CompCog: Bridging Levels of Analysis: Characterizing Algorithmic Models by Extreme Bayesian Priors

CompCog：桥接分析级别：通过极端贝叶斯先验表征算法模型

• 批准号: 2020906
• 负责人: Matthew Jones
• 金额: $ 49.15万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020906&HistoricalAwards=false
• 关键词: CompCog; Bridging; Levels; Analysis; Characterizing

The field of cognitive modeling seeks to understand human thought and behavior using the languages of mathematics, statistics, and computers. A cognitive model is a set of equations or a computer program that can mimic how people act in experimental or real-world settings. These models are useful in many ways. They can predict how people or groups will act in new situations. They can guide development of educational materials and training systems that maximize learning. They can give insight into the inner workings of the mind, which can contribute to treatment of psychological and brain disorders. They can help to explain human intelligence and creativity, leading to new methods in artificial intelligence and machine learning. This project aims to further these goals through mathematical advances and human behavioral experiments that together may lead to new, more accurate models. A variety of interdisciplinary collaborations and outreach efforts will then explore application of these models to improving psychiatric diagnosis, developing new analysis methods for neuroimaging data, making artificial intelligence more comprehensible to the user (explainable AI), and making psychological models, statistics, and AI more accessible to undergraduate and high school students.The technical portion of this project investigates connections between two types of cognitive models: algorithmic and rational. Algorithmic models describe the mind in terms of information processing, specifying mental representations and the processes that act on them in going from perceptual input to observed behavior. Rational models explain a person’s learning and decision making in terms of his or her goals and beliefs about the how the world works. They assume the mind is highly tuned to its environment, and thus that it acts optimally relative to the inherent uncertainty in the world. Researchers usually think of algorithmic models as heuristics (i.e., simplified shortcuts) that approximate rational ones. Under this interpretation of algorithmic models, cognition falls short of being optimal because of physical limitations of the brain, such as how much it can remember or how much information it can process at once. This project will develop a different connection. Using formal mathematical analysis, the investigators will show how influential algorithmic models in psychology exactly match certain rational models under the assumption that the world is extremely uncertain and unpredictable. This connection will be used in several ways to develop new models: more sophisticated rational versions of existing algorithmic ones, more efficient algorithmic versions of existing rational ones, and intermediate models that combine the strengths of rational and algorithmic ones. Four series of experiments, each spanning tasks of decision making, reward learning, and concept acquisition, will test which models best predict human behavior, and also which yield the best objective performance in natural settings. If successful, the project will yield new mathematical foundations for the field of cognitive modeling, specific models that more accurately match human behavior, new tools for AI and statistics, and a new perspective on foundational questions of rationality of the human mind.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.

认知建模领域试图使用数学，统计和计算机的语言理解人类的思想和行为。认知模型是一组方程式或计算机程序，可以模仿人们在实验或现实世界中的作用。这些模型在许多方面都有用。他们可以预测人们或团体将如何在新情况下采取行动。他们可以指导开发最大化学习的教育材料和培训系统。他们可以深入了解心灵的内在运作，这可以有助于对心理和脑部疾病的治疗。他们可以帮助解释人类的智力和创造力，从而导致人工智能和机器学习的新方法。该项目的目的是通过数学进步和人类行为实验进一步实现这些目标，这些实验可能会导致新的，更准确的模型。然后，各种跨学科的合作和宣传工作将探索这些模型以改善精神病诊断的应用，开发新的分析方法，以使神经影像数据对用户（可解释的AI）更加全面，并使心理模型，统计模型和AI更加易于研究，并且对这种模型和高中生之间的访问。 合理的。算法模型描述了信息处理，指定心理表示形式以及从感知输入到观察行为时对它们作用的过程。理性的模型从他或她的目标方面解释了一个人的学习和决策，并相信世界有效。他们认为思想是对环境的高度调整，因此它与世界上的不确定性相对最佳。研究人员通常将算法模型视为启发式方法（即简化的快捷方式），它们近似于理性。在对算法模型的这种解释下，由于大脑的物理局限性，认知没有最佳，例如它可以记住多少或一次可以处理多少信息。该项目将建立不同的连接。使用正式的数学分析，研究人员将在心理学中的影响算法模型如何与某些理性模型完全匹配，这是因为世界极为不确定和不可预测。该连接将以几种方式用于开发新模型：现有算法的更复杂的理性版本，现有理性的算法版本的更有效的算法版本以及结合了理性和算法的优势的中间模型。四个系列实验，每个跨越决策，奖励学习和概念的任务，将测试哪些模型可以预测人类行为，以及哪些在自然环境中产生最佳客观表现。 If successful, the project will yield new mathematical foundations for the field of cognitive modeling, specific models that more accurately match human behavior, new tools for AI and statistics, and a new perspective on foundational questions of rationality of the human mind.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.

项目成果

A belief systems analysis of fraud beliefs following the 2020 US election

2020 年美国大选后欺诈信念的信念系统分析

• DOI: 10.1038/s41562-023-01570-4
• 发表时间: 2023
• 期刊: Nature Human Behaviour
• 影响因子: 29.9
• 作者: Botvinik-Nezer, Rotem;Jones, Matt;Wager, Tor D.
• 通讯作者: Wager, Tor D.