Verbal Symbols Support Concrete but Enable Abstract Concept Formation: Evidence From Brain‐Constrained Deep Neural Networks
Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for symbols (e.g., democracy). When we simulated the putative brain mechanisms of...
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| Vydáno v: | Language learning Ročník 74; číslo S1; s. 258 - 295 |
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01.06.2024
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| ISSN: | 0023-8333, 1467-9922 |
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| Abstract | Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms.
A one‐page Accessible Summary of this article in nontechnical language is freely available in the Supporting Information online and at https://oasis‐database.org. |
|---|---|
| AbstractList | Concrete symbols (e.g.,
sun
,
run
) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract symbols (e.g.,
democracy
). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of abstract concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and abstract meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, abstract concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms.
A one‐page
Accessible Summary
of this article in nontechnical language is freely available in the Supporting Information online and at
https://oasis‐database.org
. Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms. A one‐page Accessible Summary of this article in nontechnical language is freely available in the Supporting Information online and at https://oasis‐database.org. Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of abstract concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and abstract meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, abstract concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms. |
| Author | Dobler, Fynn R. Pulvermüller, Friedemann Henningsen‐Schomers, Malte R. |
| Author_xml | – sequence: 1 givenname: Fynn R. orcidid: 0000-0002-7118-0016 surname: Dobler fullname: Dobler, Fynn R. email: fynn.dobler@fu-berlin.de organization: Humboldt Universität zu Berlin – sequence: 2 givenname: Malte R. surname: Henningsen‐Schomers fullname: Henningsen‐Schomers, Malte R. organization: Freie Universität Berlin – sequence: 3 givenname: Friedemann surname: Pulvermüller fullname: Pulvermüller, Friedemann email: friedemann.pulvermuller@fu-berlin.de organization: Humboldt Universität zu Berlin |
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| CitedBy_id | crossref_primary_10_1016_j_pneurobio_2023_102511 crossref_primary_10_1007_s12144_025_08066_6 crossref_primary_10_1080_0954898X_2024_2421196 crossref_primary_10_1111_lang_12643 |
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| Notes | Malte R. Henningsen‐Schomers Accessible Summary Fynn R. Dobler https://oasis‐database.org CRediT author statement conceptualization; methodology; writing – original draft preparation; writing – review and editing; funding acquisition. conceptualization; methodology; writing – original draft preparation; writing – review and editing. Friedemann Pulvermüller The data that support the findings of this study and the analysis code are available via the Open Science Framework at https://osf.io/m8dg5 Supporting Information online and at . investigation; formal analysis; writing – original draft preparation; writing – review and editing. This work was supported by the European Research Council (ERC) through the Advanced Grant “Material constraints enabling human cognition, MatCo” (ERC‐2019‐ADG 883811) and by the Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy through the Cluster of Excellence “Matters of Activity. Image Space Material” (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. The authors declare no conflict of interest. of this article in nontechnical language is freely available in the bll_berlin Author Twitter information The handling editor for this manuscript was Guillaume Thierry. A one‐page ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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| Snippet | Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is... Concrete symbols (e.g., sun , run ) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is... |
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| SubjectTerms | abstract concepts Brain Brain mechanisms Circuits Concept formation Conceptual semantics deep neural networks Democracy grounding Learning Meaning Neural networks neurocomputational modeling Neurolinguistics Semantics Semiotics Symbolism Symbols |
| Title | Verbal Symbols Support Concrete but Enable Abstract Concept Formation: Evidence From Brain‐Constrained Deep Neural Networks |
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