ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2023
Influence of language on perception and concept formation in a brain-constrained deep neural network model.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework.Cognitive neurodynamics · 2026Article
- What is so hard about abstract words, anyways? A neuromechanistic explanation using brain-constrained neural network models.Cognitive neurodynamics · 2026Article
- Article
- From Spontaneous Ignitions to Sensorimotor Cell Assemblies via Dopamine: A Spiking Neurocomputational Model of Infants' Hand Action Acquisition.Brain sciences · 2026Article
- From cortical and white matter structure to meaning: a brain-constrained neural network of semantic grounding in action and perception.Frontiers in human neuroscience · 2026Article
- Testing the Labeling Effect in Autistic Children.Journal of autism and developmental disorders · 2025Article
- Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025Article
- On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper.Cognitive neurodynamics · 2024Article
- Causal Influence of Linguistic Learning on Perceptual and Conceptual Processing: A Brain-Constrained Deep Neural Network Study of Proper Names and Category Terms.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia.Scientific reports · 2023Article
- Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks.Progress in neurobiology · 2023Review
- Editorial concepts in interaction: social engagement and inner experiences.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A neurobiologically constrained model of semantic learning in the human brain was used to simulate the acquisition of concrete and abstract concepts, either with or without verbal labels. Concept acquisition and semantic learning were simulated using Hebbian learning mechanisms. We measured the network's category learning performance, defined as the extent to which it successfully (i) grouped partly overlapping perceptual instances into a single (abstract or concrete) conceptual representation, while (ii) still distinguishing representations for distinct concepts. Co-presence of linguistic labels with perceptual instances of a given concept generally improved the network's learning of categories, with a significantly larger beneficial effect for abstract than concrete concepts. These results offer a neurobiological explanation for causal effects of language structure on concept formation and on perceptuo-motor processing of instances of these concepts: supplying a verbal label during concept acquisition improves the cortical mechanisms by which experiences with objects and actions along with the learning of words lead to the formation of neuronal ensembles for specific concepts and meanings. Furthermore, the present results make a novel prediction, namely, that such 'Whorfian' effects should be modulated by the concreteness/abstractness of the semantic categories being acquired, with language labels supporting the learning of abstract concepts more than that of concrete ones. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.