Thornton Chris
Centre for Research in Cognitive Science, University of Sussex, Brighton BN1 9QJ, UK.
Brain Cogn. 2017 Mar;112:13-24. doi: 10.1016/j.bandc.2016.03.004. Epub 2016 Apr 18.
On a traditional view of cognition, we see the agent acquiring stimuli, interpreting these in some way, and producing behavior in response. An increasingly popular alternative is the predictive processing framework. This sees the agent as continually generating predictions about the world, and responding productively to any errors made. Partly because of its heritage in the Bayesian brain theory, predictive processing has generally been seen as an inherently Bayesian process. The 'hierarchical prediction machine' which mediates it is envisaged to be a specifically Bayesian device. But as this paper shows, a specification for this machine can also be derived directly from information theory, using the metric of predictive payoff as an organizing concept. Hierarchical prediction machines can be built along purely information-theoretic lines, without referencing Bayesian theory in any way; this simplifies the account to some degree. The present paper describes what is involved and presents a series of working models. An experiment involving the conversion of a Braitenberg vehicle to use a controller of this type is also described.
在传统的认知观点中，我们看到主体获取刺激，以某种方式对其进行解释，并产生相应的行为。一种越来越流行的替代观点是预测处理框架。这种观点认为主体不断地对世界进行预测，并对所犯的任何错误做出有效的反应。部分由于其在贝叶斯大脑理论中的传承，预测处理通常被视为一个内在的贝叶斯过程。介导这一过程的“层次预测机器”被设想为一种特定的贝叶斯装置。但正如本文所示，也可以直接从信息论中导出这台机器的规范，使用预测收益度量作为组织概念。层次预测机器可以完全按照信息论的思路构建，而无需以任何方式参考贝叶斯理论；这在一定程度上简化了描述。本文描述了其中涉及的内容，并展示了一系列工作模型。还描述了一个涉及将布赖滕贝格车辆转换为使用这种类型控制器的实验。
