人类视觉皮层和深层卷积神经网络深入关注对象背景
Jessica Loke1, Noor Seijdel1, Lukas Snoek1
1University of Amsterdam.
Journal of cognitive neuroscience
|January 2, 2024
概括
深层卷积神经网络 (DCNNs) 和人类大脑在早期视觉阶段优先考虑对象背景处理而不是对象类别. 图形-地面分离是DCNN和视觉皮层对象识别的关键.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 认知科学 认知科学
背景情况:
- 深层卷积神经网络 (DCNNs) 在对象分类中部分预测大脑活动.
- 驱动DCNN预测能力的因素仍然不清楚.
- 图形与地面的分离对于人类对象识别至关重要.
研究的目的:
- 在对象分类中调查影响DCNN预测能力的因素.
- 确定图形-地面分离是否解释了DCNN预测大脑活动的能力.
- 将DCNN和人类EEG对物体背景的反应与类别进行比较.
主要方法:
- 将四个DCNN架构与62名人类参与者的EEG数据进行比较.
- 使用与相同的物体在各种背景的刺激来隔离背景影响.
- 分析了早期EEG活动 (<100毫秒) 和早期DCNN层.
主要成果:
- 早期的EEG活动和DCNN层的过程对象背景,而不是类别.
- 脑电图活动的DCNN预测是由背景处理驱动的.
- 与未经训练的网络相比,训练有素的DCNN显示出不同的激活,突出显示了图形-地面隔离的作用.
结论:
- 人类视觉皮层和DCNN都优先考虑对象分类的图形-地面分离.
- 对象背景处理是生物和人工视觉系统共享的基本机制.
- 图形-地面分离可能是对象特征识别的先决条件.
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