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.

中文翻译：

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随机音云的重复检测和快速听觉学习


随机声音对于探索听觉记忆非常有用，因为它们要求听众在受控实验条件下学习不可预测的新颖模式。先前使用白噪声或随机点击序列的研究已经证明了快速的听觉学习。在这里，我们探索了具有更多参数可变刺激的感知学习。这些“音调云”被定义为随机频率和起始时间的音调尖峰的宽带组合。改变音调的数量涵盖了从单独可听见的小音到类似噪音的刺激的感知范围。结果表明，听众可以检测并学习音云中的重复模式。任务难度根据音点的密度而变化，稀疏的音云最容易。在所有密度下都观察到单个色调云的快速学习，无论基线性能如何，学习的好处大致恒定。任务难度的变化与听觉模型中的幅度调制相关。因此，音调云提供了一种在各种任务难度设置中探索听觉学习的工具，这对于临床或神经生理学研究可能有用。他们还表明，快速听觉学习在广泛的频谱时间复杂性上发挥作用，主要是从旋律到噪音。