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Computation of conditional probability statistics by 8-month-old infants (1998)
| Venue: | PSYCHOLOGICAL SCIENCE |
| Citations: | 216 - 21 self |
BibTeX
@ARTICLE{Aslin98computationof,
author = {Richard N. Aslin and Jenny R. Saffran and Elissa L. Newport},
title = {Computation of conditional probability statistics by 8-month-old infants},
journal = {PSYCHOLOGICAL SCIENCE},
year = {1998},
volume = {9},
number = {4},
pages = {321--324}
}
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Abstract
A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants’ responses to words versus part-words (trisyllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable
Keyphrases
8-month-old infant conditional probability statistic continuous stream word boundary particular type postfamiliarization test trisyllabic nonsense word test word recent report speech syllable wordlike unit powerful learning mechanism statistical information present study word-segmentation task trisyllabic sequence transitional probability artificial language corpus fluent speech prosodic cue statistical computation transitional
