Journal

Volume 26, Issue 1 (March 31, 2025)

3 articles

• Large Language Models and Human Cognition: An Optimistic Perspective
  by Antoine Blais
  J. CS. 2025, 26(1), 1-44;
  Abstract Large Language Models (LLMs) are deep learning-based text generation tools that have shown remarkable improvement in producing coherent discourse and displaying unexpected abilities. This article explores how LLMs can contribute to our understanding of human language and cognition. The author argues... [Read more].
  Abstract Large Language Models (LLMs) are deep learning-based text generation tools that have shown remarkable improvement in producing coherent discourse and displaying unexpected abilities. This article explores how LLMs can contribute to our understanding of human language and cognition. The author argues that viewing LLMs merely as word predictors that mimic human language behavior oversimplifies their underlying mechanisms and representational capabilities. LLMs function as information acquisition and processing systems and, from a connectionist perspective, can serve as useful models of human cognition – or at least of certain aspects of it. The article begins by providing a brief account of criticisms concerning LLMs’ limitations, followed by an examination of the nature of representations within these models and a discussion on their architectural components. It further presents LLMs as general-purpose systems, highlighting their emerging non-linguistic capabilities. It is suggested that LLMs may have the potential to capture and effectively apply cultural constructs, which are primarily conveyed through language and encapsulate useful 'programs' for addressing a variety of tasks. Additionally, the article briefly examines the possibility of top-down processing and consciousness in these models. Ultimately, the author proposes that current and future generations of LLMs can contribute to a deeper understanding of human language activity and cognition, encouraging cognitive scientists to view them as more than mere engineering tools designed to mimic human language. [Collapse]
• The Effect of Numerical Syntax on Number Comparison in Children
  by Joonkoo Park, Diego Guerrero, Jihyun Hwang and Rachael McCollum
  J. CS. 2025, 26(1), 45-79;
  Abstract While much is documented and theorized about how children acquire the meaning of small numbers expressed in simplex numerals (number words that cannot be decomposed further), relatively little is known about how children come to understand complex numerals (that are composed of simplex numerals). We... [Read more].
  Abstract While much is documented and theorized about how children acquire the meaning of small numbers expressed in simplex numerals (number words that cannot be decomposed further), relatively little is known about how children come to understand complex numerals (that are composed of simplex numerals). We aimed to investigate how children make sense of complex numerals by assessing how they compare large numbers. In a novel task, children compared the numerical values of two large numbers that differed only in one syntactic position (e.g., four hundred twenty chairs vs. six hundred twenty chairs). Together in two studies, with Korean- and English-speaking children, we demonstrate that children including those with limited counting fluency (e.g., not counting past 49 or 99) are able to compare large numbers in the hundreds and the hundred-thousands and that this ability correlates with their counting fluency. At the same time, children find it more difficult to compare two complex-numeral phrases (as the example above) than two simplex-numeral phrases with the same number of words (e.g., four little yellow chairs vs. six little yellow chairs). These results suggest that syntactic complexity of complex numerals influences children’s numerical thinking, which is consistent with the idea that children’s implicit understanding of numerical syntax contributes to building generative number concepts. [Collapse]
• Models of Metaphor Identification: Observations and Insights
  by Ahmed Alharbi
  J. CS. 2025, 26(1), 81-109;
  Abstract This paper will consider a number of recently offered procedures and models of metaphor identification in the literature. The purpose is to evaluate their possible significance for application to the detection of conceptual metaphors in corpora. It is on the basis of such an evaluation that a new co... [Read more].
  Abstract This paper will consider a number of recently offered procedures and models of metaphor identification in the literature. The purpose is to evaluate their possible significance for application to the detection of conceptual metaphors in corpora. It is on the basis of such an evaluation that a new corpus-based model of metaphor identification is proposed. Compared to most previous ones, this model goes beyond the mere act of tracking the surface linguistic forms of metaphors in discourse to identifying the underlying conceptual metaphors that give rise to them. In so doing, a combination of automatic and manual means is employed. The two together have proved to be efficient in accelerating the process of revealing the conceptual bases underpinning patterns of linguistic metaphors in a particular corpus of texts. The proposed model holds important empirical implications as it highlights how conceptual metaphors operate and can be identified in a discourse context. It can therefore be utilized in the development of a simplified and generalizable framework capable of facilitating the researcher’s ability to detect and analyze metaphorical language. [Collapse]