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

 描述(申请人提供)：决策是最核心的认知功能之一。自20世纪初以来，一系列数学著作发展了现代理性选择行为的公理方法。这些规范的模型通过描述与最大化有序的、内部的价值表示(称为效用)相一致的选择的属性，彻底改变了经济学和数学心理学。然而，实验研究证明了一系列偏离这些规范理论的“非理性”行为(偏好反转)。开发了一些计算模型来解释观察到的非理性。大多数模型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。我们将结合行为数据、人类和猴子的电生理记录，以及计算方法来开发复杂、多属性决策背后的神经机制的新理论。 智力价值(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被整合以形成主观价值(效用)和偏好并调节非理性行为的动态过程。猴子和人类受试者将在一项新颖的行为任务中工作，这项任务允许我们在决策者评估报价并选择其中一项时观察他们关注的焦点。与这些行为数据一起，我们将记录几个大脑区域的决策相关活动。这个数据集将允许我们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。我们还将利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，如偏好反转(目标4)。具体来说，我们有以下目标： (1)了解决策变量(结果、数量和概率)是如何在大脑中编码的。 (2)了解如何将独立的决策变量整合在一起来计算选择方案的总体主观价值。 (3)考察注意是否影响选择期权的价值计算，如果是，又如何影响选择期权的价值计算。 (4)使用AIMS 1-3中发展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，它可以一致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，以便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定造成的。滥用非法药物、酒精和尼古丁，以及目前肥胖和代谢性疾病在人口中的流行，最终都可以追溯到人们做出了不符合他们客观最佳利益的决定。这里提出的研究是研究决定背后的变量如何在灵长类动物的大脑中表示和计算，特别是通过理解最佳选择被抛弃而支持次要选择的情况。该项目还有助于培训下一代科学家。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，本科生将成为研究小组的一部分。所有私营部门都坚定地致力于增加妇女和代表人数不足的少数群体的参与。Niebur和Stuphorn在实验室培训少数民族高中生方面有着长期的记录，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将得到扩大，摩根州立大学是巴尔的摩的一所历史悠久的黑人学院。

 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.