Learning to Detect Auditory Signals in Noise: Active Top-Down Selection and Stable Change in Signal Representations.

Training can improve detection of auditory signals in noise. This learning could potentially occur through active top-down selection mechanisms or stable changes in signal representations. Here, participants were trained and tested (pretest vs. posttest design) on abilities to detect pure tone signals in noise. Auditory evoked potentials (AEPs) to tones were gathered under dichotic listening conditions where participants attended to nontonal stimuli in the opposite ear. Improvements in detection sensitivity were observable regardless of tested tone frequency. This was true in generalization between 861 Hz and 1058-Hz tones (Experiment 1a), and when testing a frequency range >1 octave (Experiment 2). Such learning was not apparent without training (Experiment 1b). In contrast to behavior, AEP amplitude increases from pre- to posttest were partially specific to trained tone frequencies, even when selective attention was diverted to the opposite ear of tone presentation. Placed in the context of previous work, results suggest that changes in active top-down selection mechanisms and stable signal representations both play a role in auditory detection learning. The mismatch between AEP and behavioral effects suggests a need to consider how these different learning processes can impact detection performance in the variety of listening scenarios a listener may face. Public Significance Statement: This study suggests that learning to detect sounds in noise involves multiple processes. This potentially includes changes in top-down selection and stable plasticity in neural representations of experienced sounds. [ABSTRACT FROM AUTHOR]