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: 9272869
• 负责人: ERNST NIEBUR
• 金额: $ 27.91万
• 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/9272869
• 关键词: 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; Mathematics; Mediating; Metabolic Diseases; Minority; Modeling; Modernization; 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; experimental study; human subject; interest; neuromechanism; next generation; novel; preference; relating to nervous system; theories; undergraduate student

 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（由申请人提供）：决策是最核心的认知功能之一。自世纪早期以来，大量的数学著作发展了现代公理化方法来研究选择行为的合理性。这些规范模型通过描述选择的属性与最大化有序的内在价值表示（称为效用）相一致，从而彻底改变了经济学和数学心理学。然而，实验研究已经证明了一系列偏离这些规范理论的“非理性”行为（偏好逆转）。一些计算模型的发展，以占观察到的非理性。这些模型大多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函数的静态最大化过程的结果。然而，作为偏好形成核心的认知和神经过程仍然知之甚少。我们将结合联合收割机的行为数据，在人类和猴子的电生理记录，和计算方法，以开发一个新的理论的神经机制的基础上复杂的，多属性的决策。 智力优点（由申请人提供）：本提案的总体目标是了解决策变量（如奖励金额和概率）的神经代码，以及这些变量整合形成主观价值（效用）和偏好并调解非理性行为的动态过程。猴子和人类受试者将在一个新的行为任务中工作，该任务允许我们观察决策者在评估报价并选择其中一个时的注意力焦点。与这些行为数据一起，我们将记录几个大脑区域中与决策相关的活动。这个数据集将使我们能够测试各种尖端计算模型的预测，这些模型被建议用来解释偏好逆转，但基于不同的机制。我们还将使用实验结果来发展神经机制理论（目标1-3，下文），并解释非理性行为，如偏好逆转（目标4）。具体而言，我们有以下目标： (1)了解决策变量（结果，数量和概率）如何在大脑中编码。 (2)理解如何整合独立的决策变量来计算选择选项的总体主观价值。 (3)调查注意力是否会影响选择选项的价值计算，如果是，又是如何影响的。 (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个新的神经计算理论，它能始终如一地解释我们实验中的行为和神经数据。该模型将整合决策和注意力选择过程，并将产生新的预测在未来的研究中进行测试。 更广泛的影响（由申请人提供）：现代社会的一些最重要的问题是由人们做出的非最佳决策造成的。非法药物、酒精和尼古丁的滥用以及目前人口中肥胖和代谢疾病的流行，最终可以追溯到人们做出不符合其客观最佳利益的决定。这里提出的研究研究了决定背后的变量是如何在灵长类动物大脑中表示和计算的，特别是通过理解最佳选择被丢弃而有利于劣质选择的情况。该项目还有助于培养下一代科学家。四名博士生将接受培训;两名在约翰霍普金斯大学，两名在特拉维夫大学，本科生将参加研究小组。所有参与机构都坚定地致力于增加妇女和代表性不足的少数群体的参与。尼布尔和斯图霍恩在实验室培训少数民族高中生方面有着悠久的历史，成功地为他们未来的大学生涯做好了准备。此外，与摩根州立大学的现有联系，这是一所历史上位于巴尔的摩的黑人学院，将得到延长。