当前位置: X-MOL 学术Language and Cognition › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
The iconicity toolbox: empirical approaches to measuring iconicity
Language and Cognition ( IF 1.1 ) Pub Date : 2019-06-27 , DOI: 10.1017/langcog.2019.14
YASAMIN MOTAMEDI , HANNAH LITTLE , ALAN NIELSEN , JUSTIN SULIK

abstractGrowing evidence from across the cognitive sciences indicates that iconicity plays an important role in a number of fundamental language processes, spanning learning, comprehension, and online use. One benefit of this recent upsurge in empirical work is the diversification of methods available for measuring iconicity. In this paper, we provide an overview of methods in the form of a ‘toolbox’. We lay out empirical methods for measuring iconicity at a behavioural level, in the perception, production, and comprehension of iconic forms. We also discuss large-scale studies that look at iconicity on a system-wide level, based on objective measures of similarity between signals and meanings. We give a detailed overview of how different measures of iconicity can better address specific hypotheses, providing greater clarity when choosing testing methods.

中文翻译:

象似性工具箱:衡量象似性的经验方法

摘要来自认知科学的越来越多的证据表明,象似性在许多基本语言过程中发挥着重要作用,包括学习、理解和在线使用。近期实证研究热潮的一个好处是可用于测量象似性的方法的多样化。在本文中,我们以“工具箱”的形式对方法进行了概述。我们列出了在行为层面上测量象似性的经验方法,在对象形形式的感知、生产和理解中。我们还讨论了基于信号和意义之间相似性的客观测量,在系统范围内观察象似性的大规模研究。我们详细概述了不同的象似性度量如何更好地解决特定假设,从而在选择测试方法时提供更大的清晰度。
更新日期:2019-06-27
down
wechat
bug