作为感知行为理论的积极推断
Giovanni Pezzulo1, Thomas Parr2, Karl Friston3
1Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
Biological psychology
|January 5, 2024
概括
积极推断解释了感知行为,使用内部大脑模型进行预测和行动. 这篇评论探讨了它的历史,神经基础,以及人工智能和机器人的未来应用.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 积极推理提出,有意识的行为依赖于内部模型进行预测,推理和行动.
- 这个理论统一了对行动和感知的观点,从赫尔姆霍尔茨到当代模型的概念根源.
研究的目的:
- 提供积极推断的历史发展和未来轨迹的概述.
- 探索主动推理的概念基础,神经支和应用.
主要方法:
- 关于主动推理的历史和当代文献的综述.
- 检查概念演变,包括预测编码和分层生成模型.
- 讨论神经科学,精神病理学和新兴领域的应用.
主要成果:
- 积极推断通过内部大脑模型为理解行为和感知提供了一个统一的框架.
- 关键的发展包括预测编码,规划的顺序模型和层次的时间模型.
- 积极推断在解释神经生理学,精神病理学和统一心理理论方面具有应用.
结论:
- 积极推断为理解感知和大脑功能提供了一个强大的框架.
- 预计神经科学,机器人,机器学习和人工智能等领域的未来发展.
- 该理论的广泛适用性表明了超越生物系统的巨大潜力.
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