# Generative models as parsimonious descriptions of sensorimotor loops

arXiv: https://arxiv.org/abs/1904.12937 · HTML: https://mlsanity.com/paper/1904.12937/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 11 of 11)

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- [Hierarchical generative modelling for autonomous robots](https://mlsanity.com/paper/2308.07775/) (2023)
- [Dissociating Artificial Intelligence from Artificial Consciousness](https://mlsanity.com/paper/2412.04571/) (2024)
- [Habitual and Reflective Control in Hierarchical Predictive Coding](https://mlsanity.com/paper/2109.00866/) (2021)
- [AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability](https://mlsanity.com/paper/2504.04608/) (2025)
- [Rejecting Cognitivism: Computational Phenomenology for Deep Learning](https://mlsanity.com/paper/2302.09071/) (2023)
- [A brain basis of dynamical intelligence for AI and computational neuroscience](https://mlsanity.com/paper/2105.07284/) (2021)
- [The Representational Status of Deep Learning Models](https://mlsanity.com/paper/2303.12032/) (2023)
- [Turing's cascade instability supports the coordination of the mind, brain, and behavior](https://mlsanity.com/paper/2204.07904/) (2022)

## Abstract (arXiv)

The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.