Compositionality has been a central concept in linguistics and philosophy for decades, and it is increasingly prominent in many other areas of cognitive science. Its status, however, remains contentious. Here, I reassess the nature and scope of the principle of compositionality (Partee, 1995) from the perspective of psycholinguistics and cognitive neuroscience. First, I review classic arguments for compositionality and conclude that they fail to establish compositionality as a property of human language. Next, I state a new competence argument, acknowledging the fact that any competent user of a language L can assign to most expressions in L at least one meaning which is a function only of the meanings of the expression's parts and of its syntactic structure. I then discuss selected results from cognitive neuroscience, indicating that the human brain possesses the processing capacities presupposed by the competence argument. Finally, I outline a language processing architecture consistent with the neuroscience results, where semantic representations may be generated by a syntax-driven stream and by an "asyntactic" processing stream, jointly or independently. Compositionality is viewed as a constraint on computation in the former stream only.
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Sci Rep
December 2024
Department of Dermatology, Niazi Hospital, Lahore, Pakistan.
With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.
View Article and Find Full Text PDFNat Commun
December 2024
LEADS group, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands.
Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures.
View Article and Find Full Text PDFSignal Transduct Target Ther
December 2024
Department of Orthopedic Surgery/Sports Medicine Center, Southwest Hospital, Army Medical University, Chongqing, 400038, China.
Metabolites can double as a signaling modality that initiates physiological adaptations. Metabolism, a chemical language encoding biological information, has been recognized as a powerful principle directing inflammatory responses. Cytosolic pH is a regulator of inflammatory response in macrophages.
View Article and Find Full Text PDFEcol Lett
January 2025
Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Jerusalem, Israel.
Modelling the dynamics of biological processes is ubiquitous across the ecological and evolutionary disciplines. However, the increasing complexity of these models poses a challenge to the dissemination of model-derived results. Often only a small subset of model results are made available to the scientific community, with further exploration of the parameter space relying on local deployment of code supplied by the authors.
View Article and Find Full Text PDFFront Public Health
December 2024
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Objective: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.
Methods: Using X as the data source, we extracted tweets containing #long-covid, #long_covid, or "long covid," posted from May 2020 to April 2023. We then conducted an unsupervised deep learning analysis using Bidirectional Encoder Representations from Transformers (BERT).
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