Elsevier

Cognition

Volume 108, Issue 1, July 2008, Pages 271-280
Cognition

Brief article
The link between statistical segmentation and word learning in adults

https://doi.org/10.1016/j.cognition.2008.02.003Get rights and content

Abstract

Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this possible link in adults by following a statistical segmentation exposure phase with an artificial lexicon learning phase. Participants were able to learn all novel object–label pairings, but pairings were learned faster when labels contained high probability (word-like) or non-occurring syllable transitions from the statistical segmentation phase than when they contained low probability (boundary-straddling) syllable transitions. This suggests that, for adults, labels inconsistent with expectations based on statistical learning are harder to learn than consistent or neutral labels. In contrast, a previous study found that infants learn consistent labels, but not inconsistent or neutral labels.

Introduction

The task of learning words from spoken input is an extremely difficult one in part because there are no consistent cues to word boundaries. Conditional probabilities of syllable sequences are one cue to word boundaries: within-word syllable sequences are much more likely than between-word syllable sequences. Saffran and colleagues reported that adults (Saffran, Newport, & Aslin, 1996) and infants (Saffran, Aslin, & Newport, 1996) can extract novel conditional probabilities from only a few minutes of exposure and use this statistical information for sequence segmentation. Saffran and colleagues found that both adults and infants could distinguish syllable sequences that contained high probability syllable transitions (“words”, sequences that consistently occurred) from those that contained low probability syllable transitions (“partwords”, sequences straddling a “word” boundary, thus occurring only occasionally). This statistically-based learning and segmentation ability could form part of a mechanism that supports language acquisition. However, it is also possible that, although adults and infants can extract these statistics in an explicit laboratory task, this learning has no connection to the mechanisms involved in learning new words. Only recently have researchers sought to demonstrate a direct link between statistical word segmentation and word learning.

A recent study tested the relation between statistical segmentation and object label learning in infants (Graf Estes, Evans, Alibali, & Saffran, 2007). Each infant was first exposed to a non-segmented syllable stream as in typical statistical segmentation studies. After this exposure phase, the infants completed a habituation-based object label learning phase. The infants were habituated to two label–object pairings, followed by two types of test trials: trials in which the original label–object pairings remained the same versus trials in which the pairings were switched. The difference in looking times between same and switch trials reflects the extent to which infants associated a particular object with a particular label; in other words, the extent to which infants learned object labels (see also Stager and Werker, 1997, Werker et al., 1998). For some of the infants the object labels contained high probability syllable transitions (“words”) from the segmentation stream. For other infants the object labels contained low or zero probability syllable transitions (“partwords” or “nonwords”, respectively). Graf Estes et al. found that when the object labels were “words”, there was a looking time difference between same and switch trials, but there was no difference when the object labels were “partwords” or “nonwords” (labels containing syllable transitions that did not occur in the exposure stream). That is, infants learned object labels when those labels were consistent with the statistics of the preceding passive exposure phase, but not when the labels were inconsistent with those statistics. One interpretation of these results is that statistical segmentation facilitates word learning by creating memory traces that can then be mapped to meanings (object labels).

In the present work, we extended this finding by examining the link between statistical segmentation and word learning in adults. First, this extension allowed us to test whether the link is only viable in infancy. Perhaps the limited language knowledge of infants and the strong pressure to acquire new words causes infants to use more of the available information for word learning than adults, who already have large vocabularies and greater cognitive abilities that may obviate these mechanisms. Conversely, it is possible that statistical word segmentation is intrinsically linked to word learning and this link persists into adulthood. Second, testing adults allowed the use of an explicit word learning task rather than inferring word learning indirectly from dis-habituation data. Third, the infant habituation test only showed differences at a single time point, but we can use more flexible tasks with adults to examine possible differences in the trajectory of the learning curve and conduct a finer-grained analysis of the link between statistical segmentation and word learning. Our experiments tested whether adults would be better at learning object labels if the labels were consistent with syllable transition probability (i.e., “words” vs. “partwords”). Experiment 1 tested whether adults learn novel object labels faster when those labels contain high probability transitions. Experiment 2 further tested whether learning rate differences between “words” and “partwords” are due to inhibition of labels that contain low probability transitions or facilitation of labels that contain high probability transitions.

Section snippets

Participants

Participants were 49 students at the University of Connecticut who reported English as their native language and normal hearing. They received course credit for participation in the experiment.

Materials

A series of pilot experiments was used to develop a set of auditory materials that replicated the classic statistical segmentation results (Saffran, Newport, et al., 1996). The materials were based on syllables spoken by a female native speaker of American English in all possible co-articulatory contexts

Experiment 2

Experiment 1 demonstrated that participants learn novel object labels faster when those labels contain high probability syllable transitions than when they contain low probability syllable transitions. In Experiment 2, we examined whether this difference was due to statistical facilitation of high probability syllable transitions or inhibition of low probability syllable transitions. This was done by adding a third condition to the artificial lexicon; in this condition, participants learned

Conclusions

One recent study showed that infants can use statistically segmented words as object labels (Graf Estes et al., 2007). The current study provides several extensions of that finding. First, the present results showed a link between statistical segmentation and word learning in adults who already have large vocabularies; so the linguistic relevance of statistical segmentation is not limited to infants who are just beginning language learning. Second, the present results demonstrated a link

Acknowledgements

We thank Deirdre Dempsey, Matthew Freiburger, Emma Chepya, and Hillarey Jones for their help with data collection. This research was supported by NIDCD Grant R01DC005765 to J.S.M., NICHD NRSA F32HD052364 to D.M. and by NICHD Grant HD01994 to Haskins Laboratories.

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