Language and vision in conceptual processing: Multilevel analysis and statistical power

Abstract

Research has suggested that conceptual processing depends on both language-based and vision-based information. We tested this interplay at three levels of the experimental structure: individuals, words and tasks. To this end, we drew on three existing, large data sets that implemented the paradigms of semantic priming, semantic decision and lexical decision. We extended these data sets with measures of language-based and vision-based information, and analysed how the latter variables interacted with participants’ vocabulary size and gender, and also with presentation speed in the semantic priming study. We performed the analysis using mixed-effects models that included a comprehensive array of fixed effects—including covariates—and random effects. First, we found that language-based information was more important than vision-based information. Second, in the semantic priming study—whose task required distinguishing between words and nonwords—, both language-based and vision-based information were more influential when words were presented faster. Third, a ‘task-relevance advantage’ was identified in higher-vocabulary participants. Specifically, in lexical decision, higher-vocabulary participants were more sensitive to language-based information than lower-vocabulary participants. In contrast, in semantic decision, higher-vocabulary participants were more sensitive to word concreteness. Fourth, we demonstrated the influence of the analytical method on the results. These findings support the interplay between language and vision in conceptual processing, and demonstrate the influence of measurement instruments on the results. Last, we estimated the sample size required to reliably investigate various effects. We found that 300 participants were sufficient to examine the effect of language-based information contained in words, whereas more than 1,000 participants were necessary to examine the effect of vision-based information and the interactions of both former variables with vocabulary size, gender and presentation speed. In conclusion, this power analysis reveals the need to increase sample sizes when conducting research on perceptual simulation and individual differences.