University of Pennsylvania, 3720 Walnut St, Philadelphia, PA, 19104, USA.
New Mexico State University, Las Cruces, NM, USA.
Behav Res Methods. 2020 Oct;52(5):1906-1928. doi: 10.3758/s13428-020-01362-y.
Psychologists collect similarity data to study a variety of phenomena including categorization, generalization and discrimination, and representation itself. However, collecting similarity judgments between all pairs of items in a set is expensive, spurring development of techniques like the Spatial Arrangement Method (SpAM; Goldstone, Behavior Research Methods, Instruments, & Computers, 26, 381-386, 1994), wherein participants place items on a two-dimensional plane such that proximity reflects perceived similarity. While SpAM greatly hastens similarity measurement, and has been successfully used for lower-dimensional, perceptual stimuli, its suitability for higher-dimensional, conceptual stimuli is less understood. In study 1, we evaluated the ability of SpAM to capture the semantic structure of eight different categories composed of 20-30 words each. First, SpAM distances correlated strongly (r = .71) with pairwise similarity judgments, although below SpAM and pairwise judgment split-half reliabilities (r's > .9). Second, a cross-validation exercise with multidimensional scaling fits at increasing latent dimensionalities suggested that aggregated SpAM data favored higher (> 2) dimensional solutions for seven of the eight categories explored here. Third, split-half reliability of SpAM dissimilarities was high (Pearson r = .90), while the average correlation between pairs of participants was low (r = .15), suggesting that when different participants focus on different pairs of stimulus dimensions, reliable high-dimensional aggregate similarity data is recoverable. In study 2, we show that SpAM can recover the Big Five factor space of personality trait adjectives, and that cross-validation favors a four- or five-dimension solution on this dataset. We conclude that SpAM is an accurate and reliable method of measuring similarity for high-dimensional items like words. We publicly release our data for researchers.
心理学家收集相似性数据来研究多种现象，包括分类、概括和辨别，以及表示本身。然而，收集一个集合中所有对物品之间的相似性判断是昂贵的，这促使了像空间排列法（SpAM；Goldstone，行为研究方法，仪器和计算机，26，381-386，1994）这样的技术的发展，其中参与者将物品放置在二维平面上，使得接近度反映了感知到的相似性。虽然 SpAM 极大地加快了相似性的测量，并且已经成功地用于低维、感知刺激，但它对更高维、概念性刺激的适用性了解较少。在研究 1 中，我们评估了 SpAM 捕捉由 20-30 个单词组成的八个不同类别语义结构的能力。首先，SpAM 距离与成对相似性判断高度相关（r =.71），尽管低于 SpAM 和成对判断的分半可靠性（r's >.9）。其次，在增加潜在维度的多维标度拟合的交叉验证练习中，对于这里探索的八个类别中的七个，聚合的 SpAM 数据支持更高（> 2）维的解决方案。第三，SpAM 不相似性的分半可靠性很高（Pearson r =.90），而参与者之间的平均相关性很低（r =.15），这表明当不同的参与者关注不同的刺激维度对时，可以恢复可靠的高维聚合相似性数据。在研究 2 中，我们表明 SpAM 可以恢复人格特质形容词的大五因素空间，并且交叉验证支持在这个数据集上的四或五维解决方案。我们的结论是，SpAM 是一种准确可靠的高维项目相似性测量方法，如单词。我们为研究人员公开发布我们的数据。
