ML: Predictive Mind

FI2_W02 FI2_W10 FI2_W11 FI2_U03 FI2_U06 FI2_U07 FI2_U08 FI2_U13 FI2_K08

The aim of the classes is the general introduction to the selected problems of the predictive processing framework. According to this account perception and higher cognition is built on the basis of hypotheses (predictions) related to the causal structure of the world. These hypotheses provide a top-down way of organizing bottom-up sensory input originating with the senses. They are conditioned, on the one hand, by the internal world model of a given cognitive system, and on the other, by changeable information coming from the world.

The aim of the classes is the general introduction to the selected problems of the predictive processing framework. According to this account perception and higher cognition is built on the basis of hypotheses (predictions) related to the causal structure of the world. These hypotheses provide a top-down way of organizing bottom-up sensory input originating with the senses. They are conditioned, on the one hand, by the internal world model of a given cognitive system, and on the other, by changeable information coming from the world. The two layers are mutually restrictive and dynamically interdependent (Hohwy 2013: 69-70). Drawing upon the knowledge about causal relations existing in the world, the mind makes hypotheses about the probability of certain events. It can thus reduce the so-called prediction error. ). Minimization of prediction errors is a fundamental function of the brain (multilevel and hierarchically organized generative model ) because all perception serves the aim of ensuring that the organism functions efficiently in the environment. The generative model generates predictions which impose a top-down structure on the bottom-up flow of information from the sensory input. Information in the model is processed in two directions: top-down (predictions about information reaching the model) and bottom-up (information about potential prediction errors). This means that each level of the model (which processes information) predicts (generates predictions) about what is happening at the level below, while at the same time receiving information about the magnitude of the predictive error. The model is effective when predictions generated at a higher level lead to minimization of prediction errors occurring at lower levels. Effective prediction error minimization presupposes a degree of precision. Precision weighing allows to determine to what extent a given error is precise, i.e. whether the information it carries is reliable for the system or not. The more precision the system attributes to a given predictive error, the smaller the error (Friston 2010).

Anderson ,M. L., Chemero, T. (2013). The problem with brain GUTs: conflation of different senses of “prediction’’ threatens metaphysical disaster. Behavioral and Brain Sciences, 36 (3), 204–205.

Bruineberg, J., Kiverstein, J., Rietveld, E. (2016). The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective. Synthese, 1-28. doi: 10.1007/s11229-016-1239-1.

Clark, A. (2013b). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–204. doi: 10.1017/S0140525X12000477.

Clark, A. (2015a). Radical predictive processing. The Southern Journal of Philosophy, 53 (S1), 3–27.

Clark, A. (2016). Surfing Uncertainty. Prediction, Action and the Embodied Mind. Oxford: Oxford University Press.

Friston, K. (2010). The free-energy principle:a unified brain theory? Nat. Rev. Neurosci 11, 127–138.

Gładziejewski P (2016) Predictive coding and representationalism. Synthese 193:559–582. doi:10.1007/ s11229-015-0762-9

Gregory, R. L. (1980). Perceptions as hypotheses. Phil. Trans. R. Soc. Lond., Series B, Biological Sciences, 290, 181-197.

Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.

Hohwy, J. (2016). The self-evidencing brain. Noûs, 50(2), 259-285.

Orlandi, N. (2016). Bayesian perception is ecological perception. Philosophical Topics, 44(2), 327-351.

Orlandi, N. (2017). Predictive perceptual systems. Synthese, 1-20. doi: 10.1007/s11229-017-1373-4.

Swanson, L. R. (2016). The predictive processing paradigm has roots in Kant. Frontiers in Systems Neuroscience, 10, 79. doi: 10.3389/fnsys.2016.00079.

Wiese, W. (2016). What are the contents of representations in predictive processing? Phenomenology and the Cognitive Sciences, 1-22. doi: 10.1007/s11097-016- 9472-0.

Williams, D. (2018). Predictive coding and thought. Synthese, 1–27. doi: 10.1007/s11229-018-1768-x.

Zahavi, D. (2018). Brain, Mind, World: Predictive Coding, Neo-Kantianism, and Transcendental Idealism. Husserl Studies 34, 47-61. doi: 10.1007/s10743-017-9218-z.

• Knowledge: student knows the basis of the theory of predictive coding, its terminology and concepts. He recognizes the basic philosophical problems concerning the theory of predictive coding. He understands the specific relation between philosophy and cognitive science.

• Abilities: student single-handedly reads and understands the more philosophical articles concerning theory of predictive coding.

• Expertise: student efficiently organizes his work.

Lecture with elements of the conservatory. Joint reading of texts and discussion.

• 2 - student doesn't know the foundations of the theory of predictive coding. He doesn't recognize the most important thesis and problems concerning this conception.

• 3 - student badly knows the foundations of the theory of predictive coding. He has a problem with recognize the most important thesis and problems concerning this conception.

• 4 - student knows the foundations of the theory of predictive coding. He recognizes the most important thesis and problems concerning this conception.

• 5 - student very good knows the foundations of the theory of predictive coding. He recognizes the most important thesis and problems concerning this conception, and he can use this conception into philosophical discussion.

Exam - Oral Exam and/or test

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