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.

人们能够以极其复杂的方式推理世界，但我们认为这些能力是简单的“常识”的一部分，这些能力通常在不同的个人和文化中共享。然而，支持这些功能的认知系统还没有被很好地理解；即使是我们对机器人进行逆向工程的最先进的尝试也达不到人类水平的效率或灵活性。 ”将两个不同方面的科学家聚集在一起关于人类物理推理能力本质的批判性争论的一个理论（由麻省理工学院的 PI 倡导）表明，这种物理推理基于一种认知系统，该系统允许人们模拟接下来可能发生的事情，类似于视频的物理引擎。虽然这个理论已经为人类行为提供了许多成功的解释，包括精确预测人们认为叠叠乐塔会如何倒下，或者他们认为空中飞过的球会落在哪里，另一个不断增长的研究机构（由纽约大学的 PI）有许多已证明的实例，其中模拟理论无法充分描述人们的行为，但更简单和近似的“经验法则”（甚至是不准确的）可以，因为人类物理推理不太可能纯粹是模拟或纯粹基于。在简化规则方面，来自这场辩论双方的专家团队对于增进我们对这些推理能力背后的认知过程的理解至关重要，为了协调这些观点，这项资助提出了考虑已知的人类局限性的想法——例如。 ，在记忆或注意力中- 可以解释人们在推理物理世界时使用的过程，目标是将这些约束整合到一个更完整的人类推理理论中，该理论可以解释我们在理解物理世界方面的失败和成功。这些过程将需要通过设计具有与人类相似的限制和能力的计算模型来“逆向工程”人类认知和感知，这些科学模型可以为对设计像人类一样与世界交互的系统感兴趣的人工智能和机器人研究人员提供见解。包括自动驾驶汽车或假肢的控制此外，探索人们如何学习和推理物理可能为物理教育提供新的方法，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，这些工具将作为开源软件发布以开放研究。该项目研究并提出解决人类物理推理理论之间的紧张关系，这些理论表明它基于相对准确的可模拟心理模型，而那些表明它基于启发式和其他定性的理论推理的形式。研究包括与用于演示模拟理论的实验相关的实验，但经过修改，以两种截然不同的方式引入物理推理的捷径。目标 1 实验考虑了预计会遇到人力资源限制的场景，无论是在注意力、记忆力、或者时间——例如，要求人们预测由太多块组成的复杂塔的稳定性，而 Aim 2 实验考虑了可以通过模拟进行推理的场景，但可以通过简单的规则或启发式更容易地进行推理。 – 例如，研究如何人们在判断堆积有物体的平衡木将向哪个方向掉落时使用“较重的一侧会翻倒”之类的规则，根据预期的资源限制（例如，使用规则）检查这些实验中的人类行为是否与纯模拟理论存在偏差。 ，关注对象的子集，或更粗略地表示对象），并且开发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，这表明人们在一个有限的认知资源中部署有限的认知资源。有效解决他们的问题的方法行为结果和模型一起可以调查（a）人们的物理推理是否以及何时受到资源限制的限制，以及（b）人们为规避这些限制而采取的捷径类型需要开发集成的软件套件。设计这些集成包通常需要大量技术——物理模拟器、图形引擎、计算建模方法——这些技术超出了大多数心理学家的能力范围，这反过来又限制了它们。对人类物理推理的研究。 PI 在这方面处于独特的地位，因为他们的实验室专注于心理学的计算模型，并且他们在开发全球多个研究小组使用的开源软件方面拥有丰富的记录。所使用的软件套件被设计为开源的。并与更广泛的研究社区分享，以促进对人类物理推理的进一步研究，而无需广泛了解基础技术。这项工作得到了 SBE/BCS 感知、行动和认知、EHR 核心研究 (ECR) 和 CISE/IIS 的支持强壮的情报。该奖项反映了 NSF 的法定使命，并通过使用基金会的情报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

"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.