Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression.

Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model.

A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.

Background: Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results.

Human ventromedial prefrontal cortex lesions enhance the effect of expectations on pain perception.

Pain is strongly modulated by expectations and beliefs. Across species, subregions of ventromedial prefrontal cortex (VMPFC) are implicated in a variety of functions germane to pain, predictions, and learning. Human fMRI studies show that VMPFC activity tracks expectations about pain and mediates expectancy effects on pain-related activity in other brain regions.

How Pain-Related Facial Expressions Are Evaluated in Relation to Gender, Race, and Emotion.

Unlabelled: Inequities in pain assessment are well-documented; however, the psychological mechanisms underlying such biases are poorly understood. We investigated potential perceptual biases in the judgments of faces displaying pain-related movements. Across five online studies, 956 adult participants viewed images of computer-generated faces ("targets") that varied in features related to race (Black and White) and gender (women and men).