1 Predictive Processing and Representation: How Less Can Be More Thomas van Es & Erik Myin Centre for Philosophical Psychology Univerity of Antwerp Penultimate version of a manuscript destined for an anthology on Predictive Processing, edited by Steven Gouveia. Please only cite the published version Abstract: The ambitious, mathematically elegant unificatory proposal of Predictive Processing (PP) to account for perception and action seems to have taken the world by storm. Though many different varieties of PP may be distinguished, most of them adhere to representationalism in one form or another. In this paper, we inquire into these representational foundations. We argue that PP is best understood in a non-representational way. We argue that the most popular way of construing representational content in PP, despite pretensions to the contrary, proliferates representations unacceptably. Next we show that PP’s explanatory potential can be retained without positing representations. We thus show that PP can’t have and doesn’t need representations to do its explanatory work, and conclude that our efforts are better placed in furthering the programme of non-representational PP. Keywords: Anti-representationalism, covariation, representational content, Embedded View. 1. Introduction Attempts to explain cognitive phenomena can be representational or non-representational. Representationalist approaches to all or some cognitive phenomena have been criticized for a few decades (see for example Gibson 1979; Varela et al. 1991; Hutto and Myin 2013 2017; Di Paolo et al. 2017). Nonetheless, wide-ranging representationalism continues to thrive as the mainstream position in cognitive science. In this paper, we investigate whether this is because the issues have been solved, with particular focus on predictive processing (PP). We argue that, despite significant effort, PP still can’t have the representations it lays claim on. Further, we will defend that the relevant explanatory work can be done without representations. This shows that PP gains from a non-representational interpretation. PP, in short, is a theoretical framework that places cognition, action and perception under a single banner of prediction error minimization (Hohwy 2013; Clark 2016). The