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Efficient Coding and Statistically Optimal Weighting of Covariance among Acoustic Attributes in Novel Sounds

机译:新型声音中声音属性之间协方差的有效编码和统计最优权重

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摘要

To the extent that sensorineural systems are efficient, redundancy should be extracted to optimize transmission of information, but perceptual evidence for this has been limited. Stilp and colleagues recently reported efficient coding of robust correlation (r = .97) among complex acoustic attributes (attack/decay, spectral shape) in novel sounds. Discrimination of sounds orthogonal to the correlation was initially inferior but later comparable to that of sounds obeying the correlation. These effects were attenuated for less-correlated stimuli (r = .54) for reasons that are unclear. Here, statistical properties of correlation among acoustic attributes essential for perceptual organization are investigated. Overall, simple strength of the principal correlation is inadequate to predict listener performance. Initial superiority of discrimination for statistically consistent sound pairs was relatively insensitive to decreased physical acoustic/psychoacoustic range of evidence supporting the correlation, and to more frequent presentations of the same orthogonal test pairs. However, increased range supporting an orthogonal dimension has substantial effects upon perceptual organization. Connectionist simulations and Eigenvalues from closed-form calculations of principal components analysis (PCA) reveal that perceptual organization is near-optimally weighted to shared versus unshared covariance in experienced sound distributions. Implications of reduced perceptual dimensionality for speech perception and plausible neural substrates are discussed.
机译:就感觉神经系统的效率而言,应提取冗余以优化信息的传输,但对此的感知证据有限。 Stilp及其同事最近报道了新颖声音中复杂声学属性(攻击/衰减,频谱形状)之间鲁棒相关性(r = 0.97)的有效编码。正交于相关的声音的判别最初较差,但后来与遵循相关的声音的判别相当。由于不清楚的原因,这些影响因不那么相关的刺激而减弱(r = 0.54)。在此,研究对于感知组织必不可少的声学属性之间的相关性的统计特性。总体而言,主要相关性的简单强度不足以预测收听者的表现。对于统计上一致的声音对,判别的最初优势对支持相关性的物理声学/心理声学范围的减小以及对相同正交测试对的更频繁的呈现相对不敏感。但是,增加的范围支持正交维度会对感知组织产生实质性影响。从主成分分析(PCA)的闭式计算得出的连接主义模拟和特征值表明,在经验丰富的声音分布中,感知组织的权重接近最优,分别为共享协方差和非共享协方差。讨论了减小的感知维数对语音感知和可能的神经基质的影响。

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