Department of Speech, Hearing and Phonetic Sciences, University College London, London, WC1N 1PF, UK.
Department of Psychology, Royal Holloway, University of London, Egham, TW20 0EX, UK.
Nat Commun. 2019 Jun 3;10(1):2404. doi: 10.1038/s41467-019-10295-w.
Models of voice perception propose that identities are encoded relative to an abstracted average or prototype. While there is some evidence for norm-based coding when learning to discriminate different voices, little is known about how the representation of an individual's voice identity is formed through variable exposure to that voice. In two experiments, we show evidence that participants form abstracted representations of individual voice identities based on averages, despite having never been exposed to these averages during learning. We created 3 perceptually distinct voice identities, fully controlling their within-person variability. Listeners first learned to recognise these identities based on ring-shaped distributions located around the perimeter of within-person voice spaces - crucially, these distributions were missing their centres. At test, listeners' accuracy for old/new judgements was higher for stimuli located on an untrained distribution nested around the centre of each ring-shaped distribution compared to stimuli on the trained ring-shaped distribution.
语音感知模型提出，身份是相对于抽象的平均值或原型进行编码的。虽然在学习辨别不同声音时，有一些基于规范的编码证据，但对于个体声音身份的表示是如何通过对该声音的可变暴露形成的，知之甚少。在两项实验中，我们证明了参与者基于平均值形成个体声音身份的抽象表示，尽管在学习过程中从未接触过这些平均值。我们创建了 3 个感知上不同的声音身份，完全控制了它们的个体内可变性。听众首先根据位于个体声音空间周边的环形分布来学习识别这些身份 - 至关重要的是，这些分布缺失了它们的中心。在测试中，与训练有素的环形分布上的刺激相比，位于每个环形分布中心周围嵌套的未训练分布上的刺激，对于新旧判断的听众准确性更高。
