13,545 research outputs found

    Neuronal bases of structural coherence in contemporary dance observation

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    The neuronal processes underlying dance observation have been the focus of an increasing number of brain imaging studies over the past decade. However, the existing literature mainly dealt with effects of motor and visual expertise, whereas the neural and cognitive mechanisms that underlie the interpretation of dance choreographies remained unexplored. Hence, much attention has been given to the Action Observation Network (AON) whereas the role of other potentially relevant neuro-cognitive mechanisms such as mentalizing (theory of mind) or language (narrative comprehension) in dance understanding is yet to be elucidated. We report the results of an fMRI study where the structural coherence of short contemporary dance choreographies was manipulated parametrically using the same taped movement material. Our participants were all trained dancers. The whole-brain analysis argues that the interpretation of structurally coherent dance phrases involves a subpart (Superior Parietal) of the AON as well as mentalizing regions in the dorsomedial Prefrontal Cortex. An ROI analysis based on a similar study using linguistic materials (Pallier et al. 2011) suggests that structural processing in language and dance might share certain neural mechanisms

    Leading and following: Noise differently affects semantic and acoustic processing during naturalistic speech comprehension

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    Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and decreasing speech comprehension and intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments

    Language dysfunction in schizophrenia: Assessing neural tracking to characterize the underlying disorder(s)?

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    Deficits in language production and comprehension are characteristic of schizophrenia. To date, it remains unclear whether these deficits arise from dysfunctional linguistic knowledge, or dysfunctional predictions derived from the linguistic context. Alternatively, the deficits could be a result of dysfunctional neural tracking of auditory information resulting in decreased auditory information fidelity and even distorted information. Here, we discuss possible ways for clinical neuroscientists to employ neural tracking methodology to independently characterize deficiencies on the auditory–sensory and abstract linguistic levels. This might lead to a mechanistic understanding of the deficits underlying language related disorder(s) in schizophrenia. We propose to combine naturalistic stimulation, measures of speech–brain synchronization, and computational modeling of abstract linguistic knowledge and predictions. These independent but likely interacting assessments may be exploited for an objective and differential diagnosis of schizophrenia, as well as a better understanding of the disorder on the functional level—illustrating the potential of neural tracking methodology as translational tool in a range of psychotic populations

    Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions

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    Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, c ingulate, and insular regions. Activity in this “multiple demand” (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people (n=45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. Keywords: comprehension; functional localization; intersubject correlation; language network; multiple-demand network; naturalistic cognitio
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