LAUSR

One of the most interesting philosophical aspects of predictive processing (PP) is the normativity of predictive mechanisms and its function as a guide of action. In my opinion this framework provides us with good tools to describe and explain the phenomenon of normativity. It is possible to justify the thesis that explanations in the PP approach are normative in nature. They are like that because predictive mechanisms themselves are normative. By normative function of prediction I understand a feature of prediction which is constitutive (Bickhard, 2003) for action control as well as for the structure and content of the world model that is internal to a given cognitive system. They are normative in the sense of possibly being wrong (Bickhard, 2015a,b, 2016). Normative are also some properties of the environment. Both those factors are crucial for content and truth-value of representations. With no normativity, there is no error and it is hard to explain the possibility of misrepresentations. It means that predictions are also normative for action because they can be true (more probably in the Bayesian manner) or false (less probably in the Bayesian manner).