There is an uncountable number of different ways of characterizing almost any given real-world stimulus. This necessitates finding stimulus features that are perceptually relevant - that is, they have distinct and independent effects on the perception and cognition of the stimulus. Here, we provide a theoretical framework for empirically testing the perceptual relevance of stimulus features through their association with recognition, memory bias, and æsthetic evaluation. We deploy this framework in the auditory domain to explore the perceptual relevance of three recently developed mathematical characterizations of periodic temporal patterns: balance, evenness, and interonset interval entropy. By modelling recognition responses and liking ratings from 177 participants listening to a total of 1252 different musical rhythms, we obtain very strong evidence that all three features have distinct effects on the memory for, and the liking of, musical rhythms. Interonset interval entropy is a measure of the unpredictability of a rhythm derived from the distribution of its durations. Balance and evenness are both obtained from the discrete Fourier transform (DFT) of periodic patterns represented as points on the unit circle, and we introduce a teleological explanation for their perceptual relevance: the DFT coefficients representing balance and evenness are relatively robust to small random temporal perturbations and hence are coherent in noisy environments. This theory suggests further research to explore the meaning and relevance of robust coefficients such as these to the perception of patterns that are periodic in time and, possibly, space.

中文翻译：

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音乐节奏中平衡、均匀度和熵的感知相关性。

几乎任何给定的现实世界刺激都有无数种不同的方式来表征。这需要找到感知相关的刺激特征——也就是说，它们对刺激的感知和认知具有独特且独立的影响。在这里，我们提供了一个理论框架，用于通过刺激特征与识别、记忆偏差和审美评估的关联来实证测试刺激特征的感知相关性。我们在听觉领域部署了这个框架，以探索三个最近开发的周期性时间模式数学特征的感知相关性：平衡、均匀度和间隔熵。通过对 177 名参与者共听 1252 种不同的音乐节奏的识别反应和喜欢评级进行建模，我们获得了非常有力的证据，表明所有三个特征对音乐节奏的记忆和喜好都有不同的影响。Interonset 间隔熵是对从其持续时间分布得出的节律不可预测性的度量。平衡和均匀度都是从单位圆上的点表示的周期性模式的离散傅立叶变换 (DFT) 中获得的，我们为它们的感知相关性引入了目的论解释：代表平衡和均匀度的 DFT 系数对小随机时间相对稳健扰动，因此在嘈杂的环境中是连贯的。该理论建议进一步研究以探索稳健系数的意义和相关性，例如这些系数对时间和空间周期性模式的感知。