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计算机制 潜在的动机 赢得象征性增强器
Diana C Burk1, Craig Taswell1, Hua Tang1
1Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892-4415.
概括
子通过将它们与主要奖励联系起来,学会珍视象征性代币. 一个计算模型将任务特征与动机联系起来,解释了代币价值如何驱动行为并预测神经反应.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 行为经济学是一种行为经济学.
背景情况:
- 强化学习 (RL) 解释了代理人如何通过奖励和惩罚来学习.
- 象征性增强剂 (如代币) 是常见的,但它们的激励机制尚不清楚.
- 了解象征性奖励如何获得价值对于各种应用至关重要.
研究的目的:
- 调查子如何学会最大限度地利用代币作为象征性增强器来获得流体奖励.
- 模拟任务状态的价值如何受到多个特征的影响,驱动动力.
- 在基于令牌的任务中将计算状态值与行为指标相关联.
主要方法:
- 开发了一个马尔科夫决策过程 (MDP) 模型,根据任务特征计算状态值.
- 收集了来自5只子执行基于代币的奖励任务的行为数据.
- 分析固定时间,选择反应时间,以及与计算状态值相关的中断频率.
主要成果:
- 行为指标 (固定时间,反应时间,中断频率) 与模型衍生的状态值显著相关.
- MDP模型成功地捕捉了与象征性强化相关的学习和行为.
- 该模型为预测基于状态值的神经活动变化提供了一个框架.
结论:
- 象征性强化,就像代币一样,可以通过与主要奖励的关联获得价值来有效地激励行为.
- 状态值的计算建模是理解动机和决策的强大工具.
- 这项研究提供了关于神经和行为机制的见解,这些机制是象征性奖励学习的基础.
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