Centre for Neuroscience in Education, University of Cambridge, Cambridge, United Kingdom.
International Research Center for Neurointelligence, The University of Tokyo, Bunkyo City, Tokyo, Japan.
PLoS One. 2022 Oct 14;17(10):e0275631. doi: 10.1371/journal.pone.0275631. eCollection 2022.
Statistical learning of physical stimulus characteristics is important for the development of cognitive systems like language and music. Rhythm patterns are a core component of both systems, and rhythm is key to language acquisition by infants. Accordingly, the physical stimulus characteristics that yield speech rhythm in "Babytalk" may also describe the hierarchical rhythmic relationships that characterize human music and song. Computational modelling of the amplitude envelope of "Babytalk" (infant-directed speech, IDS) using a demodulation approach (Spectral-Amplitude Modulation Phase Hierarchy model, S-AMPH) can describe these characteristics. S-AMPH modelling of Babytalk has shown previously that bands of amplitude modulations (AMs) at different temporal rates and their phase relations help to create its structured inherent rhythms. Additionally, S-AMPH modelling of children's nursery rhymes shows that different rhythm patterns (trochaic, iambic, dactylic) depend on the phase relations between AM bands centred on ~2 Hz and ~5 Hz. The importance of these AM phase relations was confirmed via a second demodulation approach (PAD, Probabilistic Amplitude Demodulation). Here we apply both S-AMPH and PAD to demodulate the amplitude envelopes of Western musical genres and songs. Quasi-rhythmic and non-human sounds found in nature (birdsong, rain, wind) were utilized for control analyses. We expected that the physical stimulus characteristics in human music and song from an AM perspective would match those of IDS. Given prior speech-based analyses, we also expected that AM cycles derived from the modelling may identify musical units like crotchets, quavers and demi-quavers. Both models revealed an hierarchically-nested AM modulation structure for music and song, but not nature sounds. This AM modulation structure for music and song matched IDS. Both models also generated systematic AM cycles yielding musical units like crotchets and quavers. Both music and language are created by humans and shaped by culture. Acoustic rhythm in IDS and music appears to depend on many of the same physical characteristics, facilitating learning.
物理刺激特征的统计学习对于语言和音乐等认知系统的发展非常重要。节奏模式是这两个系统的核心组成部分，而节奏是婴儿语言习得的关键。因此，“婴儿语”（婴儿指向的语言，IDS）中产生语音节奏的物理刺激特征也可能描述了人类音乐和歌曲的分层节奏关系。使用解调方法（频谱-幅度调制相位层次模型，S-AMPH）对“婴儿语”（婴儿指向的语言，IDS）的幅度包络进行计算建模，可以描述这些特征。S-AMPH 对婴儿语的建模表明，不同时间速率的幅度调制（AMs）带及其相位关系有助于创造其结构化的固有节奏。此外，S-AMPH 对儿童童谣的建模表明，不同的节奏模式（重音，抑扬格，扬抑格）取决于以2Hz 和5Hz 为中心的 AM 带之间的相位关系。通过第二种解调方法（PAD，概率幅度解调）证实了这些 AM 相位关系的重要性。在这里，我们应用 S-AMPH 和 PAD 对西方音乐流派和歌曲的幅度包络进行解调。自然界中发现的准节奏和非人类声音（鸟鸣、雨声、风声）被用作控制分析。我们期望从 AM 的角度来看，人类音乐和歌曲中的物理刺激特征将与 IDS 的特征相匹配。鉴于之前基于语音的分析，我们还期望建模得出的 AM 周期可以识别出像四分音符、八分音符和二分音符这样的音乐单位。两种模型都揭示了音乐和歌曲的层次嵌套 AM 调制结构，但自然界的声音则没有。这种音乐和歌曲的 AM 调制结构与 IDS 相匹配。两种模型都生成了系统的 AM 周期，产生了像四分音符和八分音符这样的音乐单位。语言和音乐都是由人类创造的，并受到文化的影响。IDS 和音乐中的声学节奏似乎取决于许多相同的物理特征，这有助于学习。
