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

决策是社会各阶层最重要的核心认知功能之一。在许多现实世界的决策中，选择哪一个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。例如，在一家超市中，如果只库存100种不同的商品，那么在给定预算约束的情况下，选择一捆价值最大化的商品需要检查大约10^30种可能的组合。出于这个原因，人类并不是在所有的决策中都使用理性选择理论。除了必须将所有不同属性的影响联合收割机之外，另一个复杂性在于，一种替代方案通常在一组属性上是优选的，而另一种替代方案在其他属性上是优选的。做出选择需要权衡，这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。 这种复杂性被认为是人类有限的认知资源的负担，因此人类只能注意有限的信息集，然后根据这些信息做出决定。此外，任务历史经常系统地改变决策偏差。这项研究计划利用了从个人的大脑中获得直接记录的机会，而他们执行这种复杂的决定。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哪些属性，以及它们如何加权，可以解释典型和非典型人群中的决策。此外，该综合研究计划还为工程专业的学生提供了一个跨学科背景下接触动力系统和控制理论的机会。（接受癫痫评估的患者）植入了覆盖许多皮层和皮层下脑区的多个深度电极，和计算方法，以发展一个新的理论的神经机制的基础上的多属性决策在复杂的环境中。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参与（1）表示相关决策变量，（2）整合这些变量以形成主观值，以及（3）在多属性决策中选择一个选项的神经回路。植入电极的参与者将在一个新的行为任务中工作，这使得他们在评估报价并选择其中一个时可以观察他们的注意力焦点。 数据将约束多属性决策的尖端计算模型，这些模型将联合收割机：（i）每次试验中决策的程序模型，以及（ii）过去试验历史对决策产生偏见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所识别神经回路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价值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.