Fernandes Tânia, Kolinsky Régine, Ventura Paulo
Universidade do Porto, Porto, Portugal.
Atten Percept Psychophys. 2010 Aug;72(6):1522-32. doi: 10.3758/APP.72.6.1522.
In two artificial language learning experiments, we investigated the impact of attention load on segmenting speech through two sublexical cues: transitional probabilities (TPs) and coarticulation. In Experiment 1, we observed that coarticulation processing was resilient to high attention load, whereas TP computation was penalized in a graded manner. In Experiment 2, we showed that encouraging participants to actively search for "word" candidates enhanced overall performance but was not sufficient to preclude the impairment of statistically driven segmentation by attention load. As long as attentional resources were depleted, independently of their intention to find these "words," participants segmented only TP words with the highest TPs, not TP words with lower TPs. Attention load thus has a graded and differential impact on the relative weighting of the cues in speech segmentation, even when only sublexical cues are available in the signal.
在两项人工语言学习实验中，我们通过两个次词汇线索：过渡概率（TPs）和协同发音，研究了注意力负荷对语音分割的影响。在实验1中，我们观察到协同发音处理对高注意力负荷具有弹性，而TP计算则受到分级惩罚。在实验2中，我们表明鼓励参与者积极寻找“单词”候选词可提高整体表现，但不足以排除注意力负荷对基于统计的分割的损害。只要注意力资源被耗尽，无论参与者是否有意寻找这些“单词”，他们都只会分割具有最高TPs的TP单词，而不会分割具有较低TPs的TP单词。因此，即使信号中仅存在次词汇线索，注意力负荷对语音分割中线索的相对权重也具有分级和差异影响。
