人类大脑中的高层次视觉表现与大型语言模型保持一致
Adrien Doerig1,2,3, Tim C Kietzmann2, Emily Allen4,5
1Department of Psychology and Education, Freie Universität Berlin, Berlin, Germany.
Nature machine intelligence
|August 22, 2025
概括
大型语言模型可以模拟人类大脑如何处理来自自然场景的复杂视觉信息. 嵌入场景字幕的LLM精确地绘制和重建大脑活动, 揭示视觉感知.
科学领域:
- 认知神经科学
- 人工智能
- 计算机视觉
背景情况:
- 人类大脑从自然场景中处理复杂的视觉信息,
- 目前还没有一种定量方法来研究大脑中复杂的视觉信息处理.
研究的目的:
- 调查大语言模型 (LLM) 的上下文信息是否可以帮助大脑从自然场景中提取复杂的视觉信息.
- 确定LLM嵌入场景字幕是否可以描述和预测大脑活动模式.
主要方法:
- 现场标题的LLM嵌入被用来模拟通过观看自然场景引起的大脑活动.
- 为了评估LLM综合信息处理的贡献,进行了模型比较.
- 深度神经网络被训练到将图像输入映射到LLM表示中.
主要成果:
- 嵌入场景字幕的LLM成功地描述了大脑活动,使得从神经数据中准确地重建场景字幕.
- LLM脑图的准确性源于LLM在单个单词之外整合复杂信息的能力.
- 经过训练的深度神经网络与最先进的模型相比,尽管训练数据有限,但与大脑表示实现了更高的对齐.
结论:
- 现场标题的LLM嵌入为理解大脑中复杂的视觉信息处理提供了有价值的表示格式.
- 这种方法为研究视觉认知及其神经基础提供了定量框架.
- 这些发现突显了人工智能模型在神经科学研究中解码大脑表示的潜力.
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