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The embodied penman: Effector-specific motor-language integration during handwriting
- Publication Year :
- 2019
-
Abstract
- Several studies have yielded fine-grained insights on the embodied dynamics of language by revealing how processing of manual action verbs (MaVs) affects the programming or execution of concurrent hand movements. However, virtually all extant studies have relied on highly contrived dual tasks in which independent motoric and linguistic processes are arbitrarily related. To circumvent potential attentional confounds, we conducted the first assessment of motor-language integration during handwriting, an early acquired skill that necessarily integrates both types of processes. Using a digital pen, participants copied carefully matched MaVs, non-manual action verbs, and non-action verbs as we collected measures of motor programming (the time needed to start the writing routine after verb presentation) and motor execution (the time needed to write the whole verb). Whereas motor programming latencies were similar across conditions, the unfolding of motor routines was faster for MaVs than for the other two categories, irrespective of the subjects’ daily writing time. Moreover, this effect remained consistent regardless of whether word meanings were accessed implicitly or explicitly. In line with the Hand-Action-Network Dynamic Language Embodiment (HANDLE) model, such findings suggest that everyday manual movements can be primed by effector-congruent verbs, even in a highly automatized task that seamlessly combines linguistic and motoric processes. In addition, this effect differs from that observed for MaVs in a previous (keyboard-based) typing experiment, suggesting that language-induced sensorimotor resonance during writing depends on the motoric particularities of each production modality. More generally, our paradigm opens new avenues for fine-grained explorations of embodied language processes.
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.r3110ae70d66..7e0f305c2acd556c9bc98a4526ad570e