为了科学,临床,和机器学习的目的一样,量化高级视觉感知的口头报告是可取的。目前根本不存在这样做的方法。在这里,我们提出了一种新颖的方法论原理来帮助填补这一空白,并提供旨在作为这一原则的初步“证明”的经验证据。在提出的方法中,受试者查看现实世界场景的图像并描述,用他们自己的话说,他们看到了什么。口头描述由几个评估者独立评估。每个评估者使用新颖的排名原则为受试者对每个图像中每个视觉对象的描述分配一个排名分数,它利用了众所周知的事实,即现实生活对象和场景的语义描述通常可以按顺序排列。因此,例如,\"动物,\"\"狗,\"和\"检索器\"可以被视为越来越精细的水平,因此排名更高,给定对象的描述。这些数字分数可以保留原始口头描述的丰富性,并且可以随后使用常规统计程序进行评估。我们描述了该方法的示例实施例和证明其可行性的经验数据。通过适当的未来标准化和验证,这种新颖的方法可以作为帮助量化视觉世界的主观体验的重要工具。除了小说,潜在强大的测试工具,我们的方法也代表,根据我们的知识,唯一可用的方法来数字表示现实世界经验的口头说明。鉴于其最低要求,即,口头描述和引出描述的真相,我们的方法有各种各样的潜在的实际应用。
For scientific, clinical, and machine learning purposes alike, it is desirable to quantify the verbal
reports of high-level visual percepts. Methods to do this simply do not exist at present. Here we propose a novel methodological principle to help fill this gap, and provide empirical evidence designed to serve as the initial \"proof\" of this principle. In the proposed method, subjects view images of real-world scenes and describe, in their own words, what they saw. The verbal description is independently evaluated by several evaluators. Each evaluator assigns a rank score to the subject\'s description of each visual object in each image using a novel ranking principle, which takes advantage of the well-known fact that semantic descriptions of real life objects and scenes can usually be rank-ordered. Thus, for instance, \"animal,\" \"dog,\" and \"retriever\" can be regarded as increasingly finer-level, and therefore higher ranking, descriptions of a given object. These numeric scores can preserve the richness of the original verbal description, and can be subsequently evaluated using conventional statistical procedures. We describe an exemplar implementation of this method and empirical data that show its feasibility. With appropriate future standardization and validation, this novel method can serve as an important tool to help quantify the subjective experience of the visual world. In addition to being a novel, potentially powerful testing tool, our method also represents, to our knowledge, the only available method for numerically representing verbal accounts of real-world experience. Given that its minimal requirements, i.e., a verbal description and the ground truth that elicited the description, our method has a wide variety of potential real-world applications.