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. 2018 Feb 14;8:3047. doi: 10.1038/s41598-018-21062-0

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Theoretical/behavioral representational dissimilarity matrices. The binary taxonomic and thematic RDMs illustrated the membership of each word in three taxonomic categories (people, objects, and locations) and three thematic categories (school, medicine, and sports). The subjective semantic rating RDM was based on explicit ratings of semantic distance. The word2vec RDM was calculated as the cosine distance of vector representations of words learned in a skip-gram model. The logographeme, pixelwise and phonological RDMs were constructed by one minus the proportion of shared logographemes, overlapping pixels for visual words in a pictorial format and shared sub-syllabic units and tones, respectively, for a given word pair. The co-occurrence RDM was constructed based on the summed counts of co-occurrence within a window of five words for a given word pair in a language corpus.