The effects of aging and hearing impairment on listening in noise.

The study investigates age-related decline in listening abilities, particularly in noisy environments, where the challenge lies in extracting meaningful information from variable sensory input (figure-ground segregation). The research focuses on peripheral and central factors contributing to this decline using a tone-cloud-based figure detection task. Results based on behavioral measures and event-related brain potentials (ERPs) indicate that, despite delayed perceptual processes and some deterioration in attention and executive functions with aging, the ability to detect sound sources in noise remains relatively intact. However, even mild hearing impairment significantly hampers the segregation of individual sound sources within a complex auditory scene. The severity of the hearing deficit correlates with an increased susceptibility to masking noise. The study underscores the impact of hearing impairment on auditory scene analysis and highlights the need for personalized interventions based on individual abilities.

Auditory learning of recurrent tone sequences is present in the newborn's brain.

The seemingly effortless ability of our auditory system to rapidly detect new events in a dynamic environment is crucial for survival. Whether the underlying brain processes are innate is unknown. To answer this question, electroencephalography was recorded while regularly patterned (REG) versus random (RAND) tone sequences were presented to sleeping neonates. Regular relative to random sequences elicited differential neural responses after only a single repetition of the pattern indicating the existence of an innate capacity of the auditory system to detect auditory sequential regularities. We show that the newborn auditory system accumulates evidence only somewhat longer than the minimum amount determined by the ideal Bayesian observer model (the prediction from a variable-order Markov chain model) before detecting a repeating pattern. Thus, newborns can quickly form representations for regular features of the sound input, preparing the way for learning the contingencies of the environment.