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  1.  10
    Generative Models as Parsimonious Descriptions of Sensorimotor Loops.Manuel Baltieri & Christopher L. Buckley - 2019 - Behavioral and Brain Sciences 42.
    The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.
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  2.  16
    The Emperor Is Naked: Replies to Commentaries on the Target Article.Jelle Bruineberg, Krzysztof Dołęga, Joe Dewhurst & Manuel Baltieri - 2022 - Behavioral and Brain Sciences 45.
    The 35 commentaries cover a wide range of topics and take many different stances on the issues explored by the target article. We have organised our response to the commentaries around three central questions: Are Friston blankets just Pearl blankets? What ontological and metaphysical commitments are implied by the use of Friston blankets? What kind of explanatory work are Friston blankets capable of? We conclude our reply with a short critical reflection on the indiscriminate use of both Markov blankets and (...)
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    Embodied Skillful Performance: Where the Action Is.Inês Hipólito, Manuel Baltieri, Karl Friston & Maxwell J. D. Ramstead - 2021 - Synthese 199 (1-2):4457-4481.
    When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory, as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed—a motor command. This paper aims (...)
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