Abstract I propose a qualitative Bayesian account of explanatory goodness that is analogous to the incremental confirmation. This achieved by means complexity criterion according which an explanation h good if reduction in explanandum e brought about (the gain) greater than additional introduced context cost). To illustrate account, apply it ad hoc hypotheses.

We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, difference between psychopathology can be located in goodness model as a regulator action. What is crucial for avoiding rigid patterns thinking, feeling acting associated with regulation action based on valence affective states. In PPF, modelled error dynamics—the ch...