The neural bases of the learning and generalization of morphological inflection

Nevat, Michael; Ullman, Michael T.; Eviatar, Zohar; Bitan, Tali

doi:10.1016/j.neuropsychologia.2016.08.026

Cited by 40 publications

(30 citation statements)

References 78 publications

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“…They reported non-significant activation in pars opercularis, with significant activation appearing in pars triangularis. Although there are important methodological differences between their study and the present work, the findings of Nevat et al (2017) raise the possibility that subtle differences in regional activation may correspond to the distributional properties of the morphology to be learned.…”

Section: Discussionmentioning

confidence: 65%

“…Free morphemes, such as articles and auxiliaries, may require a different network than that described for Russian gender marking. Nevat et al (2017) reported findings for the inferior frontal gyrus that may indicate this type of regional variation is possible. Nevat et al (2017) reported significant activation during the test phase of a study in which participants were previously trained on an artificial grammatical marking system using an exposure-practice-feedback format.…”

Section: Discussionmentioning

confidence: 93%

“…Nevat et al (2017) reported findings for the inferior frontal gyrus that may indicate this type of regional variation is possible. Nevat et al (2017) reported significant activation during the test phase of a study in which participants were previously trained on an artificial grammatical marking system using an exposure-practice-feedback format. They reported non-significant activation in pars opercularis, with significant activation appearing in pars triangularis.…”

Section: Discussionmentioning

confidence: 93%

“…The ICA studies have also indicated that a number of transient activations occur during learning. Some have suggested that early activation that disappears is involved only during initial learning (Nevat et al, 2017). However, transient activations also occurred after the initial learning period here and elsewhere for implicit language learning tasks (Plante et al, 2014, 2015; López-Barroso et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

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Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm

et al. 2017

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The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the “rules” for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

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Section: Discussionmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

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Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm

et al. 2017

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“…Leminen et al, 2016;M. Leminen et al, 2020;Merkx et al, 2011;Mirković et al, 2019;Mirković & Gaskell, 2016;Nevat et al, 2017;Tamminen et al, 2012Tamminen et al, , 2015. Ferman et al (2009) investigated the long-term acquisition of an artificial morphological rule over consecutive learning sessions with 1-3-day-long intervals of offline consolidation.…”

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confidence: 99%

Semantic information modulates online morphological learning: neuromagnetic investigation

Vr¹,

Shtyrov²,

Leminen³

et al. 2020

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Learning to recognize morphemic boundaries is essential for fluent language use. In languages with rich morphology, the question of morphological learning is particularly relevant, because the abundance of bound morphemes represents one of the most challenging aspects of learning. However, neural mechanisms underlying the acquisition of novel morphological units remain largely unexplored. Here, we investigated the online build-up of novel suffix representations in the left frontal and temporal cortices. We trained 19 native Finnish-speaking participants with novel suffixes through a word-picture association task. Following this short semantic training session, we used magnetoencephalography (MEG) to record the participants’ brain responses to novel suffixes combined with real and pseudoword stems. During the MEG recording, the affixed stimuli were presented in a passive listening task without attention focused on the presented sounds. Half of the novel suffixes were trained and half untrained, and real Finnish suffixes served as acoustic controls. We compared neural sources measured in the early and late phases of short passive exposure to the novel suffixes. We found an overall response enhancement for suffixes with real stems, in contrast with a general response decrease for pseudoword stems, implying that suffixes were morphologically decomposed when they were attached to a recognizable stem. Moreover, both types of novel suffixes elicited lower fronto-temporal source activations than real suffixes, suggesting that such a brief exposure may not be sufficient for the integration of novel morphological units into the mental lexicon. However, semantic training appeared to modulate the learning process already in the early stages, indicated by the different response patterns evoked by trained and untrained novel suffixes. Responses for untrained suffixes combined with real stems, in turn, increased over the course of the passive listening task, possibly reflecting memory trace formation. Previously trained suffixes, instead, showed a consistent activation pattern throughout the task. The different activation patterns for the two suffix types may reflect distinct underlying neural processing mechanisms. Untrained suffixes with merely phonological information may be processed through procedural memory, while declarative memory could be involved in case more explicit, semantic information is attached to the novel morphemes.

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