Natural sounds contain rich patterns of amplitude modulation (AM), which is one of the essential sound dimensions for auditory perception. The sensitivity of human hearing to AM measured by psychophysics takes diverse forms depending on the experimental conditions. Here, we address with a single framework the questions of why such patterns of AM sensitivity have emerged in the human auditory system and how they are realized by our neural mechanisms. Assuming that optimization for natural sound recognition has taken place during human evolution and development, we examined its effect on the formation of AM sensitivity by optimizing a computational model, specifically, a multilayer neural network, for natural sound (namely, everyday sounds and speech sounds) recognition and simulating psychophysical experiments in which the AM sensitivity of the model was assessed. Relatively higher layers in the model optimized to sounds with natural AM statistics exhibited AM sensitivity similar to that of humans, although the model was not designed to reproduce human-like AM sensitivity. Moreover, simulated neurophysiological experiments on the model revealed a correspondence between the model layers and the auditory brain regions. The layers in which human-like psychophysical AM sensitivity emerged exhibited substantial neurophysiological similarity with the auditory midbrain and higher regions. These results suggest that human behavioral AM sensitivity has emerged as a result of optimization for natural sound recognition in the course of our evolution and/or development and that it is based on a stimulus representation encoded in the neural firing rates in the auditory midbrain and higher regions.

SIGNIFICANCE STATEMENT This study provides a computational paradigm to bridge the gap between the behavioral properties of human sensory systems as measured in psychophysics and neural representations as measured in nonhuman neurophysiology. This was accomplished by combining the knowledge and techniques in psychophysics, neurophysiology, and machine learning. As a specific target modality, we focused on the auditory sensitivity to sound AM. We built an artificial neural network model that performs natural sound recognition and simulated psychophysical and neurophysiological experiments in the model. Quantitative comparison of a machine learning model with human and nonhuman data made it possible to integrate the knowledge of behavioral AM sensitivity and neural AM tunings from the perspective of optimization to natural sound recognition.

自然声音包含丰富的幅度调制（AM）模式，这是听觉感知的基本声音维度之一。通过心理物理学测量的人类听觉对 AM 的敏感度根据实验条件的不同而呈现不同的形式。在这里，我们用一个单一的框架来解决为什么人类听觉系统中会出现这种 AM 敏感性模式以及它们是如何通过我们的神经机制实现的问题。假设在人类进化和发展过程中已经发生了自然声音识别的优化，我们通过优化自然声音的计算模型，特别是多层神经网络（即，日常声音和语音）识别和模拟心理物理实验，其中评估模型的 AM 敏感性。模型中相对较高的层针对具有自然 AM 统计数据的声音进行了优化，表现出与人类相似的 AM 灵敏度，尽管该模型并非旨在重现类似人类的 AM 灵敏度。此外，对该模型的模拟神经生理学实验揭示了模型层和听觉大脑区域之间的对应关系。出现类似人类心理物理 AM 敏感性的层与听觉中脑和更高区域表现出显着的神经生理学相似性。

意义声明这项研究提供了一种计算范式，以弥合心理物理学中测量的人类感觉系统的行为特性与非人类神经生理学中测量的神经表征之间的差距。这是通过结合心理物理学、神经生理学和机器学习的知识和技术来实现的。作为特定的目标模态，我们重点关注对声音调幅的听觉敏感性。我们建立了一个人工神经网络模型，可以执行自然声音识别，并在模型中模拟心理物理和神经生理学实验。将机器学习模型与人类和非人类数据进行定量比较，可以从优化的角度整合行为 AM 敏感性和神经 AM 调优的知识到自然声音识别。