The master's thesis studies predictive models through the prism of modern enactivism. The central theme of the paper is exploring the relationship between predictive coding, predictive processing and enactivism and also verifying the explanatory effectiveness of predictive engagement as an enactivist predictive model. The first part presents the key components of the existing predictive models: the free energy principle, Bayesian reasoning, active reasoning, and Markov blankets. The first part also presents the historical background of predictive models and the enactivist approach in philosophy. In the second part, the master's thesis offers a broad philosophical overview of three predictive models: the predictive coding model, the predictive processing model, and the predictive engagement model. In doing so, the relationship between mind, body and world in each of the mentioned models is taken under scrutiny. In the second part of the paper, I examinate the hypothesis that predictive engagement is an explanatorily effective model that successfully links predictive models with enactivism, and that predictive engagement is a useful approach to exploring the problem of the relationship between mind, body and environment. In the chapter on predictive engagement, this hypothesis is supported by a set of relevant neuroscientific studies. In addition, the chapter on predictive engagement supports the thesis that the principle of free energy and the concept of autopoiesis are not mutually exclusive, but complementary. It also highlights the advantages of predictive engagement over classical predictive models and thus studies the possibilities of replacing or upgrading classical predictive models with enactivist predictive models.
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