RTify:将深度神经网络与人类行为决策对齐
Yu-Ang Cheng1, Ivan Felipe Rodriguez1, Sixuan Chen1
1Brown University.
ArXiv
|January 7, 2025
概括
这项研究引入了一种新的计算框架,用于在视觉任务中使用循环神经网络 (RNN) 建模人类反应时间. 该方法将RNN动态与人类行为结合起来,提高视觉模型的准确性和速度.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知科学是一种认知科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 目前的灵长类视觉神经网络模型经常忽视感知决策的动态性质,主要关注行为准确性.
- 模拟人类选择的时间动态,如反应时间,对于理解视觉感知至关重要.
研究的目的:
- 引入一种新的计算框架,用于在视觉任务中建模人类行为选择的动态.
- 为了使循环神经网络 (RNN) 的时间动态与人类反应时间 (RT) 保持一致.
- 为了优化一个理想的观察者RNN模型,在没有人类数据的情况下进行速度准确性权衡.
主要方法:
- 开发了一个近似方法来限制基于人类RT的RNN时间步骤.
- 对心理物理实验的框架进行了评估.
- 训练了Wong-Wang决策模型的深度学习实现,与卷积神经网络 (CNN) 集成.
主要成果:
- 该框架成功地将RNN的时间动态与人类RTs结合起来.
- 优化的理想观察者RNN模型证明了对人类RT数据的良好考虑.
- 综合的CNN-Wong-Wang模型显示了人工和自然图像刺激的有效性.
结论:
- 这种新的框架有效地将当前的视觉模型与人类行为保持一致.
- 这种方法促进了人类视觉综合模型的发展.
- 该方法允许优化速度准确性权衡的决策模型.
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