一个大规模检查的诱导偏见塑造大脑和机器的高水平视觉表现
Colin Conwell1, Jacob S Prince2, Kendrick N Kay3
1Department of Psychology, Harvard University, Cambridge, MA, USA. conwell@g.harvard.edu.
Nature communications
|October 31, 2024
概括
计算机视觉模型显示出类似的大脑预测能力,无论建筑或任务. 训练数据,或"视觉训练饮食",显著影响模型与大脑表征的协调程度,挑战人工智能和神经科学研究中的假设.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 高性能计算机视觉模型为研究与大脑对齐的表示提供了新的途径.
- 了解影响人工系统中新兴大脑对齐的因素至关重要.
研究的目的:
- 系统地比较各种计算机视觉模型,并评估它们对视觉大脑预测能力的影响.
- 确定与生物视觉系统保持一致的关键模型属性和训练因素.
主要方法:
- 对224种不同的计算机视觉模型进行了受控比较.
- 利用了超过18亿次回归和503万次表示相似性分析.
- 将架构和任务目标等因素保持不变以隔离效应.
主要成果:
- 模型架构 (CNNs与变压器) 和任务目标在控制时对大脑预测能力的影响最小.
- 视觉训练数据的变化 ("视觉训练饮食") 对大脑预测能力产生了最大和最一致的影响.
- 许多模型实现了高大脑预测能力,尽管不同的内部表示,表明方法的灵活性.
结论:
- 视觉训练数据是大脑对齐的更关键因素,而不是模型架构或任务目标.
- 目前将计算模型与大脑数据联系起来的方法可能过于灵活.
- 控制模型比较可以揭示生物和人工视觉系统之间的共享计算原则.
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