The Influence of Categories on Perception: Explaining the Perceptual Magnet Effect as Optimal Statistical Inference
The Influence of Categories on Perception: Explaining the Perceptual Magnet Effect as Optimal Statistical Inference
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DOI：
10.1037/a0017196
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发表时间：
2009-10-01
影响因子：
5.4
通讯作者：
Morgan, James L.
中科院分区：
文献类型：
--
作者：
Feldman, Naomi H.;Griffiths, Thomas L.;Morgan, James L.
各种研究表明，将刺激分类可以影响感知刺激的方式。我们通过一种现象（感知磁体效应）探索类别对感知的影响，其中元音之间的可辨别性在接近原型元音声音时降低。我们提出了一个贝叶斯模型来解释为什么会出现这种可辨别性降低的情况：它是最优解决噪声中感知的统计问题的结果。在这个问题的最佳解决方案中，听众的感知偏向于语音类别手段，因为他们使用这些类别的知识来指导他们对说话者的目标作品的推断。模拟表明，模型预测与之前发布的人类数据密切相关，新颖的实验结果为感知扭曲和噪声之间的预测联系提供了证据。该模型统一了先前对感知磁体效应的几种解释，并为探索其他领域的分类效应提供了一个框架。
A variety of studies have demonstrated that organizing stimuli into categories can affect the way the stimuli are perceived. We explore the influence of categories on perception through one such phenomenon, the perceptual magnet effect, in which discriminability between vowels is reduced near prototypical vowel sounds. We present a Bayesian model to explain why this reduced discriminability might occur: It arises as a consequence of optimally solving the statistical problem of perception in noise. In the optimal solution to this problem, listeners' perception is biased toward phonetic category means because they use knowledge of these categories to guide their inferences about speakers' target productions. Simulations show that model predictions closely correspond to previously published human data, and novel experimental results provide evidence for the predicted link between perceptual warping and noise. The model unifies several previous accounts of the perceptual magnet effect and provides a framework for exploring categorical effects in other domains.