5 research outputs found
Action prevents error : predictive processing without active inference
According to predictive processing, minds relentlessly aim at a single goal: prediction error minimization. Prediction error minimization is said to explain everything the mind does, from perception to cognition to action. Here I focus on action. ‘Active inference’ is the standard approach to action in predictive processing. According to active inference, as it has been developed by Friston and collaborators, action ensues when proprioceptive predictions generate prediction error at the motor periphery, and classical reflex arcs engage to quash the error. I raise a series of problems for active inference. I then offer an alternative approach on which action prevents error (APE), rather than quash it. I argue that the action prevents error approach solves all the problems raised for active inference. In addition, I show how the alternative approach can be independently motivated by further commitments of predictive processing and that it is compatible with other prominent approaches to sensorimotor psychology, such as optimal feedback control
Noise, uncertainty, and interest: Predictive coding and cognitive penetration
This paper concerns how extant theorists of predictive coding conceptualize and explain possible instances of cognitive penetration. §I offers brief clarification of the predictive coding framework and relevant mechanisms, and a brief characterization of cognitive penetration and some challenges that come with defining it. §II develops more precise ways that the predictive coding framework can explain, and of course thereby allow for, genuine top-down causal effects on perceptual experience, of the kind discussed in the context of cognitive penetration. §III develops these insights further with an eye towards tracking one extant criterion for cognitive penetration, namely, that the relevant cognitive effects on perception must be sufficiently direct. Throughout these discussions, we extend the analyses of the predictive coding models, as we know them. So one open question that surfaces is how much of the extended analyses are genuinely just part of the predictive coding models, or something that must be added to them in order to generate these additional explanatory benefits. In §IV, we analyze and criticize a claim made by some theorists of predictive coding, namely, that (interesting) instances of cognitive penetration tend to occur in perceptual circumstances involving substantial noise or uncertainty. It is here that our analysis is most critical. We argue that, when applied, the claim fails to explain (or perhaps even be consistent with) a large range of important and uncontroversially interesting possible cases of cognitive penetration. We conclude with a general speculation about how the recent work on the predictive mind may influence the current dialectic concerning top-down effects on perception