Neural correlates for nouns and verbs in phrases during syntactic and semantic processing: An fMRI study

Feng, Shuang; Qi, Ruonan; Yang, Jing; Yu, Anya; Yang, Yujiu

doi:10.1016/j.jneuroling.2019.100860

Cited by 29 publications

(18 citation statements)

References 41 publications

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“…Third, these categories seem to be generally present cross-linguistically. However, we are well aware that these categories are not necessarily universal (Feng et al, 2020 ), and definitely not homogeneous. The verb category is an ideal example of that: verbs can be divided in numerous subcategories for which children have some sensitivity, for example 1-participant action verbs vs. 2-participants action verbs (Yuan and Fisher, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model

et al. 2021

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While many studies have shown that toddlers are able to detect syntactic regularities in speech, the learning mechanism allowing them to do this is still largely unclear. In this article, we use computational modeling to assess the plausibility of a context-based learning mechanism for the acquisition of nouns and verbs. We hypothesize that infants can assign basic semantic features, such as “is-an-object” and/or “is-an-action,” to the very first words they learn, then use these words, the semantic seed, to ground proto-categories of nouns and verbs. The contexts in which these words occur, would then be exploited to bootstrap the noun and verb categories: unknown words are attributed to the class that has been observed most frequently in the corresponding context. To test our hypothesis, we designed a series of computational experiments which used French corpora of child-directed speech and different sizes of semantic seed. We partitioned these corpora in training and test sets: the model extracted the two-word contexts of the seed from the training sets, then used them to predict the syntactic category of content words from the test sets. This very simple algorithm demonstrated to be highly efficient in a categorization task: even the smallest semantic seed (only 8 nouns and 1 verb known) yields a very high precision (~90% of new nouns; ~80% of new verbs). Recall, in contrast, was low for small seeds, and increased with the seed size. Interestingly, we observed that the contexts used most often by the model featured function words, which is in line with what we know about infants' language development. Crucially, for the learning method we evaluated here, all initialization hypotheses are plausible and fit the developmental literature (semantic seed and ability to analyse contexts). While this experiment cannot prove that this learning mechanism is indeed used by infants, it demonstrates the feasibility of a realistic learning hypothesis, by using an algorithm that relies on very little computational and memory resources. Altogether, this supports the idea that a probabilistic, context-based mechanism can be very efficient for the acquisition of syntactic categories in infants.

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Section: Discussionmentioning

confidence: 99%

The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model

et al. 2021

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“…One clear limitation of this study relates to the semantic processing of unaccusative verbs and unergative verbs. Semantic processing is an integral component of language processing [62][63][64]. The present study is devoted to the syntactic processing of unaccusative and unergative verbs, but the two verb classes' semantic features are different [5,65].…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

Brain Regions Involved in Underlying Syntactic Processing of Mandarin Chinese Intransitive Verbs: An fMRI Study

et al. 2021

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According to the Unaccusative Hypothesis, intransitive verbs are divided into unaccusative and unergative ones based on the distinction of their syntactic properties, which has been proved by previous theoretical and empirical evidence. However, debate has been raised regarding whether intransitive verbs in Mandarin Chinese can be split into unaccusative and unergative ones syntactically. To analyze this theoretical controversy, the present study employed functional magnetic resonance imaging to compare the neural processing of deep unaccusative, unergative sentences, and passive sentences (derived structures undergoing a syntactic movement) in Mandarin Chinese. The results revealed no significant difference in the neural processing of deep unaccusative and unergative sentences, and the comparisons between passive sentences and the other sentence types revealed activation in the left superior temporal gyrus (LSTG) and the left middle frontal gyrus (LMFG). These findings indicate that the syntactic processing of unaccusative and unergative verbs in Mandarin Chinese is highly similar but different from that of passive verbs, which suggests that deep unaccusative and unergative sentences in Mandarin Chinese are both base-generated structures and that there is no syntactic distinction between unaccusative and unergative verbs in Mandarin Chinese.

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“…Historically, the dissociation of the information by the human brain was observed when comparing Broca's aphasic agrammatical patients, whose speech involves the use of very few verbs in contrast with other anomic patients that had great difficulty finding concrete nouns. Initially, the major difficulty with verbs for Broca's patients was interpreted based on the highest syntactical complexity of verbs compared to nouns [31][32][33]. However, the idea that verbs are, in general, harder to produce has been undermined in other studies where it is indicated that patients with anomic difficulties produce verbs more easily than nouns [34,35].…”

Section: Conceptual Framework 12 Object-action Dissociation/integrationmentioning

confidence: 99%

An Abstract System for Converting and Recovering Texts like Structured Information

Verdesoto¹,

Herrera²,

Ortiz

2021

Preprint

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This paper introduces an abstract system for converting texts into structured information. The proposed architecture incorporates several strategies based on scientific models of how the brain records and recovers memories, and approaches that convert texts into structured data. The applications of this proposal are vast because, in general, the information that can be expressed like a text way, such as reports, emails, web contents, etc., is considered unstructured and, hence, the repositories based on a SQL do not capable to deal efficiently with this kind of data. The model in which was based on this proposal divides a sentence into clusters of words which in turn are transformed into members of a taxonomy of algebraic structures. The algebraic structures must comply properties of Abelian groups. Methodologically, an incremental prototyping approach has been applied to develop a satisfactory architecture that can be adapted to any language. A special case is studied, this deals with the Spanish language. The developed abstract system is a framework that permits to implements applications that convert unstructured textual information to structured information, this can be useful in contexts such as Natural Language Generation, Data Mining, dynamically generation of theories, among others.

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Neural correlates for nouns and verbs in phrases during syntactic and semantic processing: An fMRI study

Cited by 29 publications

References 41 publications

The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model

The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model

Brain Regions Involved in Underlying Syntactic Processing of Mandarin Chinese Intransitive Verbs: An fMRI Study

An Abstract System for Converting and Recovering Texts like Structured Information

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