Napovedna vključenost kot enaktivističen napovedni model v nevroznanosti

magistrsko delo

Marko Kvar (Author), Sebastjan Vörös (Mentor)

Abstract

Magistrsko delo proučuje napovedne modele skozi prizmo sodobnega enaktivizma. Osrednja tema naloge je raziskovanje odnosa napovednega kodiranja in napovednega procesiranja z enaktivizmom ter preverjanje razlagalne učinkovitosti napovedne vključenosti kot enaktivističnega napovednega modela. V prvem delu so predstavljeni ključni sestavni elementi obstoječih napovednih modelov: načelo proste energije, Bayesovo sklepanje, aktivno sklepanje in markovske odeje. Prav tako je v prvem delu predstavljeno zgodovinsko ozadje napovednih modelov ter enaktivistični pristop v filozofiji. V drugem delu magistrsko delo ponudi širok filozofski pregled treh napovednih modelov: modela napovednega kodiranja, modela napovednega procesiranja in modela napovedne vključenosti. Pri tem je pod drobnogled vzet predvsem odnos med umom, telesom in svetom v vsakem od omenjenih modelov. V drugem delu naloge preverim hipotezo, ki trdi, da napovedna vključenost predstavlja razlagalno učinkovit model, ki uspešno povezuje napovedne modele z enaktivizmom, ter da je napovedna vključenost uporaben pristop k raziskovanju problema odnosa med umom, telesom in okoljem. Ta hipoteza je v poglavju o napovedni vključenosti podprta z naborom relevantnih nevroznanstvenih raziskav. Poglavje o napovedni vključenosti dodatno zagovarja tezo, da se načelo proste energije in koncept avtopoeze med seboj ne izključujeta, ampak dopolnjujeta. To poglavje izpostavlja tudi prednosti napovedne vključenosti pred klasičnimi napovednimi modeli ter s tem proučuje zmožnosti nadomestitve oziroma nadgradnje klasičnih napovednih modelov z enaktivističnimi napovednimi modeli.

Keywords

filozofija;nevroznanost;napovedni modeli;enaktivizem;prosta energija;avtopoeza;napovedna napaka;aktivno sklepanje;entropija;

Data

Language:	Slovenian
Year of publishing:	2021
Typology:	2.09 - Master's Thesis
Organization:	UL PEF - Faculty of Education
Publisher:	[M. Kvar]
UDC:	1:611.8(043.2)
COBISS:	79505411
Views:	146
Downloads:	14
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Predictive engagement as enactive predictive model in neuroscience
Secondary abstract:	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.
Secondary keywords:	cognitive psychology;kognitivna psihologija;
File type:	application/pdf
Type (COBISS):	Master's thesis/paper
Thesis comment:	Univ. v Ljubljani, Skupni interdisciplinarni program druge stopnje Kognitivna znanost, v sodelovanju z Universität Wien, Univerzita Komenského v Bratislave in Eötvös Loránd Tudományegyetem
Pages:	70 str.
ID:	13469376