Abstract
The ability to adapt to statistical structure (often referred to as “statistical learning”) has been proposed to play a major role in the acquisition and use of natural languages. Several recent studies have explored the relationship between individual differences in statistical learning and language outcomes. These studies have produced mixed results, with some studies finding a significant relationship between statistical learning and language outcomes, and others finding weak or null results. Furthermore, the few studies that have used multiple measures of statistical learning have reported that they are not correlated (e.g., [1]). The current study assesses the reliability of various measures of auditory statistical segmentation, and their consistency over time. That is, do the generally low correlations observed between measures of statistical learning stem from task demands, the psychometric properties of the measures, or the fact that statistical learning may be a highly fragmented construct? Our results confirm previous reports that individual measures of statistical learning tend not to correlate with each other, and suggest that the somewhat weak reliability of the measures may be an important factor in the low correlations. Our data also suggest that aggregating performance across tasks may be an avenue for improving the reliability of the measures.
Highlights
Statistical information plays a key role in recent t heories of language use
Participants performed above chance on all of the languages, which indicated that they had successfully learned the structure of the training sets7
As was the case in Experiment 1, we found that a composite measure of word segmentation tasks, as well as a composite measure consisting of the word segmentation tasks and the artificial grammar learning task, was observed to have higher test-retest reliability than any of the individual measures
Summary
Statistical information plays a key role in recent t heories of language use From this perspective, a straightforward prediction is that individual differences in SL ability should be related to variation in language outcomes, with the specific expectation that individuals who are better at SL should show better language learning and processing (e.g., [1, 9, 25, 26]). Successful learning in each of these tasks has been construed as evidence of SL, the tasks themselves vary so widely as to raise the question about whether they all tap into the same underlying process, or if the term “statistical learning” might be an umbrella term describing a set of independent processes (see [39]) This uncertainty can be seen at both a theoretical and a methodological level. These differences in perceptual ease may, in turn, lead to different patterns of learning across audio and video input
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