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

用于决策的广义选择响应时间模型的高级贝叶斯方法

• 批准号: 2242962
• 负责人: William Holmes
• 金额: $ 33万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242962&HistoricalAwards=false
• 关键词: Advanced; Bayesian; Methods; Generalized; Choice

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.

该研究项目将开发新的工具来研究人类的判断和决策。研究决策的一个重大挑战是测试假设或理论是否得到数据的支持。在许多研究中，我们只看到一个感兴趣的决定的结果，这使得我们很难理解导致这个决定的过程，也很难直接测试关于这个过程的理论。计算模型已被证明是有价值的工具，可以用来编码理论并根据观察结果对其进行测试。然而，计算模型存在技术障碍，限制了其可用性，并限制了可以解决的问题的范围。该项目将提供一套有效但普遍可用的计算方法和工具，促进模型的构建和分析。将开发的新方法将扩大可能的调查范围和能够开展调查的研究人员。为确保这些工具尽可能广泛地使用，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，多属性选择的属性，并评估环境如何影响人们在自然环境中的选择。本项目将对学生进行培训，使他们掌握在科学和工业中越来越普遍的最先进的计算方法。本项目将开发先进的贝叶斯方法，用于对选择-响应时间（RT）模型进行参数估计。人们做出决策所花费的时间（RT）提供了有关负责这些决策的动态过程的有价值的信息。出于这个原因，预测选择和RT的模型被用来研究决策过程。然而，将这些类型的模型拟合到数据中是具有挑战性的，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结合简单的实验设计，以保持易处理性。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，记录是有效的，在执行贝叶斯参数估计的各种各样的选择RT模型。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，这两者结合起来可以促进新的科学研究。作为一个例子，研究人员将研究上下文在决策中的作用，涉及编码在例如语义或基于图像的刺激中的自然信息。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性的不同假设的选择RT模型相集成。这些模型适用于复杂的数据集，这些数据集来自大规模的实验设计，涉及大量的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的选择是否显示出情境依赖性的争论，这是观察到更多的人工stimulation.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

PyBEAM: A Bayesian approach to parameter inference for a wide class of binary evidence accumulation models

• DOI: 10.3758/s13428-023-02162-w
• 发表时间: 2023-08-07
• 期刊: BEHAVIOR RESEARCH METHODS
• 影响因子: 5.4
• 作者: Murrow，Matthew;Holmes，William R.
• 通讯作者: Holmes，William R.