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Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway
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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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URL: https://arro.anglia.ac.uk/id/eprint/703524/ https://doi.org/10.1038/s41598-018-28865-1 https://arro.anglia.ac.uk/id/eprint/703524/1/Devereux_et_al_2018.pdf
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Oscillatory Dynamics of Perceptual to Conceptual Transformations in the Ventral Visual Pathway
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Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway
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Phonological and syntactic competition effects in spoken word recognition: evidence from corpus-based statistics. ...
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Phonological and syntactic competition effects in spoken word recognition: evidence from corpus-based statistics.
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Phonological and syntactic competition effects in spoken word recognition: evidence from corpus-based statistics
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Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing.
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Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing
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The Centre for Speech, Language and the Brain (CSLB) concept property norms. ...
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The Centre for Speech, Language and the Brain (CSLB) concept property norms.
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Representational Similarity Analysis Reveals Commonalities and Differences in the Semantic Processing of Words and Objects
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Objects and categories: Feature statistics and object processing in the ventral stream
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Syntactic computations in the language network: characterizing dynamic network properties using representational similarity analysis.
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Syntactic Computations in the Language Network: Characterizing Dynamic Network Properties Using Representational Similarity Analysis
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The Centre for Speech, Language and the Brain (CSLB) concept property norms
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Contrasting effects of feature-based statistics on the categorisation and identification of visual objects
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