Part of speech feature is the representation between syntactic morphology and semantic category. Priming effect experiment can test the correlation between the parts of speech feature and the lexical processing process. This article puts forward part of speech representation paradigmatic and syntagmatic effect hypotheses. The experiments applied the design pattern of 3 ∗ 2 ∗ 3. Subjects are requested to make options of the part of speech of the target words. This study shows that when Chinese English learners extract an (...) individual word, their choices are still influenced by part of speech factor without the restrictions of constructive syntagmatic semantic conditions. Both the syntagmatic and paradigmatic effects are verified. Part of speech priming effect intensity is influenced by the English acquisition levels of the subjects. (shrink)

Primary progressive aphasia is a clinical syndrome in which patients progressively lose speech and language abilities. Three variants are recognized: logopenic, associated with phonology and/or short-term verbal memory deficits accompanied by left temporo-parietal atrophy; semantic, associated with semantic deficits and anterior temporal lobe atrophy; non-fluent associated with grammar and/or speech-motor deficits and inferior frontal gyrus atrophy. Here, we set out to investigate whether the three variants of PPA can be dissociated based on error patterns in a single language task. We (...) recruited 21 lvPPA, 28 svPPA, and 24 nfvPPA patients, together with 31 healthy controls, and analyzed their performance on an auditory noun-to-verb generation task, which requires auditory analysis of the input, access to and selection of relevant lexical and semantic knowledge, as well as preparation and execution of speech. Task accuracy differed across the three variants and controls, with lvPPA and nfvPPA having the lowest and highest accuracy, respectively. Critically, machine learning analysis of the different error types yielded above-chance classification of patients into their corresponding group. An analysis of the error types revealed clear variant-specific effects: lvPPA patients produced the highest percentage of “not-a-verb” responses and the highest number of semantically related nouns ; in contrast, svPPA patients produced the highest percentage of “unrelated verb” responses and the highest number of light verbs. Taken together, our findings indicate that error patterns in an auditory verb generation task are associated with the breakdown of different neurocognitive mechanisms across PPA variants. Specifically, they corroborate the link between temporo-parietal regions with lexical processing, as well as ATL with semantic processes. These findings illustrate how the analysis of pattern of responses can help PPA phenotyping and heighten diagnostic sensitivity, while providing insights on the neural correlates of different components of language. (shrink)

Language learning requires linguistic input, but several studies have found that knowledge of second language rules does not seem to improve with more language exposure. One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni-Komshian, and Liu's study of L2 learners using rule-related predictors and found that, in addition to the overall drop in performance due to a sensitive period, (...) L2 knowledge increased with years of input. Knowledge of different grammar rules was negatively associated with input frequency of those rules. To better understand these effects, we modeled the results using a connectionist model that was trained using Korean as a first language and then English as an L2. To explain the sensitive period in L2 learning, the model's learning rate was reduced in an age-related manner. By assigning different learning rates for syntax and lexical learning, we were able to model the difference between early and late L2 learners in input sensitivity. The model's learning mechanism allowed transfer between the L1 and L2, and this helped to explain the differences between different rules in the grammaticality judgment task. This work demonstrates that an L1 model of learning and processing can be adapted to provide an explicit account of how the input and the sensitive period interact in L2 learning. (shrink)