Imaginative Constraints and Generative Models

How can imagination generate knowledge when its contents are voluntarily determined? Several philosophers have recently answered this question by pointing to the constraints that underpin imagination when it plays knowledge-generating roles. Nevertheless, little has been said about the nature of these constraints. In this paper, I argue that the constraints that underpin sensory imagination come from the structure of causal probabilistic generative models, a construct that has been highly influential in recent cognitive science and machine learning. I highlight several attractions of this account, and I favourably contrast it with Peter Langland-Hassan's account of sensory imagination in terms of the forward models exploited in sensorimotor control.

Automated summary

This paper discusses how knowledge can be generated when the contents of imagination are voluntarily determined. It focuses on the nature of the constraints that underpin sensory imagination, arguing that they come from the structure of causal probabilistic generative models. The paper also contrasts this view with Peter Langland-Hassan's account of sensory imagination in terms of forward models in sensorimotor control.