Repetition detection and rapid auditory learning for stochastic tone clouds.

Abstract

Stochastic sounds are useful to probe auditory memory, as they require listeners to learn unpredictable and novel patterns under controlled experimental conditions. Previous studies using white noise or random click trains have demonstrated rapid auditory learning. Here, we explored perceptual learning with a more parametrically variable stimulus. These "tone clouds" were defined as broadband combinations of tone pips at randomized frequencies and onset times. Varying the number of tones covered a perceptual range from individually audible pips to noise-like stimuli. Results showed that listeners could detect and learn repeating patterns in tone clouds. Task difficulty varied depending on the density of tone pips, with sparse tone clouds the easiest. Rapid learning of individual tone clouds was observed for all densities, with a roughly constant benefit of learning irrespective of baseline performance. Variations in task difficulty were correlated to amplitude modulations in an auditory model. Tone clouds thus provide a tool to probe auditory learning in a variety of task-difficulty settings, which could be useful for clinical or neurophysiological studies. They also show that rapid auditory learning operates over a wide range of spectrotemporal complexity, essentially from melodies to noise.