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Perceptual learning of multiple talkers requires additional exposure
Attention, Perception, & Psychophysics ( IF 1.7 ) Pub Date : 2021-03-22 , DOI: 10.3758/s13414-021-02261-w
Sahil Luthra 1, 2 , Hannah Mechtenberg 1 , Emily B Myers 1, 2, 3
Affiliation  

Because different talkers produce their speech sounds differently, listeners benefit from maintaining distinct generative models (sets of beliefs) about the correspondence between acoustic information and phonetic categories for different talkers. A robust literature on phonetic recalibration indicates that when listeners encounter a talker who produces their speech sounds idiosyncratically (e.g., a talker who produces their /s/ sound atypically), they can update their generative model for that talker. Such recalibration has been shown to occur in a relatively talker-specific way. Because listeners in ecological situations often meet several new talkers at once, the present study considered how the process of simultaneously updating two distinct generative models compares to updating one model at a time. Listeners were exposed to two talkers, one who produced /s/ atypically and one who produced /∫/ atypically. Critically, these talkers only produced these sounds in contexts where lexical information disambiguated the phoneme’s identity (e.g., epi_ode, flouri_ing). When initial exposure to the two talkers was blocked by voice (Experiment 1), listeners recalibrated to these talkers after relatively little exposure to each talker (32 instances per talker, of which 16 contained ambiguous fricatives). However, when the talkers were intermixed during learning (Experiment 2), listeners required more exposure trials before they were able to adapt to the idiosyncratic productions of these talkers (64 instances per talker, of which 32 contained ambiguous fricatives). Results suggest that there is a perceptual cost to simultaneously updating multiple distinct generative models, potentially because listeners must first select which generative model to update.



中文翻译:

多个说话者的感知学习需要额外的接触

由于不同的说话者发出的语音不同,因此听众可以从维护关于不同说话者的声学信息和语音类别之间的对应关系的不同生成模型(信念集)中受益。关于语音重新校准的可靠文献表明,当听众遇到一个发出特殊语音的说话者(例如,发出非典型声音的说话者)时,他们可以更新该说话者的生成模型。这种重新校准已被证明是以相对特定于说话者的方式发生的。由于生态环境中的听众经常会同时遇到几个新的谈话者,因此本研究考虑了同时更新两个不同的生成模型的过程与一次更新一个模型的过程有何不同。听众接触到两名讲话者,一名非典型地发出/s/,另一名非典型地发出/∫/。至关重要的是,这些说话者仅在词汇信息消除音素身份歧义的情况下发出这些声音(例如,epi_odeflori_ing)。当最初接触两个说话者的声音被声音阻挡时(实验 1),听众在相对较少地接触每个说话者后重新校准这些说话者(每个说话者 32 个实例,其中 16 个包含不明确的摩擦音)。然而,当说话者在学习过程中混合在一起时(实验 2),听众需要更多的接触试验才能适应这些说话者独特的作品(每个说话者 64 个实例,其中 32 个包含不明确的摩擦音)。结果表明,同时更新多个不同的生成模型会产生感知成本,这可能是因为听众必须首先选择要更新的生成模型。

更新日期:2021-03-23
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