Acceso usuarios
Vesna Stojanovik, Vitor Zimmerer, Jane Setter, Kerry D. Hudson, Isil Poyraz-Bilgin, Douglas Saddy
Artificial grammar learning is an empirical paradigm that investigates basic pattern and structural processing in different populations. It can inform how higher cognitive functions, such as language use, take place. Our study used artificial grammar learning to assess how children with Williams syndrome (WS; n = 16) extract patterns in structured sequences of synthetic speech, how they compare to typically developing (TD) children (n = 60), and how prosodic cues affect learning. The TD group was divided into a group whose nonverbal abilities were within the range of the WS group, and a group whose chronological age was within the range of the WS group. TD children relied mainly on rule-based generalization when making judgments about sequence acceptability, whereas children with WS relied on familiarity with specific stimulus combinations. The TD participants whose nonverbal abilities were similar to the WS group showed less evidence of relying on grammaticality than TD participants whose chronological age was similar to the WS group. In absence of prosodic cues, the children with WS did not demonstrate evidence of learning. Results suggest that, in WS children, the transition to rule-based processing in language does not keep pace with TD children and may be an indication of differences in neurocognitive mechanisms.
