在Feedforward卷积神经网络中出现了类似人类的隐蔽注意力
Sudhanshu Srivastava1, William Yang Wang2, Miguel P Eckstein3
1Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Current biology : CB
|January 20, 2024
概括
一个针对准确度优化的卷积神经网络 (CNN) 学习了类似人类的隐藏注意力行为,而不需要明确的编程. 这些发现表明,注意力特征可能来自于神经网络中优化任务性能.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 隐蔽注意力可以在没有眼动的情况下进行视觉选择,其好处往往是由有限的资源解释的.
- 现有的注意力理论很难与神经元人口活动相协调.
- 了解注意力的神经基础对于解释感知和认知至关重要.
研究的目的:
- 为了调查类似于人类的隐蔽注意行为是否可以在没有明确的注意机制的情况下在前卷积神经网络 (CNN) 中出现.
- 为了确定是否仅仅优化目标检测准确性可以复制注意力暗示和上下文效应.
- 探索这些发现对注意力理论及其在更简单的生物体中的存在的影响.
主要方法:
- 在图像数据集上训练了CNN的前,以优化目标检测的准确性.
- 在标准的秘密注意力任务 (波斯纳提示,设置大小,上下文提示) 中,CNN被评估其利用预测线索和上下文的能力.
- 将CNN的表现与人类准确性数据和贝叶斯的理想观察者模型进行比较,并对不同训练方案和提示类型进行分析.
主要成果:
- 美国有线电视新闻网成功地学会了利用线索和背景,在秘密注意力任务中反映人类准确性模式.
- 这些暗示和上下文效应是强大的,在训练变异,暗示类型和任务措施中概括.
- 即使网络资源减少并将其推广到新的提示实例时,CNN的影响仍然存在.
结论:
- 隐蔽注意的类似人类行为特征可能是优化神经元群体任务准确性的新兴属性.
- 有限的注意力资源可能不必解释这些注意力现象.
- 这些发现为在缺乏新皮质的生物体中观察到的注意力效应提供了潜在的解释.
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