使用循环神经网络来估计人类选择行为中不可减少的随机性
Yoav Ger1, Moni Shahar2, Nitzan Shahar1,3
1Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
eLife
|September 6, 2024
概括
循环神经网络 (RNN) 有助于区分杂的决策和有缺陷的计算模型. 低智商参与者被发现比高智商参与者更杂,没有更多的模型错误指定.
科学领域:
- 认知科学是一种认知科学.
- 计算神经科学是一种计算神经科学.
- 决策科学 决策科学
背景情况:
- 理论计算模型解释认知过程,但不同个体的预测准确性各不相同.
- 对于一些参与者来说,存在一个显著的预测差距,需要对其来源进行调查.
研究的目的:
- 利用理论独立的循环神经网络 (RNN) 来区分杂的决策和理论模型错误规范.
- 评估行为数据可预测性的个体差异是否源于随机性或模型不足.
主要方法:
- 计算机模拟使用强化学习来训练RNN在具有控制噪声水平的模拟剂上.
- 分析预测性能和RNN培训时代 (早期停止) 以量化随机性.
- 将RNN方法应用于人类参与者执行两步任务的经验数据集,比较低智商和高智商组.
主要成果:
- 在模拟代理中,RNN成功识别了模型错误规范,预测性能和早期停止表明了随机性水平.
- 在实证数据集中,RNN分析表明所有参与者都存在类似水平的模型错误规范.
- 预测差距和RNN早期停止表明,低智商参与者的预测能力较低主要是由于决策噪声增加.
结论:
- 循环神经网络提供了一种方法来诊断认知计算模型中预测差距的来源.
- 低智商参与者表现出更杂的决策,而不是通过既定的理论模型更大的错误规范.
- 这些发现强调了数据驱动,理论独立的模型在补充决策科学中传统理论方法的实用性.
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