Simulating semantic dementia in a brain-constrained model of action and object words learning
Efremov, A.; Kuptsova, A.; Wennekers, T.; Shtyrov, Y.; Garagnani, M.; Gutkin, B.
Show abstract
The nature of semantic knowledge - conceptual information stored in the brain - is highly debated in the field of cognitive science. Experimental and clinical data specify various cortical regions involved in the processing of meaning. Those include semantic hubs that take part in semantic processing in general as well as sensorimotor areas that process specific conceptual categories according to their modality. Biologically inspired neurocomputational models can help adjudicate between different theories about the exact roles of those regions in the functioning of the semantic system. Here, we used an existing neuroanatomically constrained model of frontotemporal brain areas implicated in language acquisition and grounding. We adapted it to replicate and explain the effects of semantic dementia on word processing abilities. Semantic dementia is a disease characterized by semantic knowledge deterioration that correlates with neural damage in the anterior temporal lobe. The behavior of our model is in accordance with clinical data - namely, word recognition performance decreases as SD lesions progress, whereas word repetition abilities remain preserved, or are less affected. Furthermore, our model makes novel predictions about category-specific effects of SD - namely, our simulation results indicate that word processing should be more impaired for object-than for action-related words, and that white matter degradation should lead to more severe consequences than the same proportion of grey matter degradation. The present results provide a mechanistic, cortical-level explanatory account of a range of language impairments as observed in clinical populations during the onset and progress of semantic dementia.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A unified neurocomputational bilateral model of spoken language production in healthy participants and recovery in post-stroke aphasia 95%
- A hierarchy of processing complexity and timescales for natural sounds in human auditory cortex 93%
- Multilevel Development of Cognitive Abilities in an Artificial Neural Network 93%
Similar papers in this journal
- Multiscale Effects of Excitatory-Inhibitory Homeostasis in Lesioned Cortical Networks: A Computational Study 94%
- Realistic modeling of ephaptic fields in the human brain 93%
- Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.