人类在执行回归和分类时优化了哪些损失函数?
Hansol X Ryu1,2, Manoj Srinivasan3
1Biomedical Engineering, University of Calgary.
bioRxiv : the preprint server for biology
|October 3, 2023
概括
人类根据数据密度来调整他们的数据分析策略. 对于稀疏的数据,他们最大限度地减少了更大的错误,而对于更密集的数据,他们在回归和分类任务中使用了异常值耐用的方法.
科学领域:
- 认知科学 认知科学
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 了解人类的数据感知对于数据驱动的决策至关重要.
- 视觉介导的感应运动控制依赖于数据中的模式识别.
研究的目的:
- 在基本的统计估计任务中调查人类表现:线性回归和二进制分类.
- 为了推断潜在的损失函数,人类在这些任务中使用反向优化进行优化.
主要方法:
- 实验旨在观察人类受试者在2D散射图上执行线性回归和二进制分类.
- 反向优化被用来确定最能模拟人类行为的损失函数.
主要成果:
- 在稀疏的数据上,人类回归性能最好通过将错误最小化至1.7.7的功率来描述.
- 对于更密集的数据集,损失函数对异常值 (较低的指数) 不那么敏感,更好地描述了人类回归.
- 在描述人类二进制分类选择时,物流损失函数优于指数损失函数.
结论:
- 人类对数据分析的策略随着数据密度的增加而发生变化.
- 观察到的趋势表明,人类决策过程中存在显著的主体间和主体内部的变化.
- 了解人类损失函数可以提高人与人交互的人工智能应用程序的设计.
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