Dataset Information

Shared neural correlates for building phrases in signed and spoken language.

ABSTRACT: Research on the mental representation of human language has convincingly shown that sign languages are structured similarly to spoken languages. However, whether the same neurobiology underlies the online construction of complex linguistic structures in sign and speech remains unknown. To investigate this question with maximally controlled stimuli, we studied the production of minimal two-word phrases in sign and speech. Signers and speakers viewed the same pictures during magnetoencephalography recording and named them with semantically identical expressions. For both signers and speakers, phrase building engaged left anterior temporal and ventromedial cortices with similar timing, despite different linguistic articulators. Thus the neurobiological similarity of sign and speech goes beyond gross measures such as lateralization: the same fronto-temporal network achieves the planning of structured linguistic expressions.

SUBMITTER: Blanco-Elorrieta E

PROVIDER: S-EPMC5882945 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Shared neural correlates for building phrases in signed and spoken language.

Blanco-Elorrieta Esti E Kastner Itamar I Emmorey Karen K Pylkkänen Liina L

Scientific reports 20180403 1

Research on the mental representation of human language has convincingly shown that sign languages are structured similarly to spoken languages. However, whether the same neurobiology underlies the online construction of complex linguistic structures in sign and speech remains unknown. To investigate this question with maximally controlled stimuli, we studied the production of minimal two-word phrases in sign and speech. Signers and speakers viewed the same pictures during magnetoencephalography ...[more]

PMID: 29615785

Similar Datasets

Project description:Human language stands out in the natural world as a biological signal that uses a structured system to combine the meanings of small linguistic units (e.g., words) into larger constituents (e.g., phrases and sentences). However, the physical dynamics of speech (or sign) do not stand in a one-to-one relationship with the meanings listeners perceive. Instead, listeners infer meaning based on their knowledge of the language. The neural readouts of the perceptual and cognitive processes underlying these inferences are still poorly understood. In the present study, we used scalp electroencephalography (EEG) to compare the neural response to phrases (e.g., the red vase) and sentences (e.g., the vase is red), which were close in semantic meaning and had been synthesized to be physically indistinguishable. Differences in structure were well captured in the reorganization of neural phase responses in delta (approximately <2 Hz) and theta bands (approximately 2 to 7 Hz),and in power and power connectivity changes in the alpha band (approximately 7.5 to 13.5 Hz). Consistent with predictions from a computational model, sentences showed more power, more power connectivity, and more phase synchronization than phrases did. Theta-gamma phase-amplitude coupling occurred, but did not differ between the syntactic structures. Spectral-temporal response function (STRF) modeling revealed different encoding states for phrases and sentences, over and above the acoustically driven neural response. Our findings provide a comprehensive description of how the brain encodes and separates linguistic structures in the dynamics of neural responses. They imply that phase synchronization and strength of connectivity are readouts for the constituent structure of language. The results provide a novel basis for future neurophysiological research on linguistic structure representation in the brain, and, together with our simulations, support time-based binding as a mechanism of structure encoding in neural dynamics.

Project description:To investigate the impact of sensory-motor systems on the neural organization for language, we conducted an H2 (15)O-PET study of sign and spoken word production (picture-naming) and an fMRI study of sign and audio-visual spoken language comprehension (detection of a semantically anomalous sentence) with hearing bilinguals who are native users of American Sign Language (ASL) and English. Directly contrasting speech and sign production revealed greater activation in bilateral parietal cortex for signing, while speaking resulted in greater activation in bilateral superior temporal cortex (STC) and right frontal cortex, likely reflecting auditory feedback control. Surprisingly, the language production contrast revealed a relative increase in activation in bilateral occipital cortex for speaking. We speculate that greater activation in visual cortex for speaking may actually reflect cortical attenuation when signing, which functions to distinguish self-produced from externally generated visual input. Directly contrasting speech and sign comprehension revealed greater activation in bilateral STC for speech and greater activation in bilateral occipital-temporal cortex for sign. Sign comprehension, like sign production, engaged bilateral parietal cortex to a greater extent than spoken language. We hypothesize that posterior parietal activation in part reflects processing related to spatial classifier constructions in ASL and that anterior parietal activation may reflect covert imitation that functions as a predictive model during sign comprehension. The conjunction analysis for comprehension revealed that both speech and sign bilaterally engaged the inferior frontal gyrus (with more extensive activation on the left) and the superior temporal sulcus, suggesting an invariant bilateral perisylvian language system. We conclude that surface level differences between sign and spoken languages should not be dismissed and are critical for understanding the neurobiology of language.

Dataset Information

Shared neural correlates for building phrases in signed and spoken language.

Publications

Shared neural correlates for building phrases in signed and spoken language.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets