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A test of model classes accounting for individual differences in the cocktail-party effect
The Journal of the Acoustical Society of America ( IF 2.4 ) Pub Date : 2020-12-30 , DOI: 10.1121/10.0002961
Robert A Lutfi 1 , Briana Rodriguez 1 , Jungmee Lee 1 , Torben Pastore 2
Affiliation  

Listeners differ widely in the ability to follow the speech of a single talker in a noisy crowd—what is called the cocktail-party effect. Differences may arise for any one or a combination of factors associated with auditory sensitivity, selective attention, working memory, and decision making required for effective listening. The present study attempts to narrow the possibilities by grouping explanations into model classes based on model predictions for the types of errors that distinguish better from poorer performing listeners in a vowel segregation and talker identification task. Two model classes are considered: those for which the errors are predictably tied to the voice variation of talkers (decision weight models) and those for which the errors occur largely independently of this variation (internal noise models). Regression analyses of trial-by-trial responses, for different tasks and task demands, show overwhelmingly that the latter type of error is responsible for the performance differences among listeners. The results are inconsistent with models that attribute the performance differences to differences in the reliance listeners place on relevant voice features in this decision. The results are consistent instead with models for which largely stimulus-independent, stochastic processes cause information loss at different stages of auditory processing.

中文翻译:

考虑鸡尾酒会效应个体差异的模型类测试

听众在嘈杂的人群中听听单个讲话者讲话的能力差异很大——这就是所谓的鸡尾酒会效应。与听觉敏感性、选择性注意、工作记忆和有效聆听所需的决策相关的任何一个或多个因素的组合可能会出现差异。本研究试图通过基于对错误类型的模型预测将解释分组到模型类中来缩小可能性,这些错误类型在元音分离和说话者识别任务中更好地与表现较差的听众区分开来。考虑了两个模型类:那些错误可预测地与说话者的语音变化相关的那些(决策权重模型)以及那些错误发生在很大程度上独立于这种变化的那些(内部噪声模型)。对不同任务和任务需求的逐次试验响应的回归分析,压倒性地表明后一种错误是造成听众之间表现差异的原因。结果与模型不一致,这些模型将性能差异归因于听众对本决策中相关语音特征的依赖程度的差异。结果与在很大程度上独立于刺激的随机过程在听觉处理的不同阶段导致信息丢失的模型一致。
更新日期:2020-12-30
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