From mind to life - contemporary discussion

philosophy

elementary

FI1_W06; FI1_W08; FI1_W09; FI1_U10;

The aim of the lecture is to acquaint students with contemporay Bayesian models used in the philosophy of mind, cognitve sciences and life sciences. The lecture is introductory.

The aim of the lecture is to acquaint students with contemporay Bayesian models used in the philosophy of mind and cognitve sciences and life scienceas. During the lecture, the student will learn the content and meaning of the Bayesian rule, the first Bayesian models in psychology and cognitive science, rational analysis; predictive coding, predictive processing, active inference and a model based on the free energy principle. The selected models will be analyzed in terms of their explanatory powers, rationality and normativity.

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.

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