Self-Knowledge in a Predictive Processing Framework

Abstract

In this paper I propose an account of self-knowledge based on a framework of predictive processing. Predictive processing understands the brain as a prediction-action machine that tries to minimize error in its predictions about the world. For this view to evolve into a complete account of human cognition we ought to provide an idea how it can account for self-knowledge – knowledge of one's own mental states. I provide an attempt for such an account starting from remarks on introspection made by Hohwy (2013). I develop Hohwy's picture into a general model for knowledge of one's mental states, discussing how predictions about oneself can be used to capture self-knowledge. I further explore empirical predictions, and thereby argue that the model provides a good explanation for failure of self-knowledge in cases involving motor aftereffects, such as the broken escalator phenomenon. I conclude that the proposed account is incomplete, but provides a valuable first step to connect research on predictive processing with the epistemology of self-knowledge.