Despite burgeoning evidence that listeners are highly sensitive to statistical distributions of speech cues, the mechanism underlying learning may not be purely statistical tracking. Decades of research in animal learning suggest that learning results from prediction and prediction error. Two artificial language learning experiments test two predictions that distinguish error-driven from purely statistical models; namely, cue competition - specifically, Kamin's (1968) 'blocking' effect (Experiment 1) - and the predictive structure of learning events (Experiment 2). In Experiment 1, prior knowledge of an informative cue blocked learning of a second cue. This finding may help explain second language learners' difficulty in acquiring native-level perception of non-native speech cues. In Experiment 2, learning was better with a discriminative (cue-outcome) order compared to a non-discriminative (outcome-cue) order. Experiment 2 suggests that learning speech cues, including reversing effects of blocking, depends on (un)learning from prediction error and depends on the temporal order of auditory cues versus semantic outcomes. Together, these results show that (a) existing knowledge of acoustic cues can block later learning of new cues, and (b) speech sound acquisition depends on the predictive structure of learning events. When feedback from prediction error is available, this drives learners to ignore salient non-discriminative cues and effectively learn to use target cue dimensions. These findings may have considerable implications for the field of speech acquisition.

中文翻译：

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对老鼠和男人：语音获取是区别于预测误差的学习，而不仅仅是统计跟踪。

尽管有越来越多的证据表明听众对语音提示的统计分布高度敏感，但是学习的基础机制可能并非纯粹是统计跟踪。数十年的动物学习研究表明，学习结果来自预测和预测错误。两项人工语言学习实验测试了两种预测，它们将错误驱动与纯统计模型区分开来。即提示竞争-特别是Kamin（1968）的“阻塞”效应（实验1）和学习事件的预测结构（实验2）。在实验1中，先验知识性提示会阻碍第二个提示的学习。这一发现可能有助于解释第二语言学习者在获取非母语语音提示的母语理解方面的困难。在实验2中，与非歧视性（结果提示）顺序相比，区分性（提示结果）顺序的学习效果更好。实验2表明，学习语音提示，包括阻塞的逆转效果，取决于（不）从预测错误中学习，并且取决于听觉提示与语义结果的时间顺序。在一起，这些结果表明（a）现有的声音提示知识可能会阻止以后学习新的提示，并且（b）语音获取取决于学习事件的预测结构。当可获得来自预测误差的反馈时，这将驱使学习者忽略明显的非歧视性线索，并有效地学习使用目标线索尺寸。这些发现可能会对语音获取领域产生重大影响。