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Gestural enhancement of degraded speech comprehension engages the language network, motor and visual cortex as reflected by a decrease in the alpha and beta band

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Drijvers,  Linda
Center for Language Studies , External Organizations;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Ozyurek,  Asli
Center for Language Studies , External Organizations;
Research Associates, MPI for Psycholinguistics, Max Planck Society;

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引用

Drijvers, L., Ozyurek, A., & Jensen, O. (2016). Gestural enhancement of degraded speech comprehension engages the language network, motor and visual cortex as reflected by a decrease in the alpha and beta band. Poster presented at the Eighth Annual Meeting of the Society for the Neurobiology of Language (SNL 2016), London, UK.


引用: https://hdl.handle.net/11858/00-001M-0000-002B-0BE4-3
要旨
Face-to-face communication involves the integration of speech and visual information, such as iconic co-speech gestures. Especially iconic gestures, that illustrate object attributes, actions and space, can enhance speech comprehension in adverse listening conditions (e.g. Holle et al., 2010). Using magnetoencephalography (MEG), we aimed at identifying the networks and the neuronal dynamics associated with enhancing (degraded) speech comprehension by gestures. Our central hypothesis was that gestures enhance degraded speech comprehension, and that decreases in alpha and beta power reflect engagement, whereas increases in gamma reflect active processing in task relevant networks (Jensen & Mazaheri, 2010; Jokisch & Jensen, 2007). Participants (n = 30) were presented with videos of an actress uttering Dutch action verbs. Speech was presented clear or degraded by applying noise-vocoding (6-band), and accompanied by videos of an actor performing iconic gesture depicting actions (clear speech+ gesture; C-SG, degraded speech+gesture; D-SG) or no gesture (clear speech only; C-S, degraded speech only; D-S). We quantified changes in time-frequency representations of oscillatory power as the video unfolded. The sources of the task-specific modulations were identified using a beamformer approach. Gestural enhancement, calculated by comparing (D-SG vs DS) to (C-SG vs CS), revealed significant interactions between the occurrence of a gesture and degraded speech particularly in the alpha, beta and gamma band. Gestural enhancement was reflected by a beta decrease in motor areas indicative of engagement of the motor system during gesture observation, especially when speech was degraded. A beta band decrease was also observed in the language network including left inferior frontal gyrus, a region involved in semantic unification operations, and left superior temporal regions. This suggests a higher semantic unification load when a gesture is presented together with degraded versus clear speech. We also observed a gestural enhancement effect in the alpha band in visual areas. This suggests that visual areas are more engaged when a gesture is present, most likely reflecting the allocation of visual attention, especially when speech is degraded, which is in line with the functional inhibition hypothesis (see Jensen & Mazaheri, 2010). Finally we observed gamma band effects in left-temporal areas suggesting facilitated binding of speech and gesture into a unified representation, especially when speech is degraded. In conclusion, our results support earlier claims on the recruitment of a left-lateralized network including motor areas, STS/MTG and LIFG in speech-gesture integration and gestural enhancement of speech (see Ozyurek, 2014). Our findings provide novel insight into the neuronal dynamics associated with speech-gesture integration: decreases in alpha and beta power reflect the engagement of respectively the visual and language/motor networks, whereas a gamma band increase reflects the integrations in left prefrontal cortex. In future work we will characterize the interaction between these networks by means of functional connectivity analysis.