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

在几乎所有的社会层面上，决策都是最核心、最重要的认知功能之一。在许多现实世界的决策中，选择哪种可用替代方案会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。例如，在一个只储存100种不同商品的超市中，在预算限制的情况下，选择能最大化价值的捆绑商品需要检查大约10^30种可能的组合。出于这个原因，人类并不是在所有的决定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，一种选择通常在一组属性上更可取，但另一种选择在其他属性上更可取。然后，做出选择需要权衡，这会使决策过程更加复杂。然而，作为偏好形成核心的认知和神经过程仍然知之甚少。这种复杂性被认为对人类有限的认知资源造成了负担，因此人类只能关注有限的一组信息，而这些信息是做出决定的基础。此外，任务历史通常会系统地改变决策偏差。这项研究计划利用这个机会，在个人做出如此复杂的决定时，从他们的大脑中获得直接的录音。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。了解在决策过程中考虑了哪些属性，以及它们是如何加权的，可以解释典型人群和非典型人群的决策。此外，这一综合研究计划提供了一个机会，让工科学生接触到跨学科背景下的动力系统和控制理论。该项目结合了行为数据、人类(接受癫痫评估的患者)的神经记录、覆盖许多皮质和皮质下大脑区域的多个深度电极植入，以及计算方法，以开发复杂环境下多属性决策背后的神经机制的新理论。这是一个独特的机会，当人类做出这些决定时，可以同时研究多个大脑区域的大脑回路。本建议的总体目标是了解涉及(1)表示相关决策变量，(2)集成这些变量以形成主观值，以及(3)在多属性决策中选择选项之一所涉及的神经回路。参与者将在植入电极的情况下进行一项新的行为任务，这使得他们在评估报价并选择其中一种报价的同时，可以观察他们的注意力焦点。数据将限制多属性决策的尖端计算模型，这些模型将结合：(I)每个试验中的决策的程序模型，以及(Ii)过去的试验历史对决策产生偏差影响的潜在变量模型。计算模型将使识别代表任务相关变量的神经元活动以及在识别的神经电路的不同元素之间的动态信息流成为可能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.