A touch with words: Dynamic synergies between manual actions and language. (September 2016)

• Record Type: Journal Article
• Authors: García, Adolfo M.; Ibáñez, Agustín
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 68(2016)
• Page Start: 59
• Page End: 95
• Publication Date: 2016-09
• Subjects: Language embodiment -- Hand-related language -- Manual-action networks -- Predictive coding -- Enactive cognition; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2016.04.022
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: We review 108 experiments involving manual responses to hand-related verbal units. Such units either facilitate or delay manual actions in different time windows. Effects depend on verbal and motor demands, and the time-lag between both processes. We model key findings using dynamic neural-network and predictive-coding tenets. Our model paves the way for new basic and applied research on embodied cognition. Abstract: Manual actions are a hallmark of humanness. Their underlying neural circuitry gives rise to species-specific skills and interacts with language processes. In particular, multiple studies show that hand-related expressions – verbal units evoking manual activity – variously affect concurrent manual actions, yielding apparently controversial results (interference, facilitation, or null effects) in varied time windows. Through a systematic review of 108 experiments, we show that such effects are driven by several factors, such as the level of verbal processing, action complexity, and the time-lag between linguistic and motor processes. We reconcile key empirical patterns by introducing the Hand-Action-Network Dynamic Language Embodiment (HANDLE) model, an integrative framework based on neural coupling dynamics and predictive-coding principles. To conclude, we assess HANDLE against the backdrop of other action-cognition theories, illustrate its potential applications to understand high-level deficits in motor disorders, and discuss key challenges for further development. In sum, our work aligns with the 'pragmatic turn', moving away from passive and static representationalist perspectives to a more dynamic, enactive, and embodied conceptualization of cognitive processes.