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计算机制是获得象征性强化器的基础动机
Diana C Burk1, Craig Taswell1, Hua Tang1
1Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda MD, 20892-4415.
bioRxiv : the preprint server for biology
|October 24, 2023
概括
子学会了选择获得代币的选项,这些代币后来被交换成流动奖励. 一个计算模型显示了任务特征如何影响他们的动机和选择.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 强化学习 (RL) 解释了代理人如何通过奖励和惩罚来学习.
- 符号增强器 (例如,代币) 激励选择,但其潜在的神经和计算机制尚未完全理解.
- 了解象征性奖励如何推动动机对于解释复杂的决策至关重要.
研究的目的:
- 为了研究子如何学会使用代币最大限度地获得流体奖励.
- 了解任务状态的价值如何受到各种特征的影响,驱动动机动和行为.
主要方法:
- 开发了马尔科夫决策过程模型,以基于任务特征计算状态值.
- 监控子行为 (固定时间,反应时间,中止频率) 在代币增强任务期间.
- 收集了5只子的数据.
主要成果:
- 子的行为,包括固定时间和反应时间,与计算的状态值显著相关.
- 该模型成功地捕获了与象征性强化相关的学习和行为.
- 该模型根据状态值预测神经反应的瞬间变化.
结论:
- 这项研究阐明了非人类灵长类动物中由象征性强化驱动的激励机制.
- 开发的计算模型为理解状态值如何影响行为提供了一个框架.
- 这项研究提供了关于涉及延迟满足的决策的神经和计算基础的见解.
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