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Indexical variation affects semantic spread
The role of indexical variation in spoken word recognition is constrained to acoustically rich lexical representations. Theoretically, lexical activation depends on indexical variation, but subsequent processes like associative semantic spread depend on activation strength, not indexical variation....
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Published in: | The Journal of the Acoustical Society of America 2014-04, Vol.135 (4_Supplement), p.2425-2425 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Citations: | Items that cite this one |
Online Access: | Get full text |
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Summary: | The role of indexical variation in spoken word recognition is constrained to acoustically rich lexical representations. Theoretically, lexical activation depends on indexical variation, but subsequent processes like associative semantic spread depend on activation strength, not indexical variation. Social psychological theories view indexical variation as integral to online processes such as persona construal. Therefore, information gleaned from indexical variation might pervade spoken word recognition more broadly. We investigate the effects of indexical variation on semantic activation in word-association and semantic-priming paradigms. Across three studies, we show that top associate responses depend on the voice of the probe word (“space” in man’s voice: time; woman’s voice: star; child’s voice: planet). Voice also affects response frequency distributions: the man’s voice receives a wider variety of weaker responses, while the woman’s and child’s voices receive fewer, stronger, responses. We also find that semantic priming varies as a function of voice-specific word association strength: priming is stronger to strong voice-specific associates (woman: space-star) than to weak associates (woman: space-time). We argue that indexical variation affects spoken word recognition beyond an episodic lexicon and provide an account capturing effects of learned associations between acoustic patterns and linguistic and social features in spoken language processing. |
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ISSN: | 0001-4966 1520-8524 |
DOI: | 10.1121/1.4878064 |