解码fMRI中的视觉回声：面向过往语义信息的记忆解缠
Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information
AI总结：
本文针对连续视觉刺激下工作记忆如何保留过往信息的问题，提出记忆解缠任务，并设计受前摄干扰启发的解缠对比学习方法，从相邻fMRI信号中分离当前与过往信息并解码为图像描述，实验证明其有效性，有望改善fMRI时间分辨率。
摘要：
人类视觉系统能够处理连续的视觉信息流，但大脑在连续视觉处理过程中如何编码和提取近期视觉记忆仍未得到探索。本研究考察了工作记忆在连续视觉刺激下保留过往信息的能力。随后，我们提出了一项新任务——记忆解缠（Memory Disentangling），旨在从fMRI信号中提取并解码过往信息。为解决过往记忆信息干扰的问题，我们受前摄干扰现象启发，设计了一种解缠对比学习方法。该方法将相邻fMRI信号之间的信息分离为当前成分和过往成分，并将其解码为图像描述。实验结果表明，该方法能够有效解缠fMRI信号中的信息。本研究有望推动脑机接口的发展，并缓解fMRI时间分辨率较低的问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
