用神经网络逆转认知模型,从定位推断出偏好
Evan M Russek1, Frederick Callaway2,3, Thomas L Griffiths1,4
1Department of Computer Science, Princeton University.
Cognitive science
|November 6, 2024
概括
这项研究引入了一种新的方法,将认知模型和神经网络结合起来,从行为中推断出人类的偏好,使用模拟数据来提高准确性,并在做出选择之前实现预测.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 从行为中推断人类偏好对于辅助决策技术至关重要.
- 机器学习,特别是神经网络,需要大量的数据进行训练,这对偏好推断构成了挑战.
研究的目的:
- 开发一种使用认知模型模拟数据来增强有限的人类数据来训练神经网络的方法.
- 从可观察到的行为,如眼睛运动,推断个人偏好,特别是对食品的价值.
主要方法:
- 一个认知模型生成模拟的行为数据 (眼动),以补充有限的人类数据集.
- 训练了一个神经网络来逆转认知模型,学习从模拟和真实行为数据中推断偏好.
主要成果:
- 神经网络成功地从模拟的固定数据中推断出潜在的偏好.
- 模拟数据预训练网络,从真实眼睛跟踪数据中改进预测人类报告的偏好.
结论:
- 将神经网络与模型模拟的训练数据相结合,是开发偏好推断技术的有希望的策略.
- 这种方法在偏好预测准确度上提供了轻微的改进,并且可以在选择完成之前进行预测.
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