Starting Big: The Effect of Unit Size on Language Learning in Children and Adults

Predictability and Variation in Language Are Differentially Affected by Learning and Production

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory-the component of short-term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well-documented process whereby languages become less variable (on some dimension) over time. This process has been argued to be driven by the behavior of individual language users, but the specific mechanism is not agreed upon. Here, we use an artificial language learning experiment to investigate whether limitations in working memory during either language learning or language production drive regularization behavior. We find that taxing working memory during production results in the loss of all types of variation, but the process by which random variation becomes more predictable is better explained by learning biases. A computational model offers a potential explanation for the production effect using a simple self-priming mechanism.

An Emotional Analysis Method for the Analysis of Cognitive and Psychological Factors in the Change of Second Language Learning Model of Chinese Mainland Students in the Post-epidemic Era

Since the sudden outbreak of the coronavirus disease 2019 (COVID-19) epidemic in 2020, the second language learning patterns of students in mainland China have encountered new challenges that have had a psychological impact on mainland Chinese students. The epidemic has not only inconvenienced students’ normal second language learning but also greatly affected the second language learning patterns of mainland Chinese students. In the post-epidemic era, more and more students are becoming accustomed to studying and learning a second language online. The level of informatization of second language learning patterns of students in mainland China has increased significantly. This study first analyses the mechanisms of change in second language learning patterns and further analyses the influence of knowledge background on the perception of second language learning patterns on this basis. To design the influencing factors of second language learning patterns, a questionnaire was used to investigate the influence of knowledge background on the perception of second language learning patterns. The survey was conducted on students who were learning a second language in mainland China. Then, the survey data were statistically analyzed. In analyzing the influence of effect on second language learning behaviors of students in mainland China, observed variables were designed, including observed variables of affective factors and learning behaviors. After that, the findings of the experiment were summarized based on the results of the questionnaire survey, and the positive influence of emotional factors on second language learning behaviors of mainland Chinese students in the post-development era was concluded.

The Starting Big approach to language learning

The study of language acquisition has a long and contentious history: researchers disagree on what drives this process, the relevant data, and the interesting questions. Here, I outline the Starting Big approach to language learning, which emphasizes the role of multiword units in language, and of coarse-to-fine processes in learning. I outline core predictions and supporting evidence. In short, the approach argues that multiword units are integral building blocks in language; that such units can facilitate mastery of semantically opaque relations between words; and that adults rely on them less than children, which can explain (some of) their difficulty in learning a second language. The Starting Big approach is a theory of how children learn language, how language is represented, and how to explain differences between first and second language learning. I discuss the learning and processing models at the heart of the approach and their cross-linguistic implications.

The Building Blocks of Child Bilingual Code-Mixing: A Cross-Corpus Traceback Approach

This paper offers an inductive, exploratory study on the role of input and individual differences in the early code-mixing of bilingual children. Drawing on data from two German-English bilingual children, aged 2–4, we use the traceback method to check whether their code-mixed utterances can be accounted for with the help of constructional patterns that can be found in their monolingual data and/or in their caregivers' input. In addition, we apply the traceback method to check whether the patterns used by one child can also be found in the input of the other child. Results show that patterns found in the code-mixed utterances could be traced back to the input the children receive, suggesting that children extract lexical knowledge from their environment. Additionally, tracing back patterns within each child was more successful than tracing back to the other child's corpus, indicating that each child has their own set of patterns which depends very much on their individual input. As such, these findings can shed new light on the interplay of the two developing grammars in bilingual children and their individual differences.