Human hearing is robust to noise, but the basis of this robustness is poorly understood. Several lines of evidence are consistent with the idea that the auditory system adapts to sound components that are stable over time, potentially achieving noise robustness by suppressing noise-like signals. Yet background noise often provides behaviorally relevant information about the environment and thus seems unlikely to be completely discarded by the auditory system. Motivated by this observation, we explored whether noise robustness might instead be mediated by internal models of noise structure that could facilitate the separation of background noise from other sounds. We found that detection, recognition, and localization in real-world background noise were better for foreground sounds positioned later in a noise excerpt, with performance improving over the initial second of exposure to a noise. These results are consistent with both adaptation-based and model-based accounts (adaptation increases over time and online noise estimation should benefit from acquiring more samples). However, performance was also robust to interruptions in the background noise and was enhanced for intermittently recurring backgrounds, neither of which would be expected from known forms of adaptation. Additionally, the performance benefit observed for foreground sounds occurring later within a noise excerpt was reduced for recurring noises, suggesting that a noise representation is built up during exposure to a new background noise and then maintained in memory. These findings suggest that noise robustness is supported by internal models—“noise schemas”—that are rapidly estimated, stored over time, and used to estimate other concurrent sounds.

中文翻译：

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噪声方案有助于在噪声中听觉


人类听觉对噪声很强，但人们对这种稳健性的基础知之甚少。有几条证据与这样的观点是一致的，即听觉系统适应随时间稳定的声音成分，可能通过抑制类似噪声的信号来实现噪声鲁棒性。然而，背景噪音通常提供有关环境的行为相关信息，因此似乎不太可能被听觉系统完全丢弃。在这一观察的推动下，我们探讨了噪声稳健性是否可能由噪声结构的内部模型介导，该模型可以促进背景噪声与其他声音的分离。我们发现，在真实世界背景噪声中检测、识别和定位前景声音对于噪声摘录中稍后放置的前景声音效果更好，在暴露于噪声的最初一秒内性能有所提高。这些结果与基于适应和基于模型的解释一致（适应随着时间的推移而增加，在线噪声估计应该受益于获取更多样本）。然而，该片在背景噪声中断时的表现也很稳健，并且对于间歇性重复的背景也得到了增强，而已知的适应形式则不会期望这两者。此外，对于重复出现的噪声，在噪声摘录中稍后出现的前景声音所观察到的性能优势会降低，这表明噪声表示是在暴露于新的背景噪声期间建立起来的，然后保留在内存中。这些发现表明，噪声稳健性得到了内部模型（“噪声模式”）的支持，这些模型可以快速估计、随着时间的推移存储并用于估计其他并发声音。