Collaborative Research: CompCog: Adversarial Collaborative Research on Intuitive Physical Reasoning

协作研究：CompCog：直观物理推理的对抗性协作研究

• 批准号: 2121009
• 负责人: Joshua Tenenbaum
• 金额: $ 32.01万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121009&HistoricalAwards=false
• 关键词: Collaborative; Research; CompCog; Adversarial; Intuitive

People are able to reason about the world in amazingly complex ways, yet we consider these capacities part of simple “common sense,” generally shared across individuals and cultures. We toss and catch balls, stack dishes in the sink, and pour a morning cup of coffee with almost no effort. Yet the cognitive systems that support these capabilities are not well understood; even our most advanced attempts to reverse engineer them in robots fall short of human-level efficiency or flexibility. This grant was designed as an “adversarial collaboration” to bring together scientists from two different sides of a critical debate about the nature of human physical reasoning abilities. One theory (championed by the MIT PIs) suggests that this physical reasoning is based on a cognitive system that allows people to simulate what might happen next, similar to how physics engines for video games are used to predict what will happen next in those scenes. While this theory has provided many successful explanations of human behavior, including making precise predictions about how people think Jenga towers will fall, or where they think balls flying through the air will land, another growing body of research (led by the NYU PIs) has demonstrated many instances where the simulation theory cannot adequately describe what people do, but where simpler and approximate “rules-of-thumb” (even inaccurate ones) can. Because human physical reasoning is unlikely to be purely simulation or purely based on simplified rules, a team of experts from both sides of this debate will be crucial for advancing our understanding of the cognitive processes that underlie these reasoning capabilities. Towards reconciling these views, this grant advances the idea that consideration of known human limitations -- e.g., in memory or attention -- can explain the processes that people use when reasoning about the physical world. The goal is to integrate these constraints into a more complete theory of human reasoning that can account for both our failures and our successes in comprehending the physical world. True understanding of these processes will require “reverse engineering” human cognition and perception by designing computational models with similar limitations and capabilities to people. These scientific models may provide insight for researchers in AI and robotics who are interested in designing systems that interact with the world like people, including self-driving cars or the control of prosthetic limbs. Furthermore, exploring how people learn and reason about physics may provide new approaches for physics education. Finally, studying and modeling these facets of physical reasoning will require developing extensible tools, which will be released as open-source software to open up the research into human physical reasoning to a wider set of scientists.This project studies and proposes to resolve tensions between theories of human physical reasoning that suggest that it is based on relatively accurate simulatable mental models, and those that suggest it is based on heuristics and other qualitative forms of reasoning. The research includes experiments related to those that have been used to demonstrate simulation theory, but modified to induce shortcuts in physical reasoning in two broadly different ways. Aim 1 experiments consider scenarios that are expected to run into human resource limitations, either in attention, memory, or time – for instance, asking people to predict the stability of complex towers of blocks with too many pieces to track individually. Aim 2 experiments consider scenarios that could be reasoned about with simulation, but could more easily be reasoned about with simple rules or heuristics – for instance, studying how people use rules like “the heavier side will tip over” when judging which direction a balance beam stacked with objects will fall. Human behavior in these experiments is examined for deviations from pure simulation theory in line with the expected resource limitations (e.g., using rules, focusing on a subset of objects, or representing objects more coarsely), and computational models are developed to explain this behavior. These models are designed around the framework of “resource-rational” cognition, which suggests that people deploy limited cognitive resources in a way that efficiently solves the problems they encounter. The behavioral results and models together allow investigation into (a) whether and when people’s physical reasoning is constrained by resource limitations, and (b) the types of shortcuts people take to circumvent these limitations. Performing this research requires developing an integrated software suite for designing experiments and modeling across a wide variety of physical scenarios. Designing these integrated packages typically requires a large set of technologies -- physics simulators, graphics engines, computational modeling methods -- that are outside the reach of most psychologists, which in turn limits research into human physical reasoning. The PIs are in a unique position to contribute here because their laboratories are focused on computational models of psychology and they have an extensive track record of developing open-source software used by multiple research groups worldwide. The software suite used is designed to be open-sourced and shared with the broader research community to facilitate further research into human physical reasoning without requiring extensive knowledge of the underlying technologies.This work was supported by SBE/BCS Perception, Action, and Cognition, EHR Core Research (ECR), and CISE/IIS Robust Intelligence.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.

人们能够以惊人的复杂方式推论世界，但我们认为这些能力是简单的“常识”的一部分，通常在个人和文化之间共享。我们扔球，在水槽里堆放盘子，几乎没有努力倒一杯咖啡。然而，支持这些能力的认知系统尚不清楚。即使是我们在机器人中进行反向工程师的最先进的尝试都没有人类水平的效率或灵活性。这项赠款被设计为“对抗性合作”，旨在将有关人类物理推理本质的批判性辩论的两个不同方面的科学家汇集在一起​​。一种理论（由麻省理工学院PIS倡导）表明，这种物理推理是基于认知系统，该认知系统允许人们模拟接下来会发生的事情，类似于如何使用电子游戏的物理引擎来预测这些场景中接下来会发生什么。 While this theory has provided many successful explanations of human behavior, including making precise predictions about how people think Jenga towers will fall, or where they think balls flying through the air will land, another growing body of research (led by the NYU PIs) has demonstrated many instances where the simulation theory cannot adequately describe what people do, but where simpler and approximate “rules-of-thumb” (even inaccurate ones) can.人的身体推理不太可能纯粹是模拟的，或者纯粹基于简化的规则，这次辩论双方的专家团队对于促进我们对这些原因能力的认知过程的理解至关重要。为了核对这些观点，这项赠款提出了这样的观念，即考虑已知的人类限制（例如，在记忆或注意力方面）可以解释人们在推理物理世界时使用的过程。目的是将这些约束结合到一个更完整的人类推理理论中，该理论可以解释我们的失败和我们在理解物理世界方面的成功。对这些过程的真正理解将需要通过设计具有相似局限性和能力的人的计算模型来“反向工程”人类的认知和感知。这些科学模型可能会为AI和机器人技术的研究人员提供洞察力，这些研究人员有兴趣设计与世界（包括自动驾驶汽车）或假肢控制的人相互作用的系统。此外，探索人们如何学习和理性的物理学可能为物理教育提供新的方法。最后，研究和建模物理推理的这些方面将需要开发可扩展的工具，将其作为开源软件发布，以向更广泛的科学家开放对人类物理推理的研究。本项目的研究和提议解决人类物理理论之间的紧张关系，这些理论是基于相对准确的可模拟的精神模型的理论，这些理论是基于相对准确的推理，以及这些质量的质量和其他质量的质量。该研究包括与用于证明仿真理论的实验，但经过修改，以通过两种宽阔的方式以物理原因诱导快捷方式。 AIM 1实验考虑了预期在注意力，记忆或时间上会遇到人力资源限制的方案 - 例如，要求人们预测具有太多零件的块的复杂塔的稳定性，无法单独跟踪。 AIM 2实验考虑了可以通过模拟来理解的方案，但可以通过简单的规则或启发式方法更容易地进行理解 - 例如，研究人们如何在判断“较重的一侧会倾斜”之类的规则时，在判断堆叠在物体的平衡光束方向的哪个方向时会下降。根据预期的资源限制（例如，使用规则，专注于对象的子集或更粗糙的对象），对这些实验中的人类行为进行了偏离纯模拟理论的偏离，并开发了计算模型来解释这种行为。这些模型是围绕“资源理性”认知框架设计的，该框架表明人们以有效解决他们遇到的问题的方式部署有限的认知资源。行为结果和模型一起允许对（a）人们的身体推理是否受资源限制的限制，以及（b）人们所采取的捷径类型来规避这些限制。进行这项研究需要开发一个集成的软件套件，用于设计实验并在各种物理场景中进行建模。设计这些集成的软件包通常需要大量技术 - 物理模拟器，图形引擎，计算建模方法 - 超出了大多数心理学家的范围，这反过来又将研究限制在人类的身体推理中。 PI处于在这里贡献的独特位置，因为他们的实验室专注于心理学的计算模型，并且在全球多个研究小组中使用的开源软件有广泛的记录。所使用的软件套件旨在与更广泛的研究社区进行开源并与更广泛的研究社区共享，以促进对人类身体原因的进一步研究，而无需对基础技术的广泛了解。这项工作得到了SBE/BCS的感知，行动和行动和认知，认知，EHR核心研究（ECR）的认知和认知的支持，并通过CISE/IIS IS INDICENTION批准了STAT的支持。基金会的智力优点和更广泛的影响评论标准。

项目成果

"Just in Time" representations for mental simulation in intuitive physics

直觉物理学中心理模拟的“及时”表示

• 发表时间: 2023
• 期刊: Proceedings of the annual meeting of the Cognitive Science Society
• 作者: Chen, Tony;Allen, Kelsey R;Cheyette, Samuel J;Tenenbaum, Joshua T;Smith, Kevin A
• 通讯作者: Smith, Kevin A

Partial mental simulation explains fallacies in physical reasoning

• DOI: 10.1080/02643294.2022.2083950
• 发表时间: 2022-06-04
• 期刊: COGNITIVE NEUROPSYCHOLOGY
• 影响因子: 3.4
• 作者: Bass, Ilona;Smith, Kevin A.;Ullman, Tomer D.
• 通讯作者: Ullman, Tomer D.

An approximate representation of objects underlies physical reasoning.

物体的近似表示是物理推理的基础。

• DOI: 10.1037/xge0001439
• 发表时间: 2023
• 期刊: Journal of Experimental Psychology: General
• 作者: Li, Yichen;Wang, YingQiao;Boger, Tal;Smith, Kevin A.;Gershman, Samuel J.;Ullman, Tomer D.
• 通讯作者: Ullman, Tomer D.