• Citer

The impact of attention load on the use of statistical information and coarticulation as speech segmentation cues.

par Fernandes, Tania ;Kolinsky, Régine ;Ventura, Paulo
Référence Attention, Perception & Psychophysics, 72, 5, page (1522-1532)
Publication Publié, 2010

Article révisé par les pairs

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

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, while TPs 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 only segmented 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.