作为主动推理的体现决策
Matteo Priorelli1,2, Ivilin Peev Stoianov1, Giovanni Pezzulo1
1Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
PLoS computational biology
|June 18, 2025
概括
嵌入式决策涉及并行处理和动作反,与传统的串行模型不同. 积极推断自然地通过平衡准确性和机会风险来解释这些复杂的现实选择.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 决策科学 决策科学
背景情况:
- 传统的决策模型将选择视为序列过程,在此过程中,证据的积累先于行动.
- 在动态环境中体现的决策与此形成对比,涉及并行感知-行动循环和与行动相关的反.
- 现有的模型很难在现实场景中捕捉动作动态和选择选择之间的相互作用.
研究的目的:
- 展示活跃推理如何自然地解释体内决策的特征.
- 调查运动规划和运动推理之间的反机制在体现的选择中.
- 探索在生态环境中体现的决策背后的规范原则.
主要方法:
- 利用主动推断,通过自由能量最小化统一感知和行动的框架.
- 开发模拟来建模运动规划和运动推理之间的连续反循环.
- 分析模拟结果以识别体现决策的新兴特性.
主要成果:
- 积极推论模拟重现了体现决策的关键签名,包括并行处理和行动反效应.
- 证明优化体现的选择需要在行动动态和信念更新之间持续的反循环.
- 确定了决策准确性和模拟生态环境中错过机会的风险之间的规范平衡.
结论:
- 积极推断为理解传统和体现的决策提供了一个统一的框架.
- 具体决策的特点是感知,行动计划和行动执行之间的动态相互作用.
- 该框架强调了平衡决策准确性与有效利用机会的适应性优势.
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