预测性处理:阐明了动机行为背后的计算过程
Lieke L F van Lieshout1, Zhaoqi Zhang1, Karl J Friston2
1Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlandslieke.vanlieshout@donders.ru.nl claire.zhang@donders.ru.nl harold.bekkering@donders.ru.nlhttps://www.ru.nl/en/people/lieshout-l-vanhttps://www.ru.nl/en/people/zhang-z-clairehttps://www.ru.nl/en/people/bekkering-h.
这项研究将预测处理与动机相结合,将预期的自由能量分解为内在 (认识系统) 和外在 (工具) 值,以解释适应性行为和环境相互作用.
科学领域:
- 计算神经科学是一种神经科学.
- 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:
- 动机研究传统上侧重于驱动和奖励.
- 预测处理为理解感知和行动提供了一个计算框架.
- 弥合这些领域可以阐明动机行为机制.
研究的目的:
- 将预测处理框架与动机理论相结合.
- 通过分解预期的自由能量,以计算方式建模动机行为.
- 探索内在和外在价值观如何指导环境相互作用.
主要方法:
- 预期的自由能量分解为其组成价值组件.
- 预测性处理原理应用于动机构造.
- 适应性行为的理论建模.
主要成果:
- 预期的自由能量可以有意义地分解为认识和工具性负担.
- 这种分解为理解内在和外在动机提供了计算基础.
- 该框架提供了关于个人如何适应性地与环境互动的见解.
结论:
- 预测处理框架为理解动机提供了一个强大的镜头.
- 将预期的自由能量分解为内在和外在的价值推进了行为计算模型.
- 这种方法提高了我们对适应性环境参与的理解.
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