decisions. Participants, with implanted electrodes, will work in a novel behavioral task that makes it possible to observe their focus of attention while they evaluate the offers and select one of them. Data will constrain cutting edge computational models of multi-attribute decision making that will combine: (i) a procedural model of the decision in each trial, and (ii) a latent variable model of biasing influence on decision-making resulting from past trial history. The computational models will make it possible to identify neuronal activity that represents task-relevant variables and the dynamic flow of information across the different elements of the identified neural circuit.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.

决策是社会各阶层中最重要的核心认知功能之一。在许多现实世界的决策中，选择可用的替代方案受到许多不同属性的影响。这种多属性决策是复杂的，因为它们需要集成和比较许多信息片段。例如，在给定预算限制的情况下，在一个只储存100种不同商品的超市中选择价值最大化的商品束，需要检查大约10^30种可能的组合。由于这个原因，人类并不是在所有的决策中都使用理性选择理论。除了必须结合所有不同属性的影响之外，另一个复杂性是，一种选择通常对一组属性更可取，而另一种选择对其他属性更可取。做出选择需要权衡取舍，这进一步使决策过程复杂化。然而，作为偏好形成核心的认知和神经过程仍然知之甚少。这种复杂性被认为消耗了人类有限的认知资源，因此人类只能关注有限的一组信息，然后根据这些信息做出决定。此外，任务历史常常系统性地改变决策偏差。这个研究项目利用了从个人大脑中获取直接记录的机会，当他们做出如此复杂的决定时。它将研究这些活动模式，以确定它们是否可以通过决策的数学模型来解释。了解哪些属性在决策过程中被考虑，以及它们是如何加权的，可以解释典型和非典型群体的决策。此外，这个综合研究项目提供了一个机会，让工程专业的学生在跨学科的背景下接触动力系统和控制理论。本项目结合行为数据、植入覆盖多个皮层和皮层下脑区的多个深度电极的人类（接受癫痫评估的患者）神经记录，以及计算方法，开发复杂环境下多属性决策的神经机制新理论。这是一个独特的机会，可以在人类做出这些决定时同时研究多个大脑区域的大脑回路。本提案的总体目标是了解(1)表示相关决策变量，(2)整合这些变量形成主观值，以及(3)在多属性决策中选择一个选项所涉及的神经回路。被植入电极的参与者将在一个新的行为任务中工作，当他们评估并从中选择一个时，可以观察他们的注意力焦点。数据将限制多属性决策的前沿计算模型，这些模型将结合：(i)每次试验中决策的程序模型，以及（ii）由过去试验历史产生的对决策的偏倚影响的潜在变量模型。计算模型将使识别神经元活动成为可能，这些神经元活动代表任务相关变量，以及在已识别的神经回路的不同元素之间的动态信息流。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。