RI: Small: Addressing Visual Analogy Problems on the Raven's Intelligence Test

RI：小：解决乌鸦智力测试中的视觉类比问题

• 批准号: 1116541
• 负责人: Ashok Goel
• 金额: $ 45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116541&HistoricalAwards=false
• 关键词: RI; Small; Addressing; Visual; Analogy

This proposal aims to create purely image-based reasoning methods for solving visual analogy problems, particularly so-called Raven's Progressive Matrices (RPM) problems. The project draws on recent results from the study of human cognition as well computer science and mathematics. Raven's Progressive Matrices consist wholly of visual analogy problems in which a matrix of geometric figures is presented with one entry missing, and the correct missing entry must be selected from a set of answer choices. Recent analysis of RPM data suggests that although in general the performance of individuals with autism on most intelligence tests is significantly inferior to that of typically developing individuals, on the Raven's test the performance of the two groups is comparable. This data is consistent with the "Thinking in Pictures" hypothesis that has been proposed as a potential, partial cognitive explanation of autism. In both artificial intelligence and psychology, current theories of solving RPM problems first convert the visual inputs into verbal representations and then process the verbal representations. In contrast, this project explores the hypothesis that many RPM problems can be solved using only visual representations, without extracting any verbal representations from the input images. This project will develop and analyze computational techniques for addressing RPM problems with only visual representations. In particular, this project will develop a novel algorithm based on affine transformations for addressing RPM problems as well as a second algorithm that makes use of fractal encodings. With both approaches -- affine and fractal -- the project seeks to achieve human-level performance on RPM in terms of percentages of problems solved correctly. The two algorithms will also be tested on the "odd-man-out" corpus that contains thousands of visual analogy problems. The project will formally characterize the set of visual analogy problems for which the affine and fractal algorithms are applicable, analyze the computational properties of the algorithms, construct proofs of their correctness for specific classes of problems, and compare the errors made by the two algorithms with those made by two groups of humans -- typically developing individuals and individuals with autism. The project will parameterize the visual algorithms to detect the settings under which the patterns of errors made by an algorithm on RPM problems most closely match the error patterns of the two human groupings. Autism is an important problem of growing social concern. While the thinking-in-pictures hypothesis has long been a significant insight into cognition in autism, and empirical evidence -- both behavioral and neuroimaging -- in its favor is increasing, there have been no computational models for it. The proposed research would help provide a computational form to this hypothesis and may help establish a disposition towards visual thinking with autism. RPM is considered one of the core tests of intelligence, and although there have been several suggestions about the visuospatial nature of RPM problems, all current computational models addressing such visual analogy problems use sequential processing on propositional representations of the input images. The algorithms from this project that rely on visual representations for RPM could provide new insights into intelligence testing. Lastly, while fractal encodings have been used in computer graphics for generating images and in computer vision for texture analysis in image processing, this project's use of fractal encodings for visual analogies on intelligence tests will contribute to knowledge of fractal computing.

该提案旨在创建纯粹基于图像的推理方法来解决视觉类似问题，尤其是所谓的Raven的渐进式矩阵（RPM）问题。该项目借鉴了人类认知研究以及计算机科学和数学研究的最新结果。 Raven的渐进式矩阵完全由视觉类比问题组成，其中显示了一个几何图形矩阵而缺少一个条目，并且必须从一组答案选择中选择正确的缺失条目。对RPM数据的最新分析表明，尽管通常在大多数智能测试中自闭症患者的表现显着劣于通常发展的个体，但在Raven的测试中，这两组的表现是可比的。这些数据与已提出的“图片思维”假设是一种潜在的自闭症的部分认知解释。在人工智能和心理学中，解决RPM问题的当前理论首先将视觉输入转换为言语表示，然后处理口头表示。相比之下，该项目探讨了以下假设：许多RPM问题只能使用视觉表示，而无需从输入图像中提取任何口头表示。该项目将开发和分析仅使用视觉表示的RPM问题来解决RPM问题的计算技术。特别是，该项目将基于解决RPM问题的仿射转换以及第二种利用分形编码的算法开发一种新颖的算法。 通过两种方法（仿射和分形），该项目旨在以正确解决的问题百分比在RPM上实现人级绩效。这两种算法还将在包含数千个视觉类比问题的“奇数人”语料库上进行测试。该项目将正式表征仿射和分形算法适用的一组视觉类似物问题，分析算法的计算属性，构建其对特定问题类别的正确性的证明，并比较两种算法与两组人所构成的算法所犯的错误 - 典型的人 - 典型的个人和亲自的人。该项目将对视觉算法进行参数化，以检测算法在RPM问题上造成的错误模式，最与两个人类分组的误差模式最为匹配。自闭症是日益严重关注的重要问题。尽管长期以来的思维假设一直是对自闭症的认知的重大见解，而经验证据（行为和神经影像学都在增加）都在增加，但没有计算模型。拟议的研究将有助于为该假设提供计算形式，并可能有助于用自闭症建立对视觉思维的倾向。 rpm被认为是智力的核心测试之一，尽管关于RPM问题的视觉空间性质有一些建议，但所有解决此类视觉类似问题问题的当前计算模型都在输入图像的命题表示上使用顺序处理。该项目依靠RPM视觉表示的算法可以为智能测试提供新的见解。最后，尽管分形编码已在计算机图形中用于生成图像和计算机视觉中用于图像处理中的纹理分析，但该项目将分形编码用于智能测试中的可视化类比，将有助于分形计算的知识。