[论文解读] Bayesian Analogical Cybernetics

Adam Safron|arXiv (Cornell University)|Nov 4, 2019

Embodied and Extended Cognition参考文献 75被引用 9

一句话总结

本文提出贝叶斯推理与类比推理在本质上是相容的，类比认知自然地从控制论框架下预测编码机制中浮现。通过将认知建立在具身的动作-感知循环和概率生成模型之上，作者表明类比结构映射可通过分层贝叶斯网络中的信念传播来实现，为心智提供了一个统一的解释：即一种根植于自由能原理的具身、自组织控制系统。

ABSTRACT

It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy is the core of cognition. Here I will propose that these perspectives are fully compatible, in that analogical reasoning can be described in terms of Bayesian inference and vice versa, and that both of these positions require a thorough cybernetic grounding in order to fulfill their promise as unifying frameworks for understanding minds. From the Bayesian perspective of the Free Energy Principle and Active Inference framework, thought is constituted by dynamics of cascading belief propagation through the nodes of probabilistic generative models specified by a cortical heterarchy "rooted" in action-perception cycles that ground the mind as an embodied control system for an autonomous agent. From the analogical structure mapping perspective, thought is constituted by the alignment and comparison of heterogeneous structural representations. Here I will propose that this core cognitive process for analogical reasoning is naturally implemented by predictive coding mechanisms. However, both Bayesian cognitive science and models of cognitive development via analogical reasoning require rich base domains and priors (or reliably learnable posteriors) from which they can commence the process of bootstrapping minds. Here in the spirit of the work of George Lakoff and Mark Johnson, I propose that embodiment provides many of the inductive biases that are usually described in terms of innate core knowledge. (Please note: this manuscript was written and finalized in 2012.)

研究动机与目标

• 调和贝叶斯认知科学与类比推理作为认知核心的矛盾。
• 证明类比结构映射可通过分层生成模型中的预测编码来实现。
• 论证具身性为认知发展的自举过程提供了必要的归纳偏置。
• 将贝叶斯推理与类比认知共同建立在动作-感知循环的控制论框架之中。
• 表明自由能原理为信念传播与类比对齐提供了统一机制。

提出的方法

• 提出类比推理通过分层生成模型中作为皮层异序结构的贝叶斯网络中的级联信念传播来实现。
• 应用自由能原理，将认知框架化为具身自主代理的主动推理。
• 将类比结构映射建模为通过预测编码机制对异质表征进行对齐。
• 通过具身先验将乔治·莱考夫与马克·约翰逊的概念隐喻理论与贝叶斯认知科学相整合。
• 将动作-感知循环作为生成模型的基础‘根’，以实现自组织认知。
• 将学习框架化为从具身经验中获取可靠后验分布的过程，从而减少对先天核心知识的依赖。

实验结果

研究问题

• RQ1 类比推理如何在贝叶斯推理与预测编码的形式化框架中得到根基？
• RQ2 动作-感知循环在何种意义上构成了认知生成模型的基础？
• RQ3 具身性如何提供支持认知自举的归纳偏置？
• RQ4 自由能原理能否统一贝叶斯推理与类比结构映射？
• RQ5 生成模型在实现跨异质领域类比对齐中发挥何种作用？

主要发现

• 类比推理自然地通过建立在动作-感知循环之上的分层贝叶斯网络中的信念传播来实现。
• 自由能原理为预测编码与类比结构映射提供了统一机制。
• 具身性提供了必要的归纳偏置，从而减少了认知发展中对先天核心知识的依赖。
• 类比与推理等认知过程源于皮层异序结构中信念的级联更新。
• 该框架通过从具身经验中可靠学习到的后验分布支持认知的自举过程。
• 将类比结构映射与预测编码相整合，为认知提供了一个全面的解释：即具身的、自组织的控制过程。