ArticleScientific reports2018
Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Cognitive and neural mechanisms of mental imagery supporting creative cognition.Communications biology · 2025Article
- Object fine-grained discrimination as a sensitive cognitive marker of transentorhinal integrity.Communications biology · 2025Article
- Article
- Fine-Grained Concreteness Effects on Word Processing and Representation Across Three Tasks: An ERP Study.Psychophysiology · 2025Article
- The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Application of the artificial intelligence system based on graphics and vision in ethnic tourism of subtropical grasslands.Heliyon · 2024Article
- Visual Recognition Memory of Scenes Is Driven by Categorical, Not Sensory, Visual Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Using deep neural networks to disentangle visual and semantic information in human perception and memory.Nature human behaviour · 2024Article
- Representational formats of human memory traces.Brain structure & function · 2024Review
- Representation of event and object concepts in ventral anterior temporal lobe and angular gyrus.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Hippocampal Functions Modulate Transfer-Appropriate Cortical Representations Supporting Subsequent Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Representational similarity learning reveals a graded multidimensional semantic space in the human anterior temporal cortex.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Recurrent connectivity supports higher-level visual and semantic object representations in the brain.Communications biology · 2023Article
- Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- A neural network account of memory replay and knowledge consolidation.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- The Spatiotemporal Neural Dynamics of Intersensory Attention Capture of Salient Stimuli: A Large-Scale Auditory-Visual Modeling Study.Frontiers in computational neuroscience · 2022Article
- On the similarities of representations in artificial and brain neural networks for speech recognition.Frontiers in computational neuroscience · 2022Article
- Perceptual and Semantic Representations at Encoding Contribute to True and False Recognition of Objects.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Age-related dedifferentiation and hyperdifferentiation of perceptual and mnemonic representations.Neurobiology of aging · 2021Article
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3 authors.
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Abstract
Recognising an object involves rapid visual processing and activation of semantic knowledge about the object, but how visual processing activates and interacts with semantic representations remains unclear. Cognitive neuroscience research has shown that while visual processing involves posterior regions along the ventral stream, object meaning involves more anterior regions, especially perirhinal cortex. Here we investigate visuo-semantic processing by combining a deep neural network model of vision with an attractor network model of semantics, such that visual information maps onto object meanings represented as activation patterns across features. In the combined model, concept activation is driven by visual input and co-occurrence of semantic features, consistent with neurocognitive accounts. We tested the model's ability to explain fMRI data where participants named objects. Visual layers explained activation patterns in early visual cortex, whereas pattern-information in perirhinal cortex was best explained by later stages of the attractor network, when detailed semantic representations are activated. Posterior ventral temporal cortex was best explained by intermediate stages corresponding to initial semantic processing, when visual information has the greatest influence on the emerging semantic representation. These results provide proof of principle of how a mechanistic model of combined visuo-semantic processing can account for pattern-information in the ventral stream.
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