Chapter 3 (Study 2). Language and vision in conceptual processing: Multilevel analysis and statistical power

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 suggests that larger sample sizes are necessary to investigate perceptual simulation and individual differences in conceptual processing.

Over the last two decades, research in the cognitive sciences has suggested that conceptual processing depends on both language and embodiment systems. That is, understanding words involves—on the one hand—lexical and semantic associations of an amodal kind, and—on the other hand—modality-specific associations within perceptual, motor, affective and social domains (Barsalou et al., 2008; Connell, 2019; Davis & Yee, 2021; Khatin-Zadeh et al., 2021; Vigliocco et al., 2009). Studies addressing these systems have found that the language system is overall more prevalent in word processing, producing larger effect sizes (Banks et al., 2021; Kiela & Bottou, 2014; Lam et al., 2015; Louwerse et al., 2015; Pecher et al., 1998; Petilli et al., 2021). More intricately, the roles of language and embodiment are modulated by the characteristics of individuals, words and tasks. For instance, people’s individual experience with language is associated with differential effects relating to phonological, lexical and semantic features of words (Jared & O’Donnell, 2017; Pexman & Yap, 2018; Yap et al., 2009, 2012, 2017). Similarly, physical expertise and perceptual biases are associated with differences in the mental simulation of meaning (Beilock et al., 2008; Calvo-Merino et al., 2005; Vukovic & Williams, 2015). Furthermore, the embodiment system is especially suited for the processing of concrete concepts—e.g., red, building (C. R. Jones et al., 2022; Kousta et al., 2011; Ponari, Norbury, Rotaru, et al., 2018; cf. Borghi et al., 2022). Embodied information also becomes more important in the following conditions: (I) later in the time courses of word recognition (Bernabeu et al., 2017; Louwerse & Hutchinson, 2012; cf. Petilli et al., 2021) and property generation (Santos et al., 2011; Simmons et al., 2008), (II) when participants produce slower responses (Louwerse & Connell, 2011), and (III) in tasks that elicit deeper semantic processing—e.g., semantic decision, as opposed to lexical decision (Ostarek & Huettig, 2017; Petilli et al., 2021). Last, research in computational linguistics has provided further support for the complementarity of language and embodied information, by revealing increased predictive performance when models are provided with perceptual information on top of text-based information (Frome et al., 2013; Roads & Love, 2020).

In spite of the amount of evidence demonstrating the interplay between language and embodiment, there are four reasons to continue testing the interplay theory. First, the coexistence of several systems in a scientific theory must be thoroughly justified due to the value of simplicity (Gallese & Lakoff, 2005; Tillman et al., 2015). This scrutiny is particularly necessary because the language system has consistently produced larger effect sizes than the embodiment system (Banks et al., 2021; Kiela & Bottou, 2014; Lam et al., 2015; Louwerse et al., 2015; Pecher et al., 1998; Petilli et al., 2021). Therefore, it should be ruled out that the language system can suffice in all contexts.

Second, it is important to examine both language and embodiment across various levels of the experimental structure—namely, individuals (i.e., due to individual differences such as vocabulary size), words (i.e., lexical and semantic variables) and tasks (i.e., experimental conditions affecting, for instance, processing speed). Some studies have approached this comprehensive structure but there is still room to widen the scope. One of the findings revealed by cross-level analyses is the influence of word processing tasks on the importance of modality-specific information. For instance, Connell and Lynott (2014a) found that the vision-based information in words is important both for word identification (i.e., lexical decision) and for reading aloud (i.e., naming). In contrast, the auditory information in words is important for reading aloud but not so much for word identification. Another finding from cross-level research is a ‘task-relevance advantage’ for individuals that have a greater linguistic experience. Specifically, Pexman and Yap (2018) found that higher-vocabulary individuals were more sensitive to task-relevant information, such as word concreteness in the semantic decision task. Furthermore, regarding embodiment, research has revealed that individuals who are briefly exposed to a certain sport develop neural activity that allows them to mentally simulate sport-specific actions during language processing (Beilock et al., 2008). While these works have covered a large swathe of the present topic, one question remains unanswered: how does an individual’s linguistic experience relate to their sensivity to both linguistic and embodied information in words?

Third, there is some inconclusive evidence. First, some findings have suggested that higher-vocabulary participants are more sensitive to language-based information—as reflected in greater semantic priming (Yap et al., 2017)—, whereas other findings have suggested the opposite (Yap et al., 2009). Second, some studies have suggested that the language system is activated before the embodiment system (Lam et al., 2015; Louwerse & Connell, 2011), whereas a recent study suggested that this pattern does not hold in the lexical decision task (Petilli et al., 2021). Third, some evidence has suggested that female participants draw on the language system more prominently than males (Burman et al., 2008; Hutchinson & Louwerse, 2013; Jung et al., 2019; Ullman et al., 2008), whereas other research has suggested that this difference is negligible in the general population (Wallentin, 2020).

Fourth, some of the previous studies could have been affected by the scarcity of statistical power that has been identified in cognitive psychology and neuroscience (Lynott et al., 2014; Marek et al., 2022; Montero-Melis et al., 2022). Problematically, low-powered studies present more errors in the estimation of effect sizes and \(p\) values (Heyman et al., 2018; Loken & Gelman, 2017; Vasishth, Mertzen, et al., 2018). The current studies address these four key issues.


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Pablo Bernabeu, 2022. Licence: CC BY 4.0.

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