Graf Estes, K.
& Lew-Williams, C. (2015). Listening Through Voices: Infant Statistical
Word Segmentation Across Multiple Speakers. Developmental Psychology, 51(11),
1517-1528.
Statistical language
learning refers to learning aspects of language based on the patterns of the
language. For example, sounds within a word are more likely to occur together
than are sounds that cross word boundaries. These patterns might help infants
learn to distinguish words from word boundaries. In this study, Graf Estes and
Lew-Williams focused on the statistical learning in infants.
In a real
environment, infants are exposed to multiple voices, each with a unique
speaking style and rate. Graf Estes and Lew-Williams used multiple voices in a
monotone speech stream to mimic this natural environment. Infants from this
study listened to a language produced by eight different voices that changed
frequently for six minutes. Results suggested that infants were able to learn
the artificial language when tested with a common voice or a novel voice, also
suggesting that infants can generalize representations.
In the second
series of experiments, infants listened to two different voices. The researchers
suggested that the use of two dominant voices may better mimic the infant’s environment
(e.g. two parents). Infants, however, failed to display signs of language
acquisition. Graf Estes and Lew-Williams suggested two possible explanations:
Infants were able to learn both the words and part-words, thus showing no
discrimination during test phase, or the used of two voices resulted in a focus
on discerning the voices, thus inhibiting language acquisition.
Although the
mixed results require further investigation, the findings highlight the
efficiency of learning in the context of variability (see also, Plante et al. 2014).
Blogger: Hosung (Joel)
Kang is a neuroscience student completing his undergraduate thesis project in
the Language and Working Memory Lab.
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