The modulating effect of lexical predictability on perceptual learning of degraded speech

Predictive coding is considered to be an important mechanism for perceptual learning. Posterior prediction-error minimization can lead to higher rates of lasting changes in the representational hierarchy, and hence is likely to enhance the process of learning. In the field of speech processing, although considerable studies have demonstrated that a highly predictive sentence context can facilitate the perception of forthcoming word, it remains to be examined that how this type of predictability affects the perceptual learning of speech (especially degraded speech). The present study, therefore, aimed to examine whether and how the lexical predictability of spoken sentences modulates perceptual learning of speech embedded in noise, by using spoken sentences as training stimuli and strictly controlling the semantic-context constraint of these training sentences. The current study adopted a “pretest-training-posttest” procedure. Two groups of subjects participated in this perceptual learning study, with cognitive and language abilities matched across these two groups. For one group, the spoken sentences used for training all have a highly predictive semantic context; for another group, the training sentences all have a low predictive context. The results showed that both the reaction time and accuracy of the speech-in-noise intelligibility test were significantly improved in the post-training phase compared to the pre-training phase; moreover, the learning-related improvement was significantly enhanced in participants with weak-constraint sentences as training stimuli (compared to those with strong-constraint sentences as training stimuli). This enhancement effect of low lexical predictability on learning-related improvement supports a prediction-error based account of perceptual learning.