Generative models as parsimonious descriptions of sensorimotor loops
This reframes generative models in neuroscience and AI from perceptual accuracy to behavioral utility, potentially impacting theories like the Bayesian brain.
The paper challenges the assumption that generative models must accurately represent the environment, proposing instead that they should describe sensorimotor relationships relevant for behavior.
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.