Influence of language on perception and concept formation in a brain-constrained deep neural network model

Henningsen-Schomers, Malte R.; Garagnani, Max and Pulvermüller, Friedemann. 2023. Influence of language on perception and concept formation in a brain-constrained deep neural network model. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1870), 20210373. ISSN 0962-8436 [Article]

HenningsenSchomersEtAl2022.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract or Description

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.

Item Type:


Identification Number (DOI):

Additional Information:

The datasets supporting this article are available at:

This work was supported by the European Research Council (ERC) through the Advanced Grant ‘Material constraints enabling human cognition, MatCo’ (grant no. ERC-2019-ADG 883811) and under Germany's Excellence Strategy through the Cluster of Excellence ‘Matters of Activity. Image Space Material’ (grant no. DFG EXC 2025/1–390648296). We would like to thank the high-performance computing service of Freie Universität Berlin and Martin Freyer and Phillip Krause for technical support.


concepts, linguistic relativity, cognition, Hebbian learning, neurocomputational modelling, deep neural networks

Departments, Centres and Research Units:



8 September 2022Accepted
26 December 2022Published Online
February 2023Published

Item ID:


Date Deposited:

27 Sep 2022 08:56

Last Modified:

12 Jan 2023 11:17

Peer Reviewed:

Yes, this version has been peer-reviewed.


View statistics for this item...

Edit Record Edit Record (login required)