Statistical learning for speech segmentation: Age-related changes and underlying mechanisms.

Abstract

Statistical learning (SL) is a powerful learning mechanism that supports word segmentation and language acquisition in infants and young adults. However, little is known about how this ability changes over the life span and interacts with age-related cognitive decline. The aims of this study were to: (a) examine the effect of aging on speech segmentation by SL, and (b) explore core mechanisms underlying SL. Across four testing sessions, young, middle-aged, and older adults were exposed to continuous speech streams at two different speech rates, both with and without cognitive load. Learning was assessed using a two-alterative forced-choice task in which words from the stream were pitted against either part-words, which occurred across word boundaries in the stream, or nonwords, which never appeared in the stream. Participants also completed a battery of cognitive tests assessing working memory and executive functions. The results showed that speech segmentation by SL was remarkably resilient to aging, although age effects were visible in the more challenging conditions, namely, when words had to be discriminated from part-words, which required the formation of detailed phonological representations, and when SL was performed under cognitive load. Moreover, an analysis of the cognitive test data indicated that performance against part-words was predicted mostly by memory updating, whereas performance against nonwords was predicted mostly by working memory storage capacity. Taken together, the data show that SL relies on a combination of implicit and explicit skills, and that age effects on SL are likely to be linked to an age-related selective decline in memory updating. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 32 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.