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.

认知建模领域致力于使用数学、统计学和计算机语言来理解人类的思想和行为。认知模型是一组方程或计算机程序，可以模仿人们在实验或现实环境中的行为。这些模型在很多方面都很有用。他们可以预测人们或群体在新情况下将如何行动。他们可以指导教育材料和培训系统的开发，以最大限度地提高学习效果。它们可以深入了解大脑的内部运作，这有助于治疗心理和大脑疾病。它们可以帮助解释人类的智能和创造力，从而带来人工智能和机器学习的新方法。该项目旨在通过数学进步和人类行为实验进一步实现这些目标，这可能会产生新的、更准确的模型。然后，各种跨学科合作和推广工作将探索这些模型的应用，以改善精神病学诊断，开发新的神经影像数据分析方法，使人工智能更容易被用户理解（可解释的人工智能），并使本科生和高中生更容易理解心理模型、统计学和人工智能。该项目的技术部分研究两种类型的认知模型之间的联系：算法和认知模型。 合理的。算法模型从信息处理的角度描述思维，指定心理表征以及从感知输入到观察行为的过程。理性模型根据一个人的目标和对世界如何运作的信念来解释他或她的学习和决策。他们假设大脑对其环境高度适应，因此相对于世界固有的不确定性，它的行为处于最佳状态。研究人员通常将算法模型视为近似理性模型的启发法（即简化的捷径）。在这种对算法模型的解释下，由于大脑的物理限制，比如它能记住多少或一次能处理多少信息，认知能力达不到最佳状态。该项目将开发一种不同的连接。使用形式数学分析，研究人员将展示在世界极其不确定和不可预测的假设下，心理学中有影响力的算法模型如何与某些理性模型完全匹配。这种连接将以多种方式用于开发新模型：现有算法模型的更复杂的理性版本、现有理性模型的更高效算法版本以及结合理性模型和算法模型优势的中间模型。四个系列的实验，每个涵盖决策、奖励学习和概念获取的任务，将测试哪些模型最能预测人类行为，以及哪些模型在自然环境中产生最佳的客观表现。如果成功，该项目将为认知建模领域提供新的数学基础、更准确地匹配人类行为的具体模型、人工智能和统计的新工具，以及人类思维理性基本问题的新视角。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

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.