Learning structure-dependent agreement in a hierarchical artificial grammar

Franck, Julie; Rotondi, Irene; Frauenfelder, Ulrich Hans

doi:10.1016/j.jml.2015.11.003

Cited by 3 publications

(2 citation statements)

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“…Recently, using an artificial language with an artificial lexicon, Franck et al. () also found evidence that participants could generalize knowledge acquired during training to sentences with a more complex structure. Specifically, they claimed that the participants learned the following abstract agreement rule: “The verb agrees with the hierarchically highest noun in its constituent; if there is no noun in its constituent, the verb agrees with the highest noun in the immediately preceding constituent” (p. 87).…”

Section: Discussionmentioning

confidence: 99%

“…() found that the introduction of semantics boosted the learning of long‐distance dependency and center‐embedded structures. Franck, Rotondi, and Frauenfelder () also reported that including semantics in an artificial grammar was conducive to the learning of long‐distance dependencies. Endress and Hauser () found that when word categories were used as the elements of the structures, the statistical learning mechanism was suppressed unless the sequence made syntactic sense.…”

Section: Introductionmentioning

confidence: 97%

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Generalization of Syntactic Knowledge in Semiartificial Language Learning

Yan

Williams

2018

Language Learning

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A hallmark of grammatical knowledge is the ability to parse novel syntactic structures. Previous artificial language studies have examined learning hierarchical structures, but few have involved meaningful language and shown generalization to novel structures. This study addressed this issue using the semiartificial language paradigm. The experimental group of monolingual English speakers was exposed to singly embedded sentences combining Chinese syntax with English lexis. A novel testing procedure was used where participants made plausibility judgments on grammatical sentences featuring two‐level embedding. Only correct application of the rules underlying the training sentences permitted correct discrimination of plausible and implausible sentences. The experimental group performed above chance, an untrained control group performed at chance, and an untrained and unaware Chinese control group showed above‐chance performance. Confidence judgments and rule recognition analysis suggested that learning was implicit. We concluded that the experimental group demonstrated structural generalization and might have acquired the syntactic rule system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Generalization of Syntactic Knowledge in Semiartificial Language Learning

Yan

Williams

2018

Language Learning

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The Use of Grammar Models in Foreign Language Teaching to Non-Linguistics Students

Karyakina

Korableva

Pankrateva

et al. 2022

Integration of Engineering Education and the Humanities: Global Intercultural Perspectives

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Does Sleep Improve Your Grammar? Preferential Consolidation of Arbitrary Components of New Linguistic Knowledge

Mirković

Gaskell

2016

PLoS ONE

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We examined the role of sleep-related memory consolidation processes in learning new form-meaning mappings. Specifically, we examined a Complementary Learning Systems account, which implies that sleep-related consolidation should be more beneficial for new hippocampally dependent arbitrary mappings (e.g. new vocabulary items) relative to new systematic mappings (e.g. grammatical regularities), which can be better encoded neocortically. The hypothesis was tested using a novel language with an artificial grammatical gender system. Stem-referent mappings implemented arbitrary aspects of the new language, and determiner/suffix+natural gender mappings implemented systematic aspects (e.g. tib scoiffesh + ballerina, tib mofeem + bride; ked jorool + cowboy, ked heefaff + priest). Importantly, the determiner-gender and the suffix-gender mappings varied in complexity and salience, thus providing a range of opportunities to detect beneficial effects of sleep for this type of mapping. Participants were trained on the new language using a word-picture matching task, and were tested after a 2-hour delay which included sleep or wakefulness. Participants in the sleep group outperformed participants in the wake group on tests assessing memory for the arbitrary aspects of the new mappings (individual vocabulary items), whereas we saw no evidence of a sleep benefit in any of the tests assessing memory for the systematic aspects of the new mappings: Participants in both groups extracted the salient determiner-natural gender mapping, but not the more complex suffix-natural gender mapping. The data support the predictions of the complementary systems account and highlight the importance of the arbitrariness/systematicity dimension in the consolidation process for declarative memories.

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Learning structure-dependent agreement in a hierarchical artificial grammar

Cited by 3 publications

References 68 publications

Generalization of Syntactic Knowledge in Semiartificial Language Learning

Generalization of Syntactic Knowledge in Semiartificial Language Learning

The Use of Grammar Models in Foreign Language Teaching to Non-Linguistics Students

Does Sleep Improve Your Grammar? Preferential Consolidation of Arbitrary Components of New Linguistic Knowledge

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