Topic: pink noise

We investigated whether transformer-based machine learning (ML) models could have similar behaviors to humans and predict electrophysiological data in audiovisual speech recognition tasks. Human audiovisual speech recognition follows principles of Bayes-optimal integration, with each modality weighted by its reliability (Author et al., 2009). To determine whether ML models of audiovisual speech recognition follow similar Bayesian principles, we applied the technique used to estimate modality weights: assessing recognition of stimuli containing a mismatch between modalities (Author \& Author, 2

Background: Age-related sleep architecture alterations include fewer N2 sleep spindles, shorter N3 duration, and Slow Waves. This is linked to age-related decline in cognition. Auditory stimulation (AS) improves sleep oscillations. Aim: This study examines how open-loop AS impacts spindles and SW in young (YNG) and older (OLD) individuals during N2 and N3. Methods: A nap polysomnography was conducted on two healthy adult groups: YNG (n=8, 19–34 years) and OLD (n=6, 50–65 years). In N2 and N3 stages, a 20s AS was given, with 2s of 13.75 Hz modulated pink noise, 8s of constant pink noise, and 10