Department of Psychology.
J Exp Psychol Gen. 2013 Feb;142(1):256-281. doi: 10.1037/a0028860. Epub 2012 Jul 2.
Although traditional methods to collect similarity data (for multidimensional scaling [MDS]) are robust, they share a key shortcoming. Specifically, the possible pairwise comparisons in any set of objects grow rapidly as a function of set size. This leads to lengthy experimental protocols, or procedures that involve scaling stimulus subsets. We review existing methods of collecting similarity data, and critically examine the spatial arrangement method (SpAM) proposed by Goldstone (1994a), in which similarity ratings are obtained by presenting many stimuli at once. The participant moves stimuli around the computer screen, placing them at distances from one another that are proportional to subjective similarity. This provides a fast, efficient, and user-friendly method for obtaining MDS spaces. Participants gave similarity ratings to artificially constructed visual stimuli (comprising 2-3 perceptual dimensions) and nonvisual stimuli (animal names) with less-defined underlying dimensions. Ratings were obtained with 4 methods: pairwise comparisons, spatial arrangement, and 2 novel hybrid methods. We compared solutions from alternative methods to the pairwise method, finding that the SpAM produces high-quality MDS solutions. Monte Carlo simulations on degraded data suggest that the method is also robust to reductions in sample sizes and granularity. Moreover, coordinates derived from SpAM solutions accurately predicted discrimination among objects in same-different classification. We address the benefits of using a spatial medium to collect similarity measures.
虽然传统的相似性数据收集方法（多维标度 [MDS]）很稳健，但它们都有一个关键的缺点。具体来说，任何一组对象中的可能成对比较随着集合大小呈指数级增长。这导致了冗长的实验方案或涉及刺激子集缩放的过程。我们回顾了现有的相似性数据收集方法，并批判性地检查了 Goldstone（1994a）提出的空间排列方法（SpAM），其中通过一次呈现许多刺激来获得相似性评分。参与者在计算机屏幕上移动刺激物，将它们彼此之间的距离放置成与主观相似性成比例。这提供了一种快速、高效、用户友好的方法来获得 MDS 空间。参与者对人工构建的视觉刺激物（包含 2-3 个感知维度）和非视觉刺激物（动物名称）进行了相似性评分，这些刺激物的维度定义不太明确。使用 4 种方法获得了评分：成对比较、空间排列和 2 种新颖的混合方法。我们比较了替代方法与成对方法的解决方案，发现 SpAM 产生了高质量的 MDS 解决方案。对退化数据的蒙特卡罗模拟表明，该方法对样本量和粒度的减少也具有稳健性。此外，从 SpAM 解决方案得出的坐标准确预测了相同-不同分类中对象之间的区分。我们探讨了使用空间媒介收集相似性度量的好处。
