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The perceived mapping between form and meaning in American Sign Language depends on linguistic knowledge and task: evidence from iconicity and transparency judgments

Published online by Cambridge University Press:  12 July 2019

ZED SEVCIKOVA SEHYR
Affiliation:
San Diego State University
KAREN EMMOREY
Affiliation:
San Diego State University

Abstract

Iconicity is often defined as the resemblance between a form and a given meaning, while transparency is defined as the ability to infer a given meaning based on the form. This study examined the influence of knowledge of American Sign Language (ASL) on the perceived iconicity of signs and the relationship between iconicity, transparency (correctly guessed signs), ‘perceived transparency’ (transparency ratings of the guesses), and ‘semantic potential’ (the diversity (H index) of guesses). Experiment 1 compared iconicity ratings by deaf ASL signers and hearing non-signers for 991 signs from the ASL-LEX database. Signers and non-signers’ ratings were highly correlated; however, the groups provided different iconicity ratings for subclasses of signs: nouns vs. verbs, handling vs. entity, and one- vs. two-handed signs. In Experiment 2, non-signers guessed the meaning of 430 signs and rated them for how transparent their guessed meaning would be for others. Only 10% of guesses were correct. Iconicity ratings correlated with transparency (correct guesses), perceived transparency ratings, and semantic potential (H index). Further, some iconic signs were perceived as non-transparent and vice versa. The study demonstrates that linguistic knowledge mediates perceived iconicity distinctly from gesture and highlights critical distinctions between iconicity, transparency (perceived and objective), and semantic potential.

Type
Special Issue on Iconicity
Copyright
Copyright © UK Cognitive Linguistics Association 2019 

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Footnotes

*

This work was supported by the National Institutes of Health Grant R01 DC010997 and by the National Science Foundation Grant BCS-1625954. The authors would like to thank Dan Fisher for help with data coding.

References

references

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