分布式双流程模型预测了在不确定性下决策中的战略转变
Mianzhi Hu1, Hilary J Don2,3, Darrell A Worthy2
1Texas A&M University, College Station, TX, USA. rudolfhu@tamu.edu.
Communications psychology
|April 14, 2025
概括
人类的决策通过改变策略来适应不确定性. 我们新的双流程模型捕捉了这种从价值转向基于频率的学习的转变,优于传统的强化学习模型.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 行为经济学是一种行为经济学.
背景情况:
- 在不确定性下的人类决策是复杂的,涉及适应性的战略转变.
- 传统的强化学习 (RL) 模型往往无法捕捉到这些动态的战略变化.
- 了解个人如何应对不确定性对于解释适应性行为至关重要.
研究的目的:
- 提出一种权重的双流程模型,将基于价值和基于频率的决策策略整合起来.
- 研究不确定性如何影响人类决策中的战略选择.
- 将拟议模型的性能与传统RL模型进行比较.
主要方法:
- 开发了一个基于分布的模型,使用迪里克莱特和多变量高斯分布结合并行评估系统.
- 在不同程度的不确定性和奖励差异下模拟模型行为.
- 与人类参与者进行经验测试,以验证模型预测.
主要成果:
- 拟议的双流程模型成功地捕捉了参与者从基于价值的学习到基于频率的学习的战略转变.
- 增加的奖励差异导致参与者优先考虑奖励频率而不是实际奖励价值.
- 与传统的RL模型相比,该模型在解释适应性战略变化方面表现优越.
结论:
- 增加的不确定性促使个人采用补偿性评估方法,包括使用奖励频率作为价值的代理.
- 权重双流程模型为研究复杂环境中的多系统决策提供了一个强大的框架.
- 这项研究推动了我们对在不确定的多变量环境中适应性人类行为的理解.
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