在主动推理中神经表示:使用生成模型与生活的世界互动并理解它
Giovanni Pezzulo1, Leo D'Amato1,2, Francesco Mannella1
1Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
Annals of the New York Academy of Sciences
|March 26, 2024
概括
生物在主动推理中使用生成模型来最大限度地减少预测错误,从而实现自适应导航,而不仅仅是理解. 这个框架将生成模型与神经表征和认知进化联系起来.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算生物学 计算生物学
背景情况:
- 积极推断为大脑功能提供了一个规范框架.
- 生物利用生成模型来最大限度地减少预测错误,由变量自由能量量化.
- 生成模型对于适应性行为和了解世界至关重要.
研究的目的:
- 通过积极推断的透视来探索神经表征.
- 分析生成模型在自适应导航和认知过程中的作用.
- 阐明生成模型,信念动态和神经表示之间的联系.
主要方法:
- 积极推断和生成模型的概念分析.
- 检查生物系统中的变量自由能量最小化.
- 探索不同类型的生成模型 (行动感知,规划,想象力).
主要成果:
- 生成模型是为了适应性导航而学习的,而不仅仅是为了理解.
- 不同的生成模型支持各种认知功能,从行动感知到计划.
- 生成模型和信念动态与神经表达和认知能力有关.
结论:
- 生成模型在认知和神经表征的演变中发挥着不同的作用.
- 生产模型的类型会影响一个代理在其生态内的认知能力.
- 关于生成模型和高级认知的演变,仍然存在一些悬而未决的问题.
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