Speech segmentation by statistical learning is supported by domain-general processes within working memory

Abstract

The purpose of this study was to examine the extent to which working memory resources are recruited during statistical learning (SL). Participants were asked to identify novel words in an artificial speech stream where the transitional probabilities between syllables provided the only segmentation cue. Experiments 1 and 2 demonstrated that segmentation performance improved when the speech rate was slowed down, suggesting that SL is supported by some form of active processing or maintenance mechanism that operates more effectively under slower presentation rates. In Experiment 3 we investigated the nature of this mechanism by asking participants to perform a two-back task while listening to the speech stream. Half of the participants performed a two-back rhyme task designed to engage phonological processing, whereas the other half performed a comparable two-back task on un-nameable visual shapes. It was hypothesized that if SL is dependent only upon domain-specific processes (i.e., phonological rehearsal), the rhyme task should impair speech segmentation performance more than the shape task. However, the two loads were equally disruptive to learning, as they both eradicated the benefit provided by the slow rate. These results suggest that SL is supported by working-memory processes that rely on domain-general resources.