Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916415815024640 |
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| author | Xia, Runze Yin, Congchi Li, Piji |
| author_facet | Xia, Runze Yin, Congchi Li, Piji |
| contents | The human visual system is capable of processing continuous streams of visual information, but how the brain encodes and retrieves recent visual memories during continuous visual processing remains unexplored. This study investigates the capacity of working memory to retain past information under continuous visual stimuli. And then we propose a new task Memory Disentangling, which aims to extract and decode past information from fMRI signals. To address the issue of interference from past memory information, we design a disentangled contrastive learning method inspired by the phenomenon of proactive interference. This method separates the information between adjacent fMRI signals into current and past components and decodes them into image descriptions. Experimental results demonstrate that this method effectively disentangles the information within fMRI signals. This research could advance brain-computer interfaces and mitigate the problem of low temporal resolution in fMRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information Xia, Runze Yin, Congchi Li, Piji Computation and Language The human visual system is capable of processing continuous streams of visual information, but how the brain encodes and retrieves recent visual memories during continuous visual processing remains unexplored. This study investigates the capacity of working memory to retain past information under continuous visual stimuli. And then we propose a new task Memory Disentangling, which aims to extract and decode past information from fMRI signals. To address the issue of interference from past memory information, we design a disentangled contrastive learning method inspired by the phenomenon of proactive interference. This method separates the information between adjacent fMRI signals into current and past components and decodes them into image descriptions. Experimental results demonstrate that this method effectively disentangles the information within fMRI signals. This research could advance brain-computer interfaces and mitigate the problem of low temporal resolution in fMRI. |
| title | Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.20428 |