Habitual and Reflective Control in Hierarchical Predictive Coding
This addresses a theoretical issue in cognitive science by offering a unified framework for behavior types, but it is incremental as it builds on existing HPC models.
The paper tackles the problem of explaining both habitual and reflective behaviors in cognitive science by proposing that Hierarchical Predictive Coding (HPC) can model them as a continuum in a multi-layered network, eliminating the need for separate brain circuits, and demonstrates that HPC distributes learning across its hierarchy with higher layers used only when necessary.
In cognitive science, behaviour is often separated into two types. Reflexive control is habitual and immediate, whereas reflective is deliberative and time consuming. We examine the argument that Hierarchical Predictive Coding (HPC) can explain both types of behaviour as a continuum operating across a multi-layered network, removing the need for separate circuits in the brain. On this view, "fast" actions may be triggered using only the lower layers of the HPC schema, whereas more deliberative actions need higher layers. We demonstrate that HPC can distribute learning throughout its hierarchy, with higher layers called into use only as required.