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音调序列频率变化的辨别

Discrimination of frequency variance for tonal sequences.

作者信息

Byrne Andrew J, Viemeister Neal F, Stellmack Mark A

机构信息

Department of Psychology, University of Minnesota, Minneapolis, Minnesota 55455.

出版信息

J Acoust Soc Am. 2014 Dec;136(6):3172. doi: 10.1121/1.4900825.

Abstract

Real-world auditory stimuli are highly variable across occurrences and sources. The present study examined the sensitivity of human listeners to differences in global stimulus variability. In a two-interval, forced-choice task, variance discrimination was measured using sequences of five 100-ms tone pulses. The frequency of each pulse was sampled randomly from a distribution that was Gaussian in logarithmic frequency. In the non-signal interval, the sampled distribution had a variance of σSTAN (2), while in the signal interval, the variance of the sequence was σSIG (2) (with σSIG (2) >  σSTAN (2)). The listener's task was to choose the interval with the larger variance. To constrain possible decision strategies, the mean frequency of the sampling distribution of each interval was randomly chosen for each presentation. Psychometric functions were measured for various values of σSTAN (2). Although the performance was remarkably similar across listeners, overall performance was poorer than that of an ideal observer (IO) which perfectly compares interval variances. However, like the IO, Weber's Law behavior was observed, with a constant ratio of ( σSIG (2)- σSTAN (2)) to σSTAN (2) yielding similar performance. A model which degraded the IO with a frequency-resolution noise and a computational noise provided a reasonable fit to the real data.

摘要

现实世界中的听觉刺激在不同的出现情况和来源之间具有高度的变异性。本研究考察了人类听众对整体刺激变异性差异的敏感度。在一个双间隔的迫选任务中,使用由五个100毫秒音调脉冲组成的序列来测量方差辨别。每个脉冲的频率从对数频率呈高斯分布中随机采样。在非信号间隔中,采样分布的方差为σSTAN(2),而在信号间隔中,序列的方差为σSIG(2)(其中σSIG(2)>σSTAN(2))。听众的任务是选择方差较大的间隔。为了限制可能的决策策略,每次呈现时每个间隔的采样分布的平均频率是随机选择的。针对σSTAN(2)的各种值测量了心理测量函数。尽管不同听众的表现非常相似,但总体表现比理想观察者(IO)差,理想观察者能完美地比较间隔方差。然而与理想观察者一样,观察到了韦伯定律行为,(σSIG(2)-σSTAN(2))与σSTAN(2)的恒定比率产生了相似的表现。一个用频率分辨率噪声和计算噪声使理想观察者退化的模型能够合理地拟合实际数据。

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