Díez Emiliano, Díez-Álamo Antonio M, Alonso María A, Wojcik Dominika Z, Fernandez Angel
Faculty of Psychology, Institute on Community Integration (INICO), University of Salamanca, Salamanca, Spain.
Institute on Neuroscience (IUNE), University of La Laguna, Tenerife, Spain.
Front Psychol. 2024 Oct 14;15:1467796. doi: 10.3389/fpsyg.2024.1467796. eCollection 2024.
This study investigated the transparency and translucency of 1,525 pictograms from the Aragonese Portal of Augmentative and Alternative Communication (ARASAAC). A total of 521 participants took part in tasks that involved providing the word that best described the meaning of a pictogram or rating the relationship between a pictogram and a verbal label. This process allowed us to obtain indices of transparency (the quality of pictograms that makes their meaning easily "guessable" in the absence of their referent) and translucency (the degree of perceived relationship between the pictogram and its referent when the latter is present) which were further analyzed to assess their reliability and comparability with similar studies. Additionally, the relationship of those indices with various visual and psycholinguistic characteristics was explored, particularly focusing on the match between the original ARASAAC pictogram names and the most frequently provided names by the participants (modal names) for the pictograms. Results showed relatively low levels of transparency, as well as high levels of translucency, with nouns displaying the highest values in both metrics. For transparency and translucency, word imageability and concreteness were the most correlated factors, and, together with age of acquisition, they were the most important features related to the name matching with ARASAAC. The norms derived from this study enhance our understanding of pictogram perception, empowering stakeholders to leverage data-driven insights for the development and implementation of pictographic systems, thus improving cognitive accessibility.
本研究调查了阿拉贡辅助与替代沟通门户网站（ARASAAC）中1525个象形图的透明度和半透明度。共有521名参与者参与了相关任务，这些任务包括提供最能描述象形图含义的单词，或对标象形图与文字标签之间的关系进行评分。通过这个过程，我们获得了透明度（在没有指代对象的情况下，象形图的含义易于“猜测”的质量）和半透明度（当指代对象存在时，象形图与其指代对象之间的感知关系程度）的指标，并对其进行了进一步分析，以评估它们的可靠性以及与类似研究的可比性。此外，还探讨了这些指标与各种视觉和心理语言学特征的关系，特别关注ARASAAC象形图的原始名称与参与者为象形图提供的最常见名称（模态名称）之间的匹配度。结果显示，透明度水平相对较低，半透明度水平较高，名词在这两个指标上的值最高。对于透明度和半透明度而言，单词的可想象性和具体性是最相关的因素，并且与习得年龄一起，它们是与ARASAAC名称匹配最相关的重要特征。本研究得出的规范增强了我们对象形图感知的理解，使利益相关者能够利用数据驱动的见解来开发和实施象形图系统，从而提高认知可及性。
