初始期望对学习不对称性的阴影效应
Yinmei Ni1,2, Jingwei Sun1,3, Jian Li1,2
1School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
PLoS computational biology
|July 24, 2023
概括
最初的期望在强化学习 (RL) 中显著影响学习. 这项研究表明,默认值预期会导致预测错误和选择,影响个人如何从积极和消极反中学习.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 计算心理学 计算心理学
背景情况:
- 在复杂的信念更新中观察到积极性和乐观主义偏见.
- 在更简单的强化学习 (RL) 中关于学习不对称性的共识仍然难以达成.
- RL学习不对称性涉及对正负预测错误 (PE) 的差异敏感性.
研究的目的:
- 调查初始值预期是否影响强化学习 (RL) 中的学习不对称性.
- 为了确定预期默认值是否有偏差,预测错误计算和随后的选择.
- 测试一个包含不对称学习率和初始价值预期的模型.
主要方法:
- 进行了两项学习实验,使用不同的强化概率.
- 实验包括货币收益,损失和混合收益-损失环境.
- 测试了一个集成不对称学习率和初始值预期的计算模型.
主要成果:
- 结果始终支持拟议的模型.
- 发现初始价值预期会影响预测错误计算.
- 纳入初始期望的模型有效地解释了选择偏好.
结论:
- 最初的价值期望在价值更新和选择行为中起着至关重要的作用.
- 拟议的模型提供了一个更全面的了解在RL学习率不对称的RL.
- 了解最初的期望是解决RL学习不对称性研究中的争议的关键.
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