Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

Departing from rule-based linguistic models, advances in deep learning resulted in a new type of autoregressive deep language models (DLMs). These models are trained using a self-supervised next word prediction task. We provide empirical evidence for the connection between autoregressive DLMs and the human language faculty using spoken narrative and electrocorticographic recordings. Behaviorally, we demonstrate that humans have a remarkable capacity for word prediction in natural contexts, and that, given a sufficient context window, DLMs can attain human-level prediction performance. Leveraging on DLM embeddings we demonstrate that the brain constantly and spontaneously predicts the identity of the next word in natural speech, hundreds of milliseconds before they are perceived. Finally, we demonstrate that contextual embeddings derived from autoregressive DLMs capture neural representations of the unique, context-specific meaning of words in the narrative. Our findings suggest that DLMs provides a novel biologically feasible computational framework for studying the neural basis of language.

Objective:The combined spatiotemporal dynamics underlying sign language production remains largely unknown. To investigate these dynamics as compared to speech production we utilized intracranial electrocorticography during a battery of language tasks.Methods:We report a unique case of direct cortical surface recordings obtained from a neurosurgical patient with intact hearing and bilingual in English and American Sign Language. We designed a battery of cognitive tasks to capture multiple modalities of language processing and production.Results:We identified two spatially distinct cortical networks: ventral for speech and dorsal for sign production. Sign production recruited peri-rolandic, parietal and posterior temporal regions, while speech production recruited frontal, peri-sylvian and peri-rolandic regions. Electrical cortical stimulation confirmed this spatial segregation, identifying mouth areas for speech production and limb areas for sign production. The temporal dynamics revealed superior parietal cortex activity immediately before sign production, suggesting its role in planning and producing sign language.Conclusions:Our findings reveal a distinct network for sign language and detail the temporal propagation supporting sign production.

Selective attention is the ability to promote the processing of objects important for the accomplishment of our behavioral goals (target objects) over the objects not important to those goals (distractor objects). Previous investigations have shown that the mechanisms of selective attention contribute to enhancing perception in both simple daily tasks and more complex activities requiring learning new information. Recently, it has been verified that selective attention to target objects and distractor objects is separable in the frequency domain, using Logistic Regression (LR) and Support Vector Machines (SVMs) classification. However, discerning dynamics of target and distractor objects in the context of selective attention has not been accomplished yet. This paper extends the investigations on the possible classification and interpretation of distraction and intention solely relying on neural activity (frequency features). In particular, this paper (i) classifies distractor objects vs. target object replicating the LR classification of prior studies, extending the analysis by (ii) interpreting the coefficient weights relating to all features with a focus on N2PC features, and (iii) retrains an LR classifier with the features deemed important by the interpretation analysis. As a result of the interpretation methods, we have successfully decreased the feature size to 7.3 % of total features -i.e., from 19,072 to 1,386 features -while recording only a 0.04 loss in performance accuracy score -i.e., from 0.65 to 0.61. Additionally, the interpretation of the classifiers' coefficient weights unveiled new evidence regarding frequency which has been discussed along with the paper.