Victor Jonathan D, Aguilar Guillermo, Waraich Suniyya A
Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065.
ArXiv. 2025 Feb 12:arXiv:2310.07543v3.
Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes limited assumptions about how perceptual distances are converted into similarity judgments. The approach starts from a dataset of empirical judgments of relative similarities: the fraction of times that a subject chooses one of two comparison stimuli to be more similar to a reference stimulus. These empirical judgments provide Bayesian estimates of underling choice probabilities. From these estimates, we derive indices that characterize the set of judgments in three ways: compatibility with a symmetric dis-similarity, compatibility with an ultrametric space, and compatibility with an additive tree. Each of the indices is derived from rank-order relationships among the choice probabilities that, as we show, are necessary and sufficient for local consistency with the three respective characteristics. We illustrate this approach with simulations and example psychophysical datasets of dis-similarity judgments in several visual domains and provide code that implements the analyses at https://github.com/jvlab/simrank.
在感知或语义领域内刻画相似性判断，并从这些判断中推断该领域的潜在结构，在认知神经科学和系统神经科学中发挥着越来越重要的作用。我们为此提出了一个新框架，该框架对感知距离如何转化为相似性判断做出了有限的假设。该方法从相对相似性的实证判断数据集开始：即受试者选择两个比较刺激中的一个比另一个更类似于参考刺激的次数比例。这些实证判断提供了潜在选择概率的贝叶斯估计。基于这些估计，我们以三种方式得出表征判断集的指标：与对称不相似性的兼容性、与超度量空间的兼容性以及与加法树的兼容性。每个指标都源自选择概率之间的排序关系，正如我们所展示的，这些关系对于与这三个各自特征的局部一致性而言是必要且充分的。我们用几个视觉领域中不相似性判断的模拟和示例心理物理学数据集来说明这种方法，并在https://github.com/jvlab/simrank上提供实现这些分析的代码。
