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Non-adjacent dependency learning over consonants & vowels in 8- to 10-month-olds ...
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Elucidating an implicit learning network in healthy adults during artificial grammar tasks
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In: Master's Theses and Capstones (2021)
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Abstract:
Implicit learning is the unconscious extraction of rules governing complex stimuli, measured through experiments such as artificial grammar tasks, and is directly related to natural language learning. While several theories address the underlying framework for implicit learning, few studies have shed light on a consensus neural network involved in implicit learning. The short-term goal of this thesis is to further elucidate the brain regions involved in implicit learning of linguistic stimuli. The long-range goal of this research program is to understand how implicit learning and the brain regions associated with it relate to language learning and treatment outcomes in individuals with aphasia. A coordinate-based meta-analysis of 25 studies using implicit language learning tasks was completed. Activation likelihood estimate (ALE) results show significant activation in the bilateral inferior frontal gyri, bilateral insula, left supplemental motor area, right precentral gyrus, right middle cingulate, right middle occipital gyrus, and right inferior parietal lobule. The inferior frontal gyrus is discussed as a general rule-processing and error detection mechanism, and other regional activations are discussed related to their involvement in a cognitive control network. Cognitive control may be seen as an underlying mechanism for successful implicit learning and may be clinically relevant as a target for language intervention to scaffold syntax comprehension.
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Keyword:
artificial grammar; fMRI; implicit learning; statistical learning
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URL: https://scholars.unh.edu/cgi/viewcontent.cgi?article=2492&context=thesis
https://scholars.unh.edu/thesis/1453
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Information flow, artificial phonology and typology
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Perceptual saliency, lenition, and learnability: An artificial grammar learning study
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
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In: Computer Science: Faculty Publications and Other Works (2020)
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Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions
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| 7 |
Bias in Phonotactic Learning: Experimental Studies of Phonotactic Implicationals
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In: Glewwe, Eleanor. (2019). Bias in Phonotactic Learning: Experimental Studies of Phonotactic Implicationals. UCLA: Linguistics 0510. Retrieved from: http://www.escholarship.org/uc/item/4456s1j0 (2019)
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| 8 |
Inductive learning of locality relations in segmental phonology
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 10, No 1 (2019); 14 ; 1868-6354 (2019)
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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| 11 |
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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| 12 |
The role of auditory perceptual gestalts on the processing of phrase structure ...
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| 13 |
The ambiguous status of laryngeals in nasal vowel-consonant harmony
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In: Toronto Working Papers in Linguistics; Vol 40 (2018): Special issue from the CRC-sponsored phonology/phonetics workshops ; 1718-3510 ; 1705-8619 (2018)
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| 14 |
On the Relation between Phonotactic Learning and Alternation Learning
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In: Chong, Junxiang Adam. (2017). On the Relation between Phonotactic Learning and Alternation Learning. UCLA: Linguistics 0510. Retrieved from: http://www.escholarship.org/uc/item/7235q340 (2017)
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It Doesn't Hurt to Try: The Impact of a Search for Structure in Artificial Grammar Learning
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| 17 |
A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet
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In: Proceedings of COLING 2016 ; 26th International Conference on Computational Linguistics (COLING 2016) ; https://hal.archives-ouvertes.fr/hal-01709130 ; 26th International Conference on Computational Linguistics (COLING 2016), Dec 2016, Osaka, Japan. pp. 3542-3552 (2016)
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| 18 |
Simple K-star Categorial Dependency Grammars and their Inference
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In: The 13th International Conference on Grammatical Inference (ICGI) ; https://hal.archives-ouvertes.fr/hal-01363393 ; The 13th International Conference on Grammatical Inference (ICGI), Oct 2016, Delft, Netherlands ; http://icgi2016.tudelft.nl/ (2016)
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Surface Realisation from Knowledge Bases ; Bases de Connaissances et Réalisation de Surface
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In: https://hal.inria.fr/tel-01754499 ; Computation and Language [cs.CL]. Université de Lorraine, 2016. English. ⟨NNT : 2016LORR0004⟩ (2016)
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| 20 |
What Matters in Artificial Learning, Sonority Hierarchy or Natural Classes?
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2015 Annual Meeting on Phonology ; 2377-3324 (2016)
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