From mind to life - contemporary discussion WF-FI-123-WMSFT-P21

Knowledge:

1. the student knows and understands the historical character of the emergence and use of Bayesian models;

2. the student knows the ideas and arguments used by the supporters of the use of Bayesian models;

3. the student understands and recognizes the problems related to the use of Bayesian models in science and philosophy.

Skills:

1. the student reads and interprets philosophical texts on Bayesian models;

2. the student sees and recognizes the philosophical problems associated with the use of Bayesian models.

Competences:

1. the student knows the scope of his knowledge in the field of Bayesian philosophy;

2. the student understands the need for continuous learning and development in the field of philosophical issues presented during classes

ECTS [1 ECTS = 30 (25) hours]:

participation in the lecture: 0-30 hours

reading of texts: 30-60 hours

preparation for the exam: 60-90 hours

Total hours (average): 120 [120/30 (25) = 4]

Number of ECTS: 4

Oral exam based on the lectures and recommended reading material.

The final grade is the weighted average of the grade for attendance (1/3), preparation for classes, knowledge of the ordered reading and preparation of the paper (1/3) and the grade for the final exam (1/3).

n/a

Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471–517.

Bowers, J. S., Davis, C. J. (2012). Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138(3), 389–414. https://doi.org/10.1037/a0026450.

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

Colombo, M., Elkin, E., Hartmann, S. (2018). Being realist about Bayes and the predictive processing theory of mind. The British Journal for the Philosophy of Science, axy059, 1–32. https://doi.org/10.1093/bjps/axy059.

Elqayam, S., Evans, J. S. (2011). Subtracting ,,ought” from ,,is”: Descriptivism versus normativism in the study of human thinking. Behavioral and Brain Sciences, 34(5), 233–248. https://doi.org/10.1017/S0140525X1100001X.

Fink, S. B., Zednik, C. (2017). Meeting in the dark room: Bayesian rational analysis and hierarchical predictive coding. W: T. Metzinger, W. Wiese (eds.), Philosophy and Predictive Processing, 8, 1–13. Frankfurt am Main: MI ND Group. https://doi.org/10.15502/9783958573154.

Friston, K. J. (2012). A free energy principle for biological systems. Entropy, 14, 2100–2121. https://doi.org/10.3390/e14112100.

Friston, K. J., FitzGerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G. (2017). Active inference: A process theory. Neural Computation, 29(1), 1–49.

Gigerenzer, G., Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143. https://doi.org/10.1111/j.1756-8765.2008.01006.x.

Griffiths, T. L., Kemp, C., Tenenbaum, J. B. (2008). Bayesian models of cognition. W: R. Sun (ed.), The Cambridge handbook of computational cognitive modeling (1–49). Cambridge: Cambridge University Press.

Hahn, U. (2014). The Bayesian boom: Good thing or bad? Frontiers in Psychology, 5(765), 1–12. https://doi.org/10.3389/fpsyg.2014.00765.

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

Hohwy, J. (2020). New directions in predictive processing. Mind & Language, 2(35), 209–223. https://doi.org/10.1111/mila.12281.

Kwisthout, J., van Rooij, I. (2019). Computational resource demands of a predictive Bayesian brain. Synthese, first online, 1–15. https://doi.org/10.1007/s42113-019-00032-3.

Lee, T. S., Mumford, D. (2003). Hierarchical Bayesian inference in the visual cortex. Optical Society of America, 20(7), 1434–1448.

Litwin, P., Miłkowski, M. (2020). Unification by fiat: Arrested development of predictive processing. Cognitive Science, 7(44), 1–27. https://doi.org/10.1111/cogs.12867.

Oaksford, M. (2014). Normativity, interpretation and Bayesian models. Frontiers in Psychology, 5(332), 1–5.
https://doi.org/10.3389/fpsyg.2014.00332.

Oaksford, M., Chater, N. (2007). Bayesian rationality: The probabilistic approach to human reasoning. Oxford: Oxford University Press.

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

Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco: Morgan Kaufmann Publishers.

Piekarski, M. (2020. Mechanizmy predykcyjne i ich normatywność. Liberi Libri. Warszawa.

Ramstead, M. J. D., Kirchhoff, M. D., Friston, K. J. (2019). A tale of two densities: Active inference is enactive inference. Adaptive Behavior, first online, 1–15. https://doi. org/10.1177/1059712319862774.

Rescorla, M. (2015). Bayesian perceptual psychology. W: M. Matthen (ed.), The Oxford handbook of philosophy of perception (694–716). Oxford: Oxford University Press.

Spratling, M. W. (2017). A review of predictive coding algorithms. Brain and Cognition, 112, 92–97. https://doi.org/10.1016/j.bandc.2015.11.003.

Wiese, W., Metzinger, T. (2017). Vanilla PP for philosophers: A primer on predictive processing. W: T. Metzinger, W. Wiese (eds.), Philosophy and Predictive Processing, 1, 1–18. Frankfurt am Main: MI ND Group. https://doi.org/10.15502/9783958573024.

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

• Description of WF-FI-123-WMSFT-P21 in USOSweb