2015
DOI: 10.1111/lang.12137
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Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies

Abstract: When children learn their native language, they have to deal with a confusing array of dependencies between various elements in an utterance. The dependent elements may be adjacent to one another or separated by intervening material. Prior studies suggest that nonadjacent dependencies are hard to learn when the intervening material has little variability, which may be due to a trade‐off between adjacent and nonadjacent learning. In this study, we investigate the statistical learning of adjacent and nonadjacent… Show more

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Cited by 38 publications
(60 citation statements)
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References 59 publications
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“…In previous SRT experiments, we found that the learning of adjacent dependencies in triplets (Minier et al, 2016), or pairs of shapes (Fagot, Malassis, & Medam, 2018) gave rise to similar progressive (albeit earlier) decrease in response times throughout the exposure to these regularities. These data, along with previous findings s (Frost & Monaghan, 2016;Romberg & Saffran, 2013;Vuong, Meyer, & Christiansen, 2016), suggest that adjacent and non-adjacent dependencies might be extracted by similar statistical learning mechanisms. However, they do not allow disentangling between the single-process and the dual-system proposals described above.…”
Section: Discussionsupporting
confidence: 81%
“…In previous SRT experiments, we found that the learning of adjacent dependencies in triplets (Minier et al, 2016), or pairs of shapes (Fagot, Malassis, & Medam, 2018) gave rise to similar progressive (albeit earlier) decrease in response times throughout the exposure to these regularities. These data, along with previous findings s (Frost & Monaghan, 2016;Romberg & Saffran, 2013;Vuong, Meyer, & Christiansen, 2016), suggest that adjacent and non-adjacent dependencies might be extracted by similar statistical learning mechanisms. However, they do not allow disentangling between the single-process and the dual-system proposals described above.…”
Section: Discussionsupporting
confidence: 81%
“…This makes it possible to track the detailed time-course of learning. RT differences between predicted and unpredicted stimuli have also been demonstrated in other tasks in auditory [86] and audio-visual SL [20,31,32,56]. Importantly, online measures of SL have been found to correlate with sentence processing in L1 [20,31], providing preliminary evidence regarding its predictive validity.…”
Section: The Promise Of Online Measuresmentioning
confidence: 78%
“…Following this trend, we decided to use response times as an online measure of NAD learning, in particular measuring the disruption peak that occurs in the response time pattern when items are presented that are discordant with NAD rules. Previous work has shown that disruption peaks reflect sensitivity to NADs in adults (López-Barroso, Cucurell, Rodrígez-Fornells, & de Diego-Balaguer, 2016;Misyak et al, 2010;Vuong, Meyer, & Christiansen, 2015) and in primary-school-aged children (Lammertink, van Witteloostuijn et al, 2018). The use of disruption peaks as an index of statistical learning has its roots in the serial reaction time task literature (Nissen & Bullemer, 1987), and the reason to work with disruption peaks rather than a decrease in response times over the first few training blocks is that such a response time decrease is not necessarily the result of statistical learning.…”
Section: Statistical Learning and Its Methodological Challengesmentioning
confidence: 98%