积极推断和认知控制:通过精确优化来平衡思考和习惯
Riccardo Proietti1, Thomas Parr2, Alessia Tessari3
1Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, Rome 00185, Italy.
Physics of life reviews
|May 27, 2025
概括
我们介绍了一种使用主动推理的新认知控制理论. 该模型优化了精度参数,以平衡故意和习惯性的行为,提高了情境敏感性和习惯暂停,以便更好地做出决策.
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
- 人工智能的人工智能
背景情况:
- 认知控制对于适应性行为至关重要,平衡故意和习惯性行动选择.
- 现有的主动推理模型与上下文敏感的控制斗争,特别是从习惯性回归到故意的行动.
研究的目的:
- 在主动推理框架内提出一种新的认知控制公式.
- 解决在情境敏感控制和习惯暂停中标准主动推理的局限性.
主要方法:
- 开发了一种具有元认知控制的等级主动推理模型.
- 模拟了一种驾驶场景,以测试适应性习惯形成和暂停.
- 模拟了多巴胺通路,ACC和位的神经生物学动态.
主要成果:
- 这种新型号成功地通过精确参数平衡了深思熟虑和习惯性控制.
- 超认知层面可以启动和暂停习惯,增强上下文敏感性.
- 关键的认知控制过程源于自由能量最小化原理.
结论:
- 层次主动推理为理解认知控制提供了一个统一的框架.
- 这种表述解释了习惯形成,暂停和上下文切换.
- 该模型为认知控制机制提供了一个神经生物学上可信的解释.
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