Institut D'etudes De La Cognition, Ecole Normale Supérieure , Paris, France ; Scientific and Clinical Research Department, Neurelec , Vallauris, France.
Institut D'etudes De La Cognition, Ecole Normale Supérieure , Paris, France ; Sorbonne Universités, UPMC Université Paris 06, UMR_S 968, Institut De La Vision , Paris, F-75012, France ; INSERM, U968 Paris, F-75012, France ; CNRS, UMR_7210 , Paris, F-75012, France.
eNeuro. 2014 Nov 12;1(1). doi: 10.1523/ENEURO.0033-14.2014. eCollection 2014 Nov-Dec.
Musical notes can be ordered from low to high along a perceptual dimension called "pitch". A characteristic property of these sounds is their periodic waveform, and periodicity generally correlates with pitch. Thus, pitch is often described as the perceptual correlate of the periodicity of the sound's waveform. However, the existence and salience of pitch also depends in a complex way on other factors, in particular harmonic content. For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics. Here we examine the theoretical proposition that pitch is the perceptual correlate of the regularity structure of the vibration pattern of the basilar membrane, across place and time-a generalization of the traditional view on pitch. While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments. We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.
音符可以沿着一个称为“音高”的感知维度从低到高排列。这些声音的一个特征属性是它们的周期性波形,而周期性通常与音高相关。因此,音高通常被描述为声音波形周期性的感知相关物。然而,音高的存在和显著程度也以复杂的方式依赖于其他因素,特别是谐波内容。例如,由高次谐波组成的周期性声音往往比由低次谐波组成的声音的音高弱。在这里,我们检验了一个理论假设,即音高是基底膜振动模式的规则结构的感知相关物,跨越位置和时间——这是对传统音高观点的推广。虽然这个假设也将音高归因于周期性声音,但我们表明它预测了可分辨和不可分辨的谐波复合体之间的差异,以及音高存在的复杂域,这与心理物理实验一致。我们还提出了一种基于重合检测的音高估计的可能神经机制,与音高的标准时间模型相比,它不需要长的延迟。