Département d'études cognitives, Ecole Normale Supérieure, Paris, France.
Adv Exp Med Biol. 2013;787:443-51. doi: 10.1007/978-1-4614-1590-9_49.
Human listeners seem to be remarkably able to recognise acoustic sound sources based on timbre cues. Here we describe a psychophysical paradigm to estimate the time it takes to recognise a set of complex sounds differing only in timbre cues: both in terms of the minimum duration of the sounds and the inferred neural processing time. Listeners had to respond to the human voice while ignoring a set of distractors. All sounds were recorded from natural sources over the same pitch range and equalised to the same duration and power. In a first experiment, stimuli were gated in time with a raised-cosine window of variable duration and random onset time. A voice/non-voice (yes/no) task was used. Performance, as measured by d', remained above chance for the shortest sounds tested (2 ms); d's above 1 were observed for durations longer than or equal to 8 ms. Then, we constructed sequences of short sounds presented in rapid succession. Listeners were asked to report the presence of a single voice token that could occur at a random position within the sequence. This method is analogous to the "rapid sequential visual presentation" paradigm (RSVP), which has been used to evaluate neural processing time for images. For 500-ms sequences made of 32-ms and 16-ms sounds, d' remained above chance for presentation rates of up to 30 sounds per second. There was no effect of the pitch relation between successive sounds: identical for all sounds in the sequence or random for each sound. This implies that the task was not determined by streaming or forward masking, as both phenomena would predict better performance for the random pitch condition. Overall, the recognition of familiar sound categories such as the voice seems to be surprisingly fast, both in terms of the acoustic duration required and of the underlying neural time constants.
人类听众似乎能够非常出色地根据音色线索识别声音声源。在这里，我们描述了一种心理物理学范式，用于估计识别一组仅在音色线索上有所不同的复杂声音所需的时间：包括声音的最短持续时间和推断的神经处理时间。听众必须在忽略一组干扰物的情况下对人声做出反应。所有声音均由天然声源录制，具有相同的音域和相同的时长和强度。在第一个实验中，刺激随时间门控，窗口时长可变，起始时间随机。使用语音/非语音（是/否）任务。以 d' 表示的性能对于测试的最短声音（2 毫秒）仍高于随机水平；对于时长大于或等于 8 毫秒的声音，d' 值大于 1。然后，我们构建了由短声音快速连续呈现的序列。要求听众报告在序列中随机位置可能出现的单个语音令牌的存在。这种方法类似于“快速顺序视觉呈现”（RSVP）范式，该范式已用于评估图像的神经处理时间。对于由 32 毫秒和 16 毫秒声音组成的 500 毫秒序列，以每秒 30 个声音的速度呈现时，d' 值仍高于随机水平。连续声音之间的音高关系没有影响：序列中的所有声音相同或每个声音随机。这意味着该任务不是由流或前向掩蔽决定的，因为这两种现象都预测随机音高条件下的表现更好。总体而言，熟悉的声音类别（如人声）的识别速度非常快，无论是在所需的声学时长还是在潜在的神经时标上都是如此。
