Pearce Marcus T, Müllensiefen Daniel, Wiggins Geraint A
Centre for Cognition, Computation and Culture, Goldsmiths, University of London, London SE14 6NW, UK.
Perception. 2010;39(10):1365-89. doi: 10.1068/p6507.
Grouping and boundary perception are central to many aspects of sensory processing in cognition. We present a comparative study of recently published computational models of boundary perception in music. In doing so, we make three contributions. First, we hypothesise a relationship between expectation and grouping in auditory perception, and introduce a novel information-theoretic model of perceptual segmentation to test the hypothesis. Although we apply the model to musical melody, it is applicable in principle to sequential grouping in other areas of cognition. Second, we address a methodological consideration in the analysis of ambiguous stimuli that produce different percepts between individuals. We propose and demonstrate a solution to this problem, based on clustering of participants prior to analysis. Third, we conduct the first comparative analysis of probabilistic-learning and rule-based models of perceptual grouping in music. In spite of having only unsupervised exposure to music, the model performs comparably to rule-based models based on expert musical knowledge, supporting a role for probabilistic learning in perceptual segmentation of music.
分组和边界感知是认知中感觉处理许多方面的核心。我们对最近发表的音乐中边界感知的计算模型进行了一项比较研究。在此过程中，我们做出了三点贡献。第一，我们假设听觉感知中期望与分组之间存在一种关系，并引入一种新颖的信息理论感知分割模型来检验这一假设。尽管我们将该模型应用于音乐旋律，但原则上它适用于认知其他领域的序列分组。第二，我们解决了分析在个体之间产生不同感知的模糊刺激时的一个方法考量。我们提出并演示了一个基于分析前对参与者进行聚类的该问题解决方案。第三，我们对音乐中感知分组的概率学习模型和基于规则的模型进行了首次比较分析。尽管该模型仅无监督地接触音乐，但它的表现与基于专业音乐知识的基于规则的模型相当，这支持了概率学习在音乐感知分割中的作用。
