Towards the end of the second trimester of gestation, a human fetus is able to register environmental sounds. This in utero auditory experience is characterized by comprising strongly low-pass-filtered versions of sounds from the external world. Here, we present computational tests of the hypothesis that this early exposure to severely degraded auditory inputs serves an adaptive purpose—it may induce the neural development of extended temporal integration. Such integration can facilitate the detection of information carried by low-frequency variations in the auditory signal, including emotional or other prosodic content. To test this prediction, we characterized the impact of several training regimens, biomimetic and otherwise, on a computational model system trained and tested on the task of emotion recognition. We find that training with an auditory trajectory recapitulating that of a neurotypical infant in the pre-to-postnatal period results in temporally extended receptive field structures and yields the best subsequent accuracy and generalization performance on the task of emotion recognition. This strongly suggests that the progression from low-pass-filtered to full-frequency inputs is likely to be an adaptive feature of our development, conferring significant benefits to later auditory processing abilities relying on temporally extended analyses. Additionally, this finding can help explain some of the auditory impairments associated with preterm births, suggests guidelines for the design of auditory environments in neonatal care units, and points to enhanced training procedures for computational models.

中文翻译：

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产前听觉体验及其后遗症

在妊娠中期末期，人类胎儿能够识别环境声音。这种在子宫内的听觉体验的特点是包括来自外部世界的声音的强烈低通滤波版本。在这里，我们提出了假设的计算测试，即这种早期暴露于严重退化的听觉输入服务于适应性目的——它可能诱导扩展时间整合的神经发展。这种集成可以促进检测由听觉信号中的低频变化携带的信息，包括情绪或其他韵律内容。为了测试这一预测，我们描述了几种训练方案、仿生和其他训练方案对在情绪识别任务上训练和测试的计算模型系统的影响。我们发现，在产前到产后期间，使用听觉轨迹重现神经典型婴儿的听觉轨迹的训练会导致时间上扩展的感受野结构，并在情绪识别任务中产生最佳的后续准确性和泛化性能。这有力地表明，从低通滤波到全频输入的进展可能是我们发展的适应性特征，为依赖于时间扩展分析的后期听觉处理能力带来重大好处。此外，这一发现有助于解释与早产相关的一些听觉障碍，为新生儿护理病房的听觉环境设计提供指导方针，并指出加强计算模型的训练程序。