Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…

We tested the frequent assumption that the difficulty of word retrieval increases when a speaker is being observed and evaluated. We modified the Trier Social Stress Test (TSST) so that participants believed that its evaluative observation components continued throughout the duration of a subsequent word retrieval task, and measured participants'…

The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called "Model Judge," focused toward the two main actors in the process…

Ambridge argues that the existence of exemplar models for individual phenomena (words, inflection rules, etc.) suggests the feasibility of a unified, exemplars-everywhere model that eschews abstraction. The argument would be strengthened by a description of such a model. However, none is provided. I show that any attempt to do so would immediately…

This study investigates the potential of ChatGPT, a cutting-edge large language model in generative artificial intelligence (AI), to support the teaching of dialogic pedagogy to preservice teachers. A workshop was conducted with 29 preservice teachers, wherein ChatGPT and another prominent AI model, Bert, were sequentially integrated to facilitate…

Multi-componential models of translation competence are widely used in translator training as a yardstick for curricular and syllabus design. These models must be adapted to reflect professional trends, such as the impact of artificial intelligence, and machine translation in particular, on working methods. This paper describes the process of…

With artificial intelligence (AI) now producing human-quality text in seconds via natural language generation, urgent questions arise about the nature and purpose of the teaching of writing in English. Humans have already been co-composing with digital tools for decades, in the form of spelling and grammar checkers built into word processing…

In this commentary, I suggest that it may be helpful to think about the formidable problem space that Westergaard's (2021) Linguistic Proximity Model seeks to address at the three levels of analysis that Marr (1982) famously proposed are needed to understand any complex cognitive system. I argue that at the computational level of analysis, where…

Sentiment Analysis is a field of Natural Language Processing which aims at classifying the author's sentiment in text. This paper first describes a sentiment analysis model for students' comments about professor performance. The model achieved impressive results for comments collected from student surveys conducted at a private university in…

Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge…

This paper explores recent advancements and implications of artificial intelligence (AI) technology, with a specific focus on Large Language Models (LLMs) like ChatGPT 3.5, within the realm of higher education. Through a comprehensive review of the academic literature, this paper highlights the unprecedented growth of these models and their…

The authors argue that Ambridge's radical exemplar account of language cannot clearly explain all syntactic priming evidence, such as inverse preference effects ("greater" priming for less frequent structures), and the contrast between short-lived lexical boost and long-lived abstract priming. Moreover, without recourse to a level of…

The radical exemplar model resonates with work on perceptual classification and categorization highlighting the role of exemplars in memory representations. Further development of the model requires acknowledgment of both the fleeting and fragile nature of perceptual representations and the gist-based, good-enough quality of long-term memory…

Chinese coordinative compound words are common and unique in inter-character semantic and orthographic relationships. This study explored the inter-character orthographic similarity effects on the recognition of transparent two-morpheme coordinative compound words. Seventy-two native Chinese readers participated in a lexical decision task. The…

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers,…