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Updated: Jun 27, 2025

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
Published on: June 29, 2021
Statistical relationships between surface form and sensory meanings of English words influence lexical processing
Greig I de Zubicaray1, Elaine Kearney1, Frank Guenther2
1Faculty of Health, School of Psychology and Counselling, Queensland University of Technology (QUT).
Abstract:
Across spoken languages, there are some words whose acoustic features resemble the meanings of their referents by evoking perceptual imagery, i.e., they are iconic (e.g., in English, "splash" imitates the sound of an object hitting water). While these sound symbolic form-meaning relationships are well-studied, relatively little work has explored whether the sensory properties of English words also involve systematic (i.e., statistical) form-meaning mappings. We first test the prediction that surface form properties can predict sensory experience ratings for over 5,000 monosyllabic and disyllabic words (Juhasz & Yap, 2013), confirming they explain a significant proportion of variance. Next, we show that iconicity and sensory form typicality, a statistical measure of how well a word's form aligns with its sensory experience rating, are only weakly related to each other, indicating they are likely to be distinct constructs. To determine whether form typicality influences processing of sensory words, we conducted regression analyses using lexical decision, word recognition, naming and semantic decision tasks from behavioral megastudy data sets. Across the data sets, sensory form typicality was able to predict more variance in performance than sensory experience or iconicity ratings. Further, the effects of typicality were consistently inhibitory in comprehension (i.e., more typical forms were responded to more slowly and less accurately), whereas for production the effect was facilitatory. These findings are the first evidence that systematic form-meaning mappings in English sensory words influence their processing. We discuss how language processing models incorporating Bayesian prediction mechanisms might be able to account for form typicality in the lexicon. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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