Malte R Henningsen-Schomers
Biographic Data
| ID | 9609138 |
|---|---|
| NAME | Malte R Henningsen-Schomers |
| GIVEN NAMES | Malte R |
| FAMILY NAME | Henningsen-Schomers |
| SIGNATURE | HENNINGSEN-SCHOMERS M R |
| AFFILIATIONS | Freie Universität Berlin |
| ORCID | 0000-0002-4506-1697 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Verbal Symbols Support Concrete but Enable Abstract Concept Formation
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 langua…
Modelling concrete and abstract concepts using brain-constrained deep neural networks
A neurobiologically constrained deep neural network mimicking cortical area function relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically ‘ground’ concrete and abstract concepts. Grounding set…
Verbal Symbols Support Concrete but Enable Abstract Concept Formation
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 langua…
Modelling concrete and abstract concepts using brain-constrained deep neural networks
A neurobiologically constrained deep neural network mimicking cortical area function relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically ‘ground’ concrete and abstract concepts. Grounding set…
Verbal Symbols Support Concrete but Enable Abstract Concept Formation
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 langua…
Action Observation and Synchronization (2 works) · Artificial Intelligence (2 works) · Artificial neural network (2 works) · Cognitive science (2 works) · Computer Science (2 works) · Concept learning (2 works) · Psychology (2 works) · Artificial Intelligence (1 work) · Biology (1 work) · Categorization, perception, and language (1 work)