Top-Down Structure Influences Learning of Non-Adjacent Dependencies in an Artificial Language

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Description: Due to the hierarchical organization of natural languages, words that are syntactically related are not always linearly adjacent. For example, the subject and verb in the child always runs agree in person and number, although they are not adjacent in the sequences of words. Since such dependencies are indicative of abstact linguistc structure, it is of significant theoretical interest how these relationships are acquired by language learners. Most experiments that investigate non-adjacent dependency (NAD) learning have used artificial languages in which the to-be-learned dependencies are isolated, by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. We report the first demonstration to our knowledge of successful learning of embedded NADs, in which silences do not mark dependency boundaries. Subjects heard passages of English with a predictable structure, interspersed with passages of the artificial language. The English sentences were designed to induce boundaries in the artificial languages. In Experiment 1 & 3 the artificial NADs were contained within the induced boundaries and subjects learned them, whereas in Experiment 2 & 4, the NADs crossed the induced boundaries and subjects did not learn them. We take this as evidence that sentential structure was “carried over” from the English sentences and used to organize the artificial language. This approach provides several new insights into the basic mechanisms of NAD learning in particular and statistical learning in general. © American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors permission. The final article is available via its DOI: 10.1037/xge0000384

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