Shared computational principles for language processing in humans and deep language models

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

Reconstructing Speech from Human Auditory Cortex

Direct brain recordings from neurosurgical patients listening to speech reveal that the acoustic speech signals can be reconstructed from neural activity in auditory cortex

Field Performance of Reinforced Dunes for Improving Coastal Resilience

Increased coastal erosion rates have forced communities to rethink how to manage vulnerable coastlines. In many locations there is a trend towards implementing temporary engineering solutions, such as geotextile sand containers (GSCs) and geo-tubes, to stabilize erosion hot spots and assess the impact of these designs while long-term solutions are developed. GSCs and geo-tubes have the potential to increase the resilience of natural systems to protect coastlines from smaller storm events (e.g. 25-year storms) while providing flexibility in design considering the uncertainty regarding future rates of sea level rise and storm frequency. The objective of this paper is to summarize the performance of geotextile stabilized coastal sites and present results of on-going field studies to assess the performance of GSC reinforced dunes in Montauk, NY. The better-than-expected, resilient performance of GSCs and geo-tubes at most locations and recent reinforcement of dunes, bluffs, and shorelines in New York, Massachusetts, and Hawaii emphasizes the need for continued field research and in situ monitoring to collect high-quality performance data to better evaluate laboratory experiments and numerical models developed to predict the hydraulic stability of these systems

Human cortical sensorimotor network underlying feedback control of vocal pitch

The control of vocalization is critically dependent on auditory feedback. Here, we determined the human peri-Sylvian speech network that mediates feedback control of pitch using direct cortical recordings. Subjects phonated while a real-time signal processor briefly perturbed their output pitch (speak condition). Subjects later heard the same recordings of their auditory feedback (listen condition). In posterior superior temporal gyrus, a proportion of sites had suppressed responses to normal feedback, whereas other spatially independent sites had enhanced responses to altered feedback. Behaviorally, speakers compensated for perturbations by changing their pitch. Single-trial analyses revealed that compensatory vocal changes were predicted by the magnitude of both auditory and subsequent ventral premotor responses to perturbations. Furthermore, sites whose responses to perturbation were enhanced in the speaking condition exhibited stronger correlations with behavior. This sensorimotor cortical network appears to underlie auditory feedback-based control of vocal pitch in humans