Habitual and Reflective Control in Hierarchical Predictive Coding
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 34 references that are in the map. First posted on arXiv 2 Sep 2021.
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3 of 34 references.
References · 3
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Citing papers per year: 2022: 3.
3 papers.
- 2022Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?cited by 45
- 2022Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learningcited by 26
- 2022Preventing Deterioration of Classification Accuracy in Predictive Coding Networkscited by 5
- 2022Preventing Deterioration of Classification Accuracy in Predictive Coding Networks15 Aug 2022
- 2022Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning31 May 2022
- 2022Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?18 Feb 2022