Recurrent connectivity supports higher-level visual and semantic object representations in the brain
Abstract Visual object recognition has been traditionally conceptualised as a predominantly feedforward process through the ventral visual pathway. While feedforward artificial neural networks (ANNs) can achieve human-level classification on some image-labelling tasks, it’s unclear whether computati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-023-05565-9 |
| _version_ | 1797414239278727168 |
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| author | Jacqueline von Seth Victoria I. Nicholls Lorraine K. Tyler Alex Clarke |
| author_facet | Jacqueline von Seth Victoria I. Nicholls Lorraine K. Tyler Alex Clarke |
| author_sort | Jacqueline von Seth |
| collection | DOAJ |
| description | Abstract Visual object recognition has been traditionally conceptualised as a predominantly feedforward process through the ventral visual pathway. While feedforward artificial neural networks (ANNs) can achieve human-level classification on some image-labelling tasks, it’s unclear whether computational models of vision alone can accurately capture the evolving spatiotemporal neural dynamics. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate to visual and semantic object properties. Critically, we show that recurrent processing between the anterior and posterior ventral temporal cortex relates to higher-level visual properties prior to semantic object properties, in addition to semantic-related feedback from the frontal lobe to the ventral temporal lobe between 250 and 500 ms after stimulus onset. These results demonstrate the distinct contributions made by semantic object properties in explaining neural activity and connectivity, highlighting it as a core part of object recognition not fully accounted for by current biologically inspired neural networks. |
| first_indexed | 2024-03-09T05:30:13Z |
| format | Article |
| id | doaj.art-6000471f611a4ca38e4d2c38b9c68c8a |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-09T05:30:13Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-6000471f611a4ca38e4d2c38b9c68c8a2023-12-03T12:33:15ZengNature PortfolioCommunications Biology2399-36422023-11-016111510.1038/s42003-023-05565-9Recurrent connectivity supports higher-level visual and semantic object representations in the brainJacqueline von Seth0Victoria I. Nicholls1Lorraine K. Tyler2Alex Clarke3MRC Cognition and Brain Sciences Unit, University of CambridgeDepartment of Psychology, University of CambridgeDepartment of Psychology, University of CambridgeDepartment of Psychology, University of CambridgeAbstract Visual object recognition has been traditionally conceptualised as a predominantly feedforward process through the ventral visual pathway. While feedforward artificial neural networks (ANNs) can achieve human-level classification on some image-labelling tasks, it’s unclear whether computational models of vision alone can accurately capture the evolving spatiotemporal neural dynamics. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate to visual and semantic object properties. Critically, we show that recurrent processing between the anterior and posterior ventral temporal cortex relates to higher-level visual properties prior to semantic object properties, in addition to semantic-related feedback from the frontal lobe to the ventral temporal lobe between 250 and 500 ms after stimulus onset. These results demonstrate the distinct contributions made by semantic object properties in explaining neural activity and connectivity, highlighting it as a core part of object recognition not fully accounted for by current biologically inspired neural networks.https://doi.org/10.1038/s42003-023-05565-9 |
| spellingShingle | Jacqueline von Seth Victoria I. Nicholls Lorraine K. Tyler Alex Clarke Recurrent connectivity supports higher-level visual and semantic object representations in the brain Communications Biology |
| title | Recurrent connectivity supports higher-level visual and semantic object representations in the brain |
| title_full | Recurrent connectivity supports higher-level visual and semantic object representations in the brain |
| title_fullStr | Recurrent connectivity supports higher-level visual and semantic object representations in the brain |
| title_full_unstemmed | Recurrent connectivity supports higher-level visual and semantic object representations in the brain |
| title_short | Recurrent connectivity supports higher-level visual and semantic object representations in the brain |
| title_sort | recurrent connectivity supports higher level visual and semantic object representations in the brain |
| url | https://doi.org/10.1038/s42003-023-05565-9 |
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