Growing Random Forests reveals that exposure and proficiency best account for individual variability in L2 (and L1) brain potentials for syntax and semantics

Fromont, Lauren A.; Royle, Phaedra; Steinhauer, Karsten

doi:10.1016/j.bandl.2020.104770

Cited by 19 publications

(33 citation statements)

References 104 publications

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“…Finally, our study showed that while N400 effects elicited by participants were not correlated across all experimental manipulations, individuals who elicited a P600 tended to do so in every condition. We suggest that this component reflects participants' ability to categorize between correct and unacceptable sentences: ongoing research from the same authors investigating online proficiency effects on ERP responses in native and second language speakers [70] will further address this issue.…”

Section: Discussionmentioning

confidence: 92%

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Verbing nouns and nouning verbs: Using a balanced design provides ERP evidence against “syntax-first” approaches to sentence processing

2020

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In this event-related potential (ERP) study we reevaluate syntax-first approaches to sentence processing by implementing a novel paradigm in French that includes correct sentences, pure syntactic category violations, lexical-semantic anomalies, and combined anomalies. Our balanced design systematically controlled for target word (noun vs. verb) and the context immediately preceding it. Group results from 36 native speakers of Quebec French revealed that, up to 300 ms, ERPs elicited by syntactic category violations were comparable with ERP responses to correct sentences, showing that there is no early activation reflecting syntactic category identification. Instead, in response to all anomalous conditions, we observed an N400 followed by a P600. Combined anomalies yielded additive effects of syntactic category and lexical-semantic anomalies on the N400, and a large P600 effect similar to the one observed in the pure syntactic condition. These results provide strong evidence against the hypothesis that (i) syntactic categories are processed first, and (ii) that syntactic category errors "block" lexical-semantic processing. Further, the N400 effect in response to pure syntactic category violations reflects a mismatch detection between a predicted word-stem and the actual target. This mechanism takes place simultaneously (and potentially in parallel) with lexical-semantic processing. In contrast, an interaction of syntax and semantics for the P600 reveals that the same neurocognitive resources are recruited for syntactic and semantic integration, both promoted by the implementation of an acceptability judgement task in our design. Additional analyses of individual data complemented these observations: during sentence processing, participants did not rely on one single cognitive mechanism reflected by either the N400 or the P600 effect but on both, suggesting that the biphasic N400-P600 ERP wave can indeed be considered to be an index of phrase-structure violations in most individuals, at least if they are realized on content words.

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Section: Discussionmentioning

confidence: 92%

“…Note that the variance explained by the mixed-effect models is quite small. Fromont and collaborators discuss this issue further and offer alternatives for ERP data analysis[70].https://doi.org/10.1371/journal.pone.0229169.t003…”

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confidence: 99%

Verbing nouns and nouning verbs: Using a balanced design provides ERP evidence against “syntax-first” approaches to sentence processing

2020

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“…This can help us understand how the system works. For example, Fromont et al (2020) illustrated the effect of individual variability by visualizing decision trees.…”

Section: Results Of Questionmentioning

confidence: 99%

“…Munsell et al (2019) applied machine learning algorithms to predict the performance of naming in temporal lobe epilepsy. Fromont et al (2020) applied random forests to model the individual data and found that language exposure and proficiency were the most important predictive variables. All in all, the neural network and ML algorithms may show a bright future in psycholinguistics and neurolinguistics.…”

Section: Psycholinguistics and Neurolinguisticsmentioning

confidence: 99%

A Methodological Review of Machine Learning in Applied Linguistics

Liu¹

2020

ELT

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The traditional linear regression in applied linguistics (AL) suffers from the drawbacks arising from the strict assumptions namely: linearity, and normality, etc. More advanced methods are needed to overcome the shortcomings of the traditional method and grapple with intricate linguistic problems. However, there is no previous review on the applications of machine learning (ML) in AL, the introduction of interpretable ML, and related practical software. This paper addresses these gaps by reviewing the representative algorithms of ML in AL. The result shows that ML is applicable in AL and enjoys a promising future. It goes further to discuss the applications of interpretable ML for reporting the results in AL. Finally, it ends with the recommendations of the practical programming languages, software, and platforms to implement ML for researchers in AL to foster the interdisciplinary studies between AL and ML.

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“…This can aid us in better understanding the functionality of the system. For example, by presenting decision trees, Fromont et al (2020) highlighted the impact of individual variability on the experiment's outcome.…”

Section: Teaching and Learning Phoneticsmentioning

confidence: 99%

The Uses of Machine Learning (ML) in Teaching and Learning English Language: A Methodical Review.

AlHarbi

2022

المجلة التربوية لکلية التربية بسوهاج

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Regular teaching methods for ESL/EFL may rely on assumptions that do not lead to the desired results in student learning. Consequently, the current study sought to perform a systematic review of the research that revealed numerous applications of machine learning (ML) in English language instruction (Applied Linguistics) and the development of interpretable machine learning or associated practical software. The current research employed a mixed-methods approach (approach) that included qualitative and quantitative analysis to survey the previous studies on machine learning in EFL/ESL instruction. Results showed that machine learning (ML) may teach English as a foreign language or as a second language and that it has a bright future in this sector. Interpretable machine learning (ML) was found to help in teaching many EFL/ESL topics such as dictation, phonetics instruction, language blogging, and all of which contribute to the facilitation of English language learning and teaching processes. Finally, the study concluded with recommendations for researchers in EFL or ESL on how to incorporate machine learning in English classrooms using functional programming languages, tools, and platforms. More studies were also advised to determine the most effective methods and procedures for incorporating machine learning (ML) into English as ESL/EFL in the classroom.

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Growing Random Forests reveals that exposure and proficiency best account for individual variability in L2 (and L1) brain potentials for syntax and semantics

Cited by 19 publications

References 104 publications

Verbing nouns and nouning verbs: Using a balanced design provides ERP evidence against “syntax-first” approaches to sentence processing

Verbing nouns and nouning verbs: Using a balanced design provides ERP evidence against “syntax-first” approaches to sentence processing

A Methodological Review of Machine Learning in Applied Linguistics

The Uses of Machine Learning (ML) in Teaching and Learning English Language: A Methodical Review.

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