Whereas the energetic and informational masking effects of unintelligible babble on auditory speech recognition are well established, the present study is the first to investigate its effects on visual speech recognition. Young and older adults performed two lipreading tasks while simultaneously experiencing either quiet, speech-shaped noise, or 6-talker background babble. Both words at the end of uninformative carrier sentences and key words in everyday sentences were harder to lipread in the presence of babble than in the presence of speech-shaped noise or quiet. Contrary to the inhibitory deficit hypothesis of cognitive aging, babble had equivalent effects on young and older adults. In a follow-up experiment, neither the babble nor the speech-shaped noise stimuli interfered with performance of a face-processing task, indicating that babble selectively interferes with visual speech recognition and not with visual perception tasks per se. The present results demonstrate that babble can produce cross-modal informational masking and suggest a breakdown in audiovisual scene analysis, either because of obligatory monitoring of even uninformative speech sounds or because of obligatory efforts to integrate speech sounds even with uncorrelated mouth movements.
Background: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports.
Searching for a unique target is faster when its unique feature repeats than when it changes. The standard account for this priming-of-popout (PoP) phenomenon is that selecting a target increases the attentional priority of its features in subsequent searches. However, empirical tests of this priority account have yielded contradictory findings.
The utilization of Electroencephalography (EEG) for emotion recognition has emerged as the primary tool in the field of affective computing. Traditional supervised learning methods are typically constrained by the availability of labeled data, which can result in weak generalizability of learned features. Additionally, EEG signals are highly correlated with human emotional states across temporal, spatial, and spectral dimensions.
We investigated how spatiotemporal neural dynamics underlying perceptual integration changed with the degree of conscious access to a set of backward-masked pacman-shaped inducers that generated the percept of an illusory triangle. We kept the stimulus parameters at a fixed near-threshold level throughout the experiment and recorded electroencephalography from participants who reported the orientation and subjective visibility of the illusory triangle on each trial. Our multivariate pattern analysis revealed that posterior and central areas initially used dynamic neural code and later switched to stable neural code.
Objective: To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict esophageal variceal bleeding.
Materials And Methods: Two hundred twenty-four cirrhotic patients with esophageal variceal bleeding and non-bleeding were included in the retrospective study. Clinical and serum biomarkers were used in our study.
