Simultaneous online tracking of adjacent and nonadjacent dependencies in statistical learning

Vuong, Loan; Meyer, Antje S.; Christiansen, Morten H.

doi:10.1037/e512592013-516

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…Studies in which participants are either tested multiple times after multiple familiarization sessions or in which on‐line learning measures are collected (such as the AGL‐SRT task developed by Misyak et al., ) could be used to reveal individual learning trajectories. One recent study did employ this paradigm to test learning of a language containing both adjacent and non‐adjacent dependencies (Vuong, Meyer, & Christiansen, ). Consistent with the results of the current studies, they found that learners were sensitive to both types of dependencies from early in familiarization, though a long exposure duration was needed to reveal significant effects.…”

Section: Discussionmentioning

confidence: 99%

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

Romberg¹,

Saffran²

2013

Cognitive Science

109

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Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and nonadjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and nonadjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic nonadjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition.

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Section: Discussionmentioning

confidence: 99%

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

Romberg¹,

Saffran²

2013

Cognitive Science

109

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“…52 Several studies with infants and adults have shown categorization of novel words when these words are embedded in frame contexts giving credence to both frame and flexible frame proposals of lexical Volume 4, September/October 2013 category acquisition. 8,9,53,54 However, it is difficult to determine whether learning in these studies is the result of adjacent or nonadjacent dependency learning or both.…”

Section: Nonadjacent Dependencies and Lexical Categoriesmentioning

confidence: 99%

The development of nonadjacent dependency learning in natural and artificial languages

Sandoval

Gómez

2013

WIRES Cognitive Science

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Nonadjacent dependencies occur over one or more intervening units and require learners to track discontinuous sequential relationships. These discontinuous relationships are present at multiple levels in language (e.g., as seen in morphosyntactic dependencies and at the phonological level in vowel harmony). Experiments suggest that these dependencies are acquired using statistical learning mechanisms and that this learning is also affected by perceptual biases. Artificial and natural language studies have shown that infants are sensitive to these statistical regularities but there appear to be developmental constraints on learning. Developmental investigations have also examined how knowledge and processing of the intervening elements affect learning, and whether categories can be acquired using nonadjacent dependency information. WIREs Cogn Sci 2013, 4:511-522. doi: 10.1002/wcs.1244 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

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“…If true, this would suggest a severe limitation in learners' ability to track adjacent and nonadjacent statistics concurrently (see also Pacton & Perruchet, 2008). Romberg and Saffran (2013) investigated whether adjacent and nonadjacent dependencies can be learned concurrently or whether there may be trade-offs between adjacent and nonadjacent learning (see also Vuong, Meyer, & Christiansen, 2011). Similar to Gómez (2002), they trained adult learners on an AxB grammar with deterministic nonadjacent dependencies between the first and third elements (three A-B frames, p = 1.0 each).…”

Section: Concurrent Statistical Learning Of Adjacent and Nonadjacent mentioning

confidence: 99%

Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies

2015

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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 dependencies under low intervening variability using a modified serial reaction time (SRT) task. Young adults were trained on mixed sets of materials comprising equally probable adjacent and nonadjacent dependencies. Offline tests administered after training showed better performance for adjacent than nonadjacent dependencies. However, online SRT data indicated that the participants developed sensitivity to both types of dependencies during training, with no significant differences between dependency types. The results demonstrate the value of online measures of learning and suggest that adjacent and nonadjacent learning can occur together even when there is low variability in the intervening material. Open Practices This article has been awarded an Open Materials badge. All materials are publicly accessible in the IRIS digital repository at http://www.iris-database.org. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki

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Simultaneous online tracking of adjacent and nonadjacent dependencies in statistical learning

Cited by 5 publications

References 13 publications

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

The development of nonadjacent dependency learning in natural and artificial languages

Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies

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