Department of Psychology, Universidade Federal de São Carlos, São Carlos, São Paulo, Brazil.
Programa de Pós-Graduação em Psicologia, Centro de Ciências Humanas, Universidade Federal de São Carlos, Via Washington Luís, Km 235-Caixa Postal 676, São Carlos, SP, 13565-905, Brazil.
Mem Cognit. 2021 Oct;49(7):1300-1310. doi: 10.3758/s13421-021-01163-4. Epub 2021 Mar 9.
Statistical regularities in linguistic input, such as transitional probability and phonotactic probability, have been shown to promote speech segmentation. It remains unclear, however, whether or how the combination of transitional probabilities and subtle phonotactic probabilities influence segmentation. The present study provides a fine-grained investigation of the effects of such combined statistics. Adults (N = 81) were tested in one of two conditions. In the Anchor condition, they heard a continuous stream of words with small differences in phonotactic probabilities. In the Uniform condition, all words had comparable phonotactic probabilities. In both conditions, transitional probability was stronger in words than in part-words. Only participants from the Anchor condition preferred words at test, indicating that the combination of transitional probabilities and subtle phonotactic probabilities may facilitate speech segmentation. We discuss the methodological implications of our findings, which demonstrate that even small phonotactic variations should be accounted for when investigating statistical speech segmentation.
语言输入中的统计规律,如转移概率和音韵概率,已被证明可以促进言语分割。然而,目前尚不清楚过渡概率和细微音韵概率的组合如何影响分割。本研究提供了对这种组合统计数据影响的精细研究。81 名成年人(N=81)在两种条件下接受了测试。在锚定条件下,他们听到了连续的单词流,这些单词在音韵概率上只有微小的差异。在均匀条件下,所有的单词都具有可比的音韵概率。在这两种情况下,过渡概率在单词中比在部分单词中更强。只有来自锚定条件的参与者在测试中更喜欢单词,这表明过渡概率和细微音韵概率的组合可能促进言语分割。我们讨论了我们发现的方法学意义,这些发现表明,即使是细微的音韵变化,在研究统计言语分割时也应该被考虑进去。