Advanced Bayesian Methods for Generalized Choice Response Time Models of Decision-Making

这个研究项目将开发新的工具来研究人类的判断和决策。研究决策过程中的一个重大挑战是检验数据对假设或理论的支持程度。在许多研究中，我们只看到感兴趣的决定的结果，这使得我们很难理解导致该决定的过程，也很难直接测试关于该过程的理论。计算模型已被证明是有价值的工具，用于编码理论并根据观测对其进行测试。然而，计算模型存在技术障碍，限制了它们的可用性并限制了可以解决的问题的范围。该项目将提供一套高效但普遍可用的计算方法和工具，以促进模型的构建和分析。即将开发的新方法将扩大可能的调查范围和能够进行调查的研究人员的范围。为确保其尽可能广泛的可用性，这些工具将以免费提供的软件包分发。研究人员将使用这些工具来研究多选项、多属性选择的性质，并评估在自然主义环境下背景如何影响人们的选择。这个项目支持的学生将接受最先进的计算方法的培训，这些方法在科学和工业中变得越来越常见。这个项目将开发先进的贝叶斯方法来执行选择-响应时间(RT)模型的参数估计。人们做出决策所需的时间(RTS)提供了有关负责这些决策的动态过程的宝贵信息。出于这个原因，预测选择和RTS的模型被用来研究决策过程。然而，将这些类型的模型与数据相匹配是具有挑战性的，这是评估它们编码的理论质量的必要步骤。因此，基于模型的方法通常使用几十年前的模型与简单的实验设计相结合，两者都是为了保持易操纵性。为了解决这些问题，该项目将开发一套可访问的、高质量的概率方法，这些方法被证明在执行各种选择-RT模型的贝叶斯参数估计方面是有效的。研究人员将能够构建更复杂的选择-RT模型，并利用更复杂的实验设计，两者结合起来可以促进新的科学研究。作为一个例子，研究人员将研究语境在涉及自然信息的决策中的作用，这些信息编码在例如语义或基于图像的刺激中。语言或图像表示的机器学习模型将与编码关于上下文依赖的不同假设的选择-RT模型相结合。这些模型适用于来自大规模实验设计的复杂数据集，这些实验设计涉及大量参与者做出自然主义的决定。这项研究的结果将有助于解决关于自然主义选择是否表现出背景依赖性的争论，这是在更多的人工刺激下观察到的。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop new tools to study human judgment and decision making. A significant challenge in studying decision making is testing how well a hypothesis or theory is supported by data. In many studies, we only see the outcome of a decision of interest, which makes it difficult to understand the process that led to that decision and to directly test theories about that process. Computational models have proven valuable as tools to encode theories and test them against observations. However, computational models present technical barriers that limit their usability and constrain the scope of questions that can be addressed. This project will provide a suite of efficient yet generally usable computational approaches and tools that facilitate model construction and analysis. The new methods to be developed will broaden the scope of investigations that are possible and the researchers who can carry them out. To ensure their broadest possible usability, the tools will be disseminated in freely available software packages. The investigators will use these tools to study the properties of multi-alternative, multi-attribute choice and assess how context influences people's choices in naturalistic settings. Students supported by this project will be trained in state-of-the-art computational methods which are becoming increasingly more common in science and industry.This project will develop advanced Bayesian methodologies for performing parameter estimation for choice-response time (RT) models. The time it takes for people to make decisions (RTs) provides valuable information about the dynamic process responsible for those decisions. For this reason, models that predict both choices and RTs are used to study decision processes. However, it is challenging to fit these types of models to data, which is a necessary step in assessing the quality of the theories they encode. As a result, model-based approaches are most often applied using decades old models in conjunction with simple experimental designs, both to maintain tractability. To address these issues, this project will develop a set of accessible, high-quality probabilistic methods that are documented to be effective at performing Bayesian parameter estimation for a wide variety of choice-RT models. Researchers will be able to construct more complex choice-RT models and utilize more complex experimental designs, which in combination can facilitate new scientific investigations. As an example of this, the investigators will study the role of context in decisions involving naturalistic information encoded in, for example, semantic or image-based stimuli. Machine-learning models of language or image representations will be integrated with choice-RT models encoding different assumptions about contextual dependencies. These models are fit to complex data sets derived from large-scale experimental designs involving large numbers of participants making naturalistic decisions. The results of this study will help resolve the debate about whether naturalistic choices show context-dependency, which is observed with more artificial stimuli.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.