CRCNS: Neural decision mechanisms: from value-encoding to preference reversal

CRCNS：神经决策机制：从价值编码到偏好逆转

• 批准号: 9105756
• 负责人: ERNST NIEBUR
• 金额: $ 28.26万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9105756
• 关键词: Accounting; Alcohols; Area; Attention; Back; Baltimore; Behavior; Behavioral; Brain; Choice Behavior; Code; Cognitive; Complex; Computer Simulation; Data; Data Set; Decision Making; Decision Modeling; Doctor of Philosophy; Economics; Electrophysiology (science); Epidemic; Future; Goals; Heart; High School Student; Historically Black Colleges and Universities; Human; Inferior; Investigation; Mediating; Metabolic Diseases; Minority; Modeling; Monkeys; Nicotine; Obesity; Outcome; Pharmaceutical Preparations; Population; Primates; Probability; Process; Property; Psychology; Research; Rewards; Scientist; Societies; Students; Testing; Training; Underrepresented Minority; Universities; Weight; Woman; Work; base; career; cognitive function; college; human subject; interest; neuromechanism; next generation; novel; preference; relating to nervous system; research study; theories

 DESCRIPTION (provided by applicant): Decision-making is one of the most central cognitive functions. Since the early days of the 20th century a body of mathematical work developed the modern axiomatic approach to rationality in choice behavior. These normative models revolutionized economics and mathematical psychology by describing the properties of choices consistent with maximizing an ordered, internal representation of value, termed utility. Experimental research, however, has demonstrated a wide set of "non-rational" behaviors (preference reversals) that deviate from these normative theories. A number of computational models where developed to account for the observed non-rationalities. Most of these models explain behavioral preferences as the outcome of a dynamic computational process and not of a static maximization process with fixed utility and probability weighting functions. However, the cognitive and neural processes that are at the heart of preference formation are still poorly understood. We will combine behavioral data, electrophysiological recordings in humans and monkeys, and computational approaches to develop a new theory of the neural mechanisms underlying complex, multi-attribute decision-making. Intellectual Merit (provided by applicant): The overall goal of the present proposal is to understand the neural code of decision-variables (such as reward amounts and probabilities) and of the dynamic process by which these variables are integrated to form subjective values (utility) and preferences and mediate nonrational behavior. Monkey and human subjects will work in a novel behavioral task that allows us to observe the focus of attention of decision makers while they evaluate the offers and select one of them. Together with these behavioral data we will record decision-related activity in several brain areas. This data set will allow us t test the predictions of various cutting edge computational models that have been suggested to explain preference reversals, but are based on different mechanisms. We will also use the experimental findings to develop a neural mechanistic theory (Aims 1-3, below) and to account for non-rational behaviors, such as preference reversal (Aim 4). Specifically, we have the following aims: (1) Understand how the decision-variables (outcomes, amounts and probabilities) are encoded in the brain. (2) Understand how the separate decision-variables are integrated to compute the overall subjective value of choice options. (3) Investigate whether, and if yes how, attention influences the value computation of choice options. (4) Use the decision model developed in aims 1-3 to explain preference reversals. The end point of these investigations will be a new neurocomputational theory that consistently explains behavioral and neural data in our experiments. This model will integrate decision and attentional selection processes and will generate novel predictions to be tested in future research. Broader Impact (provided by applicant): Some of the most important problems of modern societies are caused by non-optimal decisions made by people. Abuse of illegal drugs, alcohol and nicotine but also the current epidemic of obesity and metabolic disease in the population can ultimately be traced back to people making decisions that are not in their objective best interest. The research proposed here studies how the variables underlying decisions are represented and computed in the primate brain, in particular by understanding situations in which optimal choices are discarded in favour of inferior ones. The project also contributes to the training of the next generation of scientists. Four PhD students will be trained; two at Johns Hopkins University and two at Tel Aviv University, and undergraduates will be part of the research groups. All PIs are strongly committed to increase participation by women and underrepresented minorities. Niebur and Stuphorn have a long track record of training minority high school students in their labs, successfully preparing them for a future college career. In addition, existing connections with Morgan State University, a historically black college in Baltimore, will be extended.

 描述（由应用程序提供）：决策是最中心的认知功能之一。自20世纪初以来，一系列数学工作就发展出了选择行为的理性的现代公理学方法。这些正常模型通过描述选择的特性与最大化有序的，价值的内部表示相一致，从而彻底改变了经济学和数学心理学。但是，实验研究表明，偏离这些正常理论的广泛的“非理性”行为（偏好逆转）。许多计算模型开发了以说明观察到的非理性。这些模型中的大多数将行为偏好解释为动态计算过程的结果，而不是具有固定效用和概率加权功能的静态最大化过程的结果。但是，偏好形成的核心的认知和神经过程仍然很少了解。我们将结合行为数据，人类和猴子中的电生理记录，以及开发一种新理论的计算方法，讲述了复杂的，多重构成决策的神经机制。 智力优点（由适用提供）：本提案的总体目标是了解决策变量的神经守则（例如奖励金额和可能性）以及这些变量被整合以形成主观值（实用性）和偏好和介导非政治行为的动态过程。猴子和人类受试者将在一项新颖的行为任务中发挥作用，使我们能够在评估要约并选择其中一个时观察他们的注意力。与这些行为数据一起，我们将记录几个大脑区域中与决策有关的活动。该数据集将使我们测试已建议解释偏好逆转的各种尖端计算模型的预测，但基于不同的机制。我们还将使用实验发现来开发神经元的机械理论（以下目的1-3），并说明非理性行为，例如偏好逆转（AIM 4）。特别是，我们有以下目的： （1）了解如何在大脑中编码决策变量（结果，数量和可能性）。 （2）了解如何整合单独的决策变量以计算选择选项的总体主观价值。 （3）调查注意力是否以及如何影响选择选项的价值计算。 （4）使用目标1-3中开发的决策模型来解释偏好逆转。 这些研究的终点将是一种新的神经计算理论，该理论一致地解释了我们的实验中的行为和神经逻辑。该模型将整合决策和注意力选择过程，并将在未来的研究中产生要测试的新预测。 更广泛的影响（由适用提供）：现代社会的一些最重要的问题是由人们做出的非最佳决定引起的。滥用非法药物，酒精和尼古丁，但目前在人群中肥胖和代谢疾病的流行最终可以追溯到做出不符合其客观最大利益的人。这里提出的研究研究了如何在灵长类动物的大脑中代表和计算的变量，尤其是通过理解丢弃最佳选择以降低下等方面的情况。该项目还为下一代科学家的培训做出了贡献。四名博士生将接受培训；约翰·霍普金斯大学（Johns Hopkins University）的两个，两个在特拉维夫大学（Tel Aviv University），本科生将成为研究小组的一部分。所有PI都强烈承诺增加妇女和代表性不足的少数民族的参与。 Niebur和Stuphorn在实验室中培训少数族裔高中生的悠久记录，成功地为未来的大学生涯做好了准备。此外，将扩大与巴尔的摩历史上一所历史悠久的黑人学院摩根州立大学的现有联系。