Department of Psychology, Lancaster University, Lancaster, UK.
Department of Experimental Psychology, Ghent University, Ghent, Belgium.
Behav Res Methods. 2020 Jun;52(3):1271-1291. doi: 10.3758/s13428-019-01316-z.
Sensorimotor information plays a fundamental role in cognition. However, the existing materials that measure the sensorimotor basis of word meanings and concepts have been restricted in terms of their sample size and breadth of sensorimotor experience. Here we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon's Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: They represent the largest-ever set of semantic norms for English, at 40,000 words × 11 dimensions (plus several informative cross-dimensional variables), they extend perceptual strength norming to the new modality of interoception, and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies, in which we (1) extracted an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength) and (2) demonstrated the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas, including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big-data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).
感觉运动信息在认知中起着基础作用。然而，现有的衡量词义和概念的感觉运动基础的材料在样本量和感觉运动经验的广度方面受到限制。在这里，我们提出了 39707 个概念在六种感觉模式（触觉、听觉、嗅觉、味觉、视觉和内感受）和五个动作效应器（嘴/喉咙、手/臂、脚/腿、头不包括嘴/喉咙和躯干）的感觉运动强度的规范，这些规范是从亚马逊的 Mechanical Turk 平台上的 3500 名个体参与者中收集的。兰开斯特感觉运动规范在许多方面是独特和创新的：它们代表了英语中最大的语义规范集，共有 40000 个单词×11 个维度（加上几个信息性的跨维度变量），它们将感觉运动强度规范扩展到新的内感受模态，并且包括对单独身体效应器的动作强度的首次规范。在第一项研究中，我们描述了数据收集程序，提供了数据集的总结描述，并解释了观察到的感觉运动维度之间的关系。然后，我们报告了另外两项研究，在这两项研究中，我们（1）从 11 维感觉运动特征中提取出一个最佳的单一变量组合（Minkowski 3 强度），（2）在两个独立的数据集上展示了知觉和动作强度在促进词汇决策时间和准确性方面的效用。这些规范为包括心理语言学、扎根认知、认知语义学、知识表示、机器学习和语言及概念表示的大数据分析在内的不同领域的研究人员提供了宝贵的资源。这些数据可以通过开放科学框架（http://osf.io/7emr6/）和一个交互式网络应用程序（https://www.lancaster.ac.uk/psychology/lsnorms/）获得。
