Mechanisms of multi-attribute decision-making

多属性决策机制

• 批准号: 10774849
• 负责人: Erin L Rich
• 金额: $ 74.18万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10774849
• 关键词: Address; Anxiety; Arbitration; Area; Attention; Behavior; Behavioral Paradigm; Biological; Brain; Choice Behavior; Code; Color; Complex; Computer Analysis; Computer Models; Decision Making; Diffusion; Dimensions; Disease; Goals; Human Characteristics; Individual; Link; Macaca mulatta; Maps; Measures; Mediating; Mental Depression; Mental disorders; Modeling; Monkeys; Neurons; Neurosciences; Outcome; Population; Population Analysis; Population Dynamics; Probability; Process; Psychopathology; Reporting; Rewards; Risk; Signal Transduction; Structure; Suggestion; Testing; Visual; Visual attention; Weight; attentional bias; attentional modulation; cost; falls; frontal eye fields; gaze; information model; information processing; insight; multimodality; neural; neural circuit; neuroeconomics; neuromechanism; nonhuman primate; novel; predictive modeling; response; temporal measurement; theories

When making a complex decision, we often consider multiple dimensions, such as costs and qualities, that vary among choice options. Evaluating important attributes of a given option is critical for optimal choice behavior, and poor decision-making can result from an inability to properly weigh attributes, as is commonly observed in psychiatric disorders. These deficits are accompanied by alterations in the structure and function of the orbitofrontal cortex (OFC), an area critical for value-based decision-making. However, the underlying neural mechanisms and how they are disrupted remain unclear, and this limits our ability to map decision-making deficits to neural computation. The long-term goal of this proposal is to understand how the brain uses information to make optimal decisions, and our specific objective is to develop a comprehensive model of information processing in OFC during multi-attribute choices. To do this, we will use a multi-modal approach to evaluate different frameworks of decision formation. A neuroeconomics view posits that the values of different attributes are combined to compute an overall, or integrated value, and comparisons are made in the space of these option values. In contrast, other evidence suggests that direct competition between attributes, perhaps mediated by visual attention, is an important part of the decision process. Arbitrating between these models is critical to advancing theoretical frameworks that can link decision-making deficits to disordered neural computations, but a key challenge is that the steps of decision formation occur rapidly and internally, making them difficult to observe or otherwise measure. Here, we address this by combining a novel multi-attribute choice task with large-scale neural recording and population analyses necessary to reveal within-trial dynamics of otherwise covert decision-making processes. In Aim 1, we will assess how OFC codes individual attributes during multi-attribute decisions, and how this relates to classically reported integrated value signals. Next, we will assess how attention to attributes alters OFC coding, value computation, and subsequent decisions (Aim 2). Finally, in Aim 3, we propose a novel computational model of multi-attribute decisions that can determine the extent to which choices are driven by the relative values of attributes versus integrated options. Our model will also reveal latent variables that evolve during decision formation, which we will map on to neural responses. In doing so, we aim to localize specific choice processes to unique neural circuits, and also demonstrate the biological relevance of the model and its conclusions. Together, these studies leverage our combined expertise in non-human primate behavior, computational analysis, and modeling to define the neural underpinnings of multi-attribute choice in OFC. If successful, our results will not only refine the theoretical frameworks that guide decision neuroscience, but will also shed light on neural processes that underlie decision-making deficits characteristic of human psychiatric disorders.

在做一个复杂的决策时，我们通常会考虑多个维度，比如成本和质量，这些维度各不相同 在众多选择中。评估给定选项的重要属性对于最优选择行为至关重要， 而糟糕的决策可能是由于无法正确权衡属性造成的，正如在 精神疾病这些缺陷伴随着结构和功能的改变， 眶额皮质（OFC），一个对基于价值的决策至关重要的区域。然而，潜在的神经 机制以及它们是如何被破坏的仍然不清楚，这限制了我们绘制决策图的能力 神经计算的缺陷这项提案的长期目标是了解大脑如何使用 信息做出最佳决策，我们的具体目标是开发一个全面的模型， 多属性选择过程中OFC的信息处理。为此，我们将使用多模式方法， 评估不同的决策形成框架。神经经济学的观点认为， 属性被组合以计算总体或综合值，并在以下空间中进行比较： 这些选项值。相反，其他证据表明，属性之间的直接竞争，也许 由视觉注意力介导，是决策过程的重要组成部分。在这些模型之间进行仲裁是 对于推进可以将决策缺陷与神经紊乱联系起来的理论框架至关重要。 计算，但一个关键的挑战是，决策形成的步骤发生迅速和内部， 它们难以观察或测量。在这里，我们通过结合一种新颖的多属性选择来解决这个问题。 任务与大规模的神经记录和人口分析，必要的，以揭示试验内动态 否则就是隐蔽的决策过程。在目标1中，我们将评估OFC如何对单个属性进行编码， 多属性决策，以及这与传统报告的综合价值信号的关系。接下来我们就 评估对属性的关注如何改变OFC编码、价值计算和后续决策（目标2）。 最后，在目标3中，我们提出了一种新的多属性决策计算模型，可以确定 选择在多大程度上是由属性相对于综合选项的相对价值驱动的。我们的模型将 还揭示了在决策形成过程中演变的潜在变量，我们将映射到神经反应。在 这样做，我们的目标是将特定的选择过程定位到独特的神经回路中，并证明 模型的生物相关性及其结论。总之，这些研究利用了我们的综合专业知识 在非人类灵长类动物的行为，计算分析和建模，以确定神经基础的 OFC中的多属性选择如果成功，我们的结果不仅将完善指导的理论框架 决策神经科学，但也将揭示神经过程的基础决策缺陷 人类精神疾病的特征