Polarity adverbs facilitate predictive processing in L2 Japanese

Mitsugi, Sanako

doi:10.1177/02676583211000837

Cited by 5 publications

(7 citation statements)

References 66 publications

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“…Prevalent among the reported L1/L2 differences in prediction are differences in the time course of prediction. A later onset of prediction in L2 processing relative to L1 processing has been reported for the use of semantic information such as the lexicalsemantics of verbs (Chun & Kaan, 2019), the semantics of classifiers (Mitsugi, 2018), negative polarity adverbs (Mitsugi, 2022), the broader semantic context (Dijkgraaf, Hartsuiker & Duyck, 2019;, gender stereotypes (Corps, Liao & Pickering, in press), the use of morphosyntactic information such as number encoded at the verb (Koch, Bulté, Housen & Godfroid, 2021) as well as discourse-level information (Kim & Grüter, 2021). In addition to differences in the time course, several studies have attested weaker effects in L2 processing (e.g., Kim & Grüter, 2021;Schlenter & Felser, 2021).…”

Section: Quantitative Differencesmentioning

confidence: 87%

Prediction in bilingual sentence processing: How prediction differs in a later learned language from a first language

Schlenter

2022

Bilingualism

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This review provides an update on what we know about differences in prediction in a first and second language after several years of extensive research. It shows when L1/L2 differences are most likely to occur and provides an explanation as to why they occur. For example, L2 speakers may capitalize more on semantic information for prediction than L1 speakers, or possibly they do not make predictions due to differences in the weighting of cues. A different weighting of cues can be the result of prior experience from the L1 and/or the prior experience in an experiment which affects L1 and L2 processing to a different extent. Overall, prediction in L2 processing often emerges later and/or is weaker than in L1 processing. Because L2 processing is generally slower, L1/L2 differences are likely to occur at certain levels of prediction, most notably at the form level, in line with a prediction-by-production mechanism.

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Section: Quantitative Differencesmentioning

confidence: 87%

Prediction in bilingual sentence processing: How prediction differs in a later learned language from a first language

Schlenter

2022

Bilingualism

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“…3) Relying on the word frequency effects of Japanese two-character kanji type and two-character kanji "的" type adjectives, it is possible to clarify the correctness of Japanese learners' translation of adjectives in the Japanese translation process, and when the values of the twoword frequency effects are between [4,8], the word frequency effects of both types of adjectives can significantly predict Japanese learners' translation correctness. Analyzing the frequency of the two types of adjectives can help Japanese learners to understand the specific causes of translation bias and thus encourage Japanese learners to enhance the effectiveness of Japanese translation.…”

Section: Discussionmentioning

confidence: 99%

“…From the distribution of the scatter plot, the word frequency of two-character kanji adjectives used by the 60 Japanese learners was mostly distributed in the interval of [4,8], while the percentage of correct judgment of two-character kanji adjectives by the Japanese learners fluctuated from 68.74% to 82.18% at this time. From the results of linear regression analysis, the R 2 fit value of Japanese learners' correct judgment of two-character kanji adjectives is 0.048, which is only 5.88% lower than the R 2 fit value of word frequency of two-character kanji adjectives, and both of them show a strong and significant linear correlation at the 5% level.…”

Section: Word Frequency Effects In Adjective Translationmentioning

confidence: 97%

“…According to the scattered distribution in the figure, the frequency of using the two-character Chinese character "的" type adjectives is mainly concentrated in the interval [4,8], and the correct rate of Japanese learners in judging the two-character "的" type adjectives is between 48.59% and 72.07%.…”

Section: Word Frequency Effects In Adjective Translationmentioning

confidence: 99%

“…Japanese learners' translation biases mainly stem from an inadequate understanding of Japanese grammar, vocabulary, and expressions [4][5][6]. These biases include not only direct translation errors but also the neglect of linguistic nuances, such as inappropriate use of context, misuse of tone, and polite expressions.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Analysis of Japanese Learners’ Translation Bias Using Neurosemantic Analysis

Cao

2024

Applied Mathematics and Nonlinear Sciences

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In today’s increasingly frequent cultural exchanges between China and Japan, accurate and error-free Japanese translation has become an inevitable choice for cross-cultural communication. In this paper, based on twin neural network and attention mechanism, BiLSTM model is combined with sentence semantic similarity matching algorithm to construct a Japanese translation bias sentence semantic similarity model. The Japanese corpus data were collected and preprocessed by Python technology, and the Japanese translation corpus database was searched and counted using Wordsmith and AntConc tools. For the Japanese learners’ translation bias in the Japanese translation process, a comparative analysis was carried out in several aspects, such as end-of-sentence modal expressions, consecutive translations, and word frequency effects. The study results show that the difference in the frequency distribution of Japanese learners’ modal expressions is only 4.66% compared with that of native speakers of Japanese. Still, the difference between the two is significant at the 1% level, and the difference in the frequency of Japanese learners’ use of the modal expression “yes” is 56 sentences per 10,000 sentences. The frequency of Japanese learners’ use of successive expressions was 30.1 percentage points higher than that of native speakers. The neural semantic analysis method combined with the Japanese translation corpus can clarify the translation bias of Japanese learners in the process of Japanese translation, which can provide a reference for enhancing the translation quality of Japanese learning.

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Inferential evaluation and revision in L1 and L2 text comprehension: An eye movement study

Pérez,

Schmidt,

Tsimpli

2023

Bilingualism

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Text comprehension frequently demands the resolution of no longer plausible interpretations to build an accurate situation model, an ability that might be especially challenging during second language comprehension. Twenty-two native English speakers (L1) and twenty-two highly proficient non-native English speakers (L2) were presented with short narratives in English. Each text required the evaluation and revision of an initial prediction. Eye movements in the text and a comprehension sentence indicated less efficient performance in the L2 than in L1 comprehension, in both inferential evaluation and revision. Interestingly, these effects were determined by individual differences in inhibitory control and linguistic proficiency. Higher inhibitory control reduced the time rereading previous parts of the text (better evaluation) as well as revisiting the text before answering the sentence (better revision) in L2 comprehenders, whereas higher proficiency reduced the time in the sentence when the story was coherent, suggesting better general comprehension in both languages.

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Polarity adverbs facilitate predictive processing in L2 Japanese

Cited by 5 publications

References 66 publications

Prediction in bilingual sentence processing: How prediction differs in a later learned language from a first language

Prediction in bilingual sentence processing: How prediction differs in a later learned language from a first language

A Comparative Analysis of Japanese Learners’ Translation Bias Using Neurosemantic Analysis

Inferential evaluation and revision in L1 and L2 text comprehension: An eye movement study

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