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Computer Modelling of the Influence of Natural Selection on Perceptual Veridicality

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Computer Modelling of the Influence of Natural Selection on Perceptual Veridicality UNIVERZA V LJUBLJANI SKUPNI INTERDISCIPLINARNI PROGRAM DRUGE STOPNJE KOGNITIVNA ZNANOST V SODELOVANJU Z UNIVERSITÄT WIEN, UNIVERZITA KOMENSKÉHO V BRATISLAVE IN EÖTVÖS LORÁND TUDOMÁNYEGYETEM Tine Kolenik Computer modelling of the influence of natural selection on perceptual veridicality Računalniško modeliranje vpliva naravnega izbora na veridičnost zaznavanja MAGISTRSKO DELO Ljubljana, 2018 UNIVERSITY OF LJUBLJANA MIDDLE EUROPEAN INTERDISCIPLINARY MASTER’S PROGRAMME IN COGNITIVE SCIENCE IN ASSOCIATION WITH UNIVERSITÄT WIEN, UNIVERZITA KOMENSKÉHO V BRATISLAVE AND EÖTVÖS LORÁND TUDOMÁNYEGYETEM Tine Kolenik Computer modelling of the influence of natural selection on perceptual veridicality MASTER’S THESIS Supervisor: Univ. Prof. Dr. Urban Kordeš Co-supervisor: Univ. Prof. Dr. Igor Farkaš Ljubljana, 2018 UNIVERZA V LJUBLJANI SKUPNI INTERDISCIPLINARNI PROGRAM DRUGE STOPNJE KOGNITIVNA ZNANOST V SODELOVANJU Z UNIVERSITÄT WIEN, UNIVERZITA KOMENSKÉHO V BRATISLAVE IN EÖTVÖS LORÁND TUDOMÁNYEGYETEM Tine Kolenik Računalniško modeliranje vpliva naravnega izbora na veridičnost zaznavanja MAGISTRSKO DELO Mentor: izr. prof. dr. Urban Kordeš Somentor: univ. prof. dr. Igor Farkaš Ljubljana, 2018 ACKNOWLEDGEMENTS To my parents for the eternal support and for genuinely believing that knowledge is a gift no one can take away from you. This thesis is dedicated to you. To Polona for making sure I did not go (too) crazy while writing this thesis. To Professor Urban Kordeš for the continued inspiration and true mentorship. To Professor Igor Farkaš for the help and the always uplifting words. To the “Observatory” research team for the ideas. ZAHVALA Staršem za večno podporo in pristno vero v rek, da ti znanja ne more vzeti nihče. Magistrsko delo je posvečeno vama. Poloni, ker si poskrbela, da se mi med pisanjem magistrskega dela ni (še bolj) zmešalo. Profesorju Urbanu Kordešu za nenehen navdih in resnično mentorstvo. Profesorju Igorju Farkašu za vso pomoč in vedno vzpodbujajoče besede. Skupini »Observatorija« za vse ideje. Abstract The thesis explores computer modelling and its value in cognitive science as natural epistemology. This exploration is realised on several levels of analysis in terms of abstractness. Cognitive science and epistemology are argued to be closely related, manifesting their overlap in what some proponents of this connection name natural epistemology. The latter is defined as the study of epistemological questions with scientific methods. Key elements of natural epistemology are identified and proposed, most importantly the loop of knowings between epistemological insights in cognitive science and epistemology of cognitive science, which characterises progress in natural epistemology. Cognitive science and epistemology both primarily wonder what the relationship between mind and world is, and perception is identified as one of the sources on the knowledge of the world. It is therefore chosen for investigating this relationship, taking the evolutionary perspective on the development of perception. Computer modelling with genetic algorithms is used to study whether it is isomorphic or non- isomorphic perception that is more beneficial for modelled organisms. Two computer models are introduced – a model presented by Donald D. Hoffman and his colleagues, which possesses cognitivist presuppositions, and a newly designed model, which builds on Hoffman’s model by replacing certain cognitivist presuppositions for enactivist ones, mostly focusing on the addition of a sensorimotor loop. The models both produce the same results, as they show that non-isomorphic perception is evolutionary more beneficial than isomorphic. However, the sensorimotor loop causes the newly designed model to evolve faster. Afterwards, computer modelling is presented in the light of cognitive science as natural epistemology, questioning the results’ validity. The value and role of computer modelling is shown to be historically monumental by placing it in the loop of knowings and showing its influence on epistemological insights in cognitive science as well as epistemology of cognitive science. Despite the influence, several problems are identified, especially the “PacMan Syndrome”, the problem of the designed agents being unable to self-determine their meaning, which is forced upon them by the designer instead. The value of the two implemented models is discussed in this light. Two essential questions are posed: What do the models tell us about cognition? What role does their modelling play, especially the approach of designing models with different (epistemological) presuppositions and discerning their influence on final results? The first question is addressed by evaluating the models in several areas. The models are found to be explanatory of a possibility of non-isomorphic perception evolving (as opposed to the prevalent thoughts on that not being possible), not predictive (as they are not meant to be), abstract and simple, which may hinder the approach of comparing the models for their presuppositions, as they might not be able to affect the results because of the simplicity. Regarding the role of genetic algorithms, their arbitrariness in certain elements is presented as problematic, but as even more problematic, the design of the fitness function is presented. The fitness function is identified as an instantiation of the PacMan Syndrome, as the fitness function dictates what is good and what is bad for the models’ agents. It is suggested that by making the fitness function evolvable phylogenetically and ontogenetically, the designer’s role in predictably forcing its own meaning onto the agent is diminished a bit. By making the models more complex, the approach of comparing them would be made more legitimate in this case, but it was argued that it was a useful approach, as it showed the value of the sensorimotor loop. Regarding the models’ value on learning about cognition, it is suggested that they offer a functional understanding of a possible occurrence of non-isomorphic perception. Finally, the models are placed in the loop of knowings, their possible influence speculated upon. Keywords: cognitive science, computer modelling, enactivism, epistemology, evolution, genetic algorithms, perception Povzetek Magistrsko delo raziskuje računalniško modeliranje in njegovo vlogo v kognitivni znanosti kot naravni epistemologiji. Raziskovanje se odvija na različnih nivojih abstraktnosti. Kognitivna znanost in epistemologija sta predstavljeni kot sorodni disciplini pod imenom naravna epistemologija, ki označuje raziskovanje epistemoloških vprašanj z znanstvenimi metodami. Predstavljeni so ključni elementi naravne epistemologije s poudarkom na zanki med védenjem znotraj kognitivne znanosti in védenjem kognitivne znanosti. Razmerje med zunanjim svetom in umom predstavlja eno glavnih tem kognitivne znanosti in epistemologije, zaznavanje pa je opredeljeno kot eno glavnih virov za spoznavanje zunanjega sveta in zato zanimivo za raziskovanje. Evolucijski vidik je pokazan kot en najzanimivejših obravnavanj zaznavanja. Računalniško modeliranje z genetskimi algoritmi je uporabljeno za raziskovanje vprašanja, ali je za modeliran organizem preživetveno koristnejše izomorfno ali neizomorfno zaznavanje. Predstavljena sta dva modela – kognitivistični model Donalda D. Hoffmana in sodelavcev ter lasten model, ki nekatere kognitivistične predpostavke modela Hoffmana in sodelavcev zamenja z enaktivističnimi, s poudarkom na senzomotorični zanki. Oba modela rezultirata v razvoju neizomorfnega zaznavanja kot preživetveno koristnejšega za modelirane organizme. Senzomotorična zanka v enaktivističnem modelu se izkaže za koristno, saj povzroči hitrejši razvoj v modelu. Po predstavitvi modelov magistrsko delo razišče vlogo računalniškega modeliranja za naravno epostemologijo, predvsem z namenom prevpraševanja rezultatov predstavljenih modelov. Vloga računalniškega modeliranja je prestavljena kot zgodovinsko izredno pomembna v kognitivni znanosti, kar se kaže, ko je metoda postavljena v zanko védenj. Kljub pomembnosti je izpostavljenih več težav, predvsem Pacmanov sindrom, ki označuje težavo od raziskovalke vsiljenega pomena v modeliranih agentih, ki se ne morejo samodoločati. V tej luči se glede uporabnosti implementiranih modelov postavljata dve vprašanji: Kaj nam modela povesta o kogniciji? Kakšno vlogo igra modeliranje, še posebej pristop primerjanja vloge različnih (epistemoloških) predpostavk v samih modelih? Prvo vprašanje je naslovljeno z vrednotenjem modelov na več področjih. Ugotovljeno je, da modela pojasnjujeta možnost razvoja neizomorfnega zaznavanja (ki gre proti prevladujoči ideji, da je evolucija takšnega zaznavanja nemogoča) in ne napovedujeta (saj temu nista namenjena). Ugotovljeno je tudi, da sta abstraktna in preprosta, kar oteži pristop primerjave modelov na podlagi njihovih predpostavk, saj se zdi, da te zaradi preprostosti ne morejo vplivati na končni rezultat. Raziskana je vloga genetskih algoritmov, v katerih sta odkriti težavi v njihovi delni arbitrarnosti ter v predpostavljeni kriterijski funkciji. Ta je označena kot primer Pacmanovega sindroma, saj kriterijska funkcija veluje, kaj je za organizem koristno in kaj nekoristno. Predlagano je, da se raziskovalkina moč v določanju razvoja agentov lahko omeji z oblikovanjem kriterijske funkcije tako, da se le-ta filogenetsko in ontogenetsko razvija. S tem se moč ustvarjalke modeliranih agentov v tem, da lahko namerno določa in vnaprej pozna njihov razvoj, omeji. Pristop primerjave modelov na podlagi njihovih predpostavk
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