Synchronization of Speech and Gesture : Evidence for Interaction in Action

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Electrophysiological and kinematic correlates of communicative intent in the planning and production of pointing gestures and speech

Acknowledgements We thank Albert Russel for assistance in setting up the experiments, and Charlotte Paulisse for help in data collection.Peer reviewedPublisher PD

Beat that Word : How Listeners Integrate Beat Gesture and Focus in Multimodal Speech Discourse

Peer reviewedPublisher PD

Body-Specific Motor Imagery of Hand Actions: Neural Evidence from Right- and Left-Handers

If motor imagery uses neural structures involved in action execution, then the neural correlates of imagining an action should differ between individuals who tend to execute the action differently. Here we report fMRI data showing that motor imagery is influenced by the way people habitually perform motor actions with their particular bodies; that is, motor imagery is ‘body-specific’ (Casasanto, 2009). During mental imagery for complex hand actions, activation of cortical areas involved in motor planning and execution was left-lateralized in right-handers but right-lateralized in left-handers. We conclude that motor imagery involves the generation of an action plan that is grounded in the participant's motor habits, not just an abstract representation at the level of the action's goal. People with different patterns of motor experience form correspondingly different neurocognitive representations of imagined actions

Opposing and following responses in sensorimotor speech control : why responses go both ways

When talking, speakers continuously monitor and use the auditory feedback of their own voice to control and inform speech production processes. When speakers are provided with auditory feedback that is perturbed in real time, most of them compensate for this by opposing the feedback perturbation. But some speakers follow the perturbation. In the current study, we investigated whether the state of the speech production system at perturbation onset may determine what type of response (opposing or following) is given. The results suggest that whether a perturbation-related response is opposing or following depends on ongoing fluctuations of the production system: It initially responds by doing the opposite of what it was doing. This effect and the non-trivial proportion of following responses suggest that current production models are inadequate: They need to account for why responses to unexpected sensory feedback depend on the production-system’s state at the time of perturbation

In dialogue with an avatar, language behaviour is identical compared to dialogue with a human partner.

The use of virtual reality (VR) as a methodological tool is becoming increasingly popular in behavioral research as its flexibility allows for a wide range of applications. This new method has not been as widely accepted in the field of psycholinguistics, however, possibly due to the assumption that language processing during human-computer interactions does not accurately reflect human-human interactions. Yet at the same time there is a growing need to study human-human language interactions in a tightly controlled context, which has not been possible using existing methods. VR, however, offers experimental control over parameters that cannot be (as finely) controlled in the real world. As such, in this study we aim to show that human-computer language interaction is comparable to human-human language interaction in virtual reality. In the current study we compare participants’ language behavior in a syntactic priming task with human versus computer partners: we used a human partner, a human-like avatar with human-like facial expressions and verbal behavior, and a computer-like avatar which had this humanness removed. As predicted, our study shows comparable priming effects between the human and human-like avatar suggesting that participants attributed human-like agency to the human-like avatar. Indeed, when interacting with the computer-like avatar, the priming effect was significantly decreased. This suggests that when interacting with a human-like avatar, sentence processing is comparable to interacting with a human partner. Our study therefore shows that VR is a valid platform for conducting language research and studying dialogue interactions in an ecologically valid manner

Deep Learning Models to Study Sentence Comprehension in the Human Brain

Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data