Učni načrt predmeta

Predmet:

Kognitivne znanosti

Course:

Cognitive Sciences

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Informacijske in komunikacijske tehnologije, 3. stopnja	Inteligentni sistemi in robotika	1	1
Information and Communication Technologies, 3rd cycle	Intelligent Systems and Robotics	1	1

Vrsta predmeta / Course type

Izbirni / Elective

Univerzitetna koda predmeta / University course code:

IKT3-630

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
15	15			15	105	5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:

prof. dr. Matjaž Gams

Sodelavci / Lecturers:

Tine Kolenik , dr. Vedrana Vidulin

Jeziki / Languages:

Predavanja / Lectures:

Slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:

Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second-cycle studies in information or communication technologies or completed second-cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:

Content (Syllabus outline):

Znanstvena metoda:
strukture znanstvenega védenja, znanstvene aktivnosti in procesi.

Uvod:
uvod v kognitivne znanosti kot študij uma in inteligence z interdisciplinarnega stališča;
uvod v um, zavest, čustva, podzavest, kvalia, psihologija, drugi pristopi;
povezava med kognitivnimi znanostmi in umetno inteligenco ter inteligentnimi sistemi.

Kognitivni paradoksi in koncepti:
pregled glavnih paradigm v kognitivni znanosti;
Turingov test, TT, TTT, TTTT;
kitajska Searlova soba, Einsteinova knjiga;
problem telo-duh, teorije zavesti:
princip in paradoks mnogoterega znanja;
je GPT lahko zavesten, generativen in inteligenten;
lahko in težko vprašanje;
trendi.

Kognitivne arhitekture:
teoretične osnove;
pregled arhitektur;
arhitekture podsistemov kognicije;
celovite arhitekture tipa 1;
arhitekture tipa 2;
nizko in visokonivojske arhitekture.

Kognitivne tehnike in metode:
metode kognitivne nevroznanosti;
modeliranje kognicije;
logika, pravila, koncepti, analogije, asociacije, povezave;
kognitivni agenti;
praktična uporaba izbranih tehnik in orodij.

Praktično usposabljanje:
praktična uporaba izbranih tehnik in orodij kognitivnih znanosti.

Scientific Method:
scientific knowledge structures, scientific activities/processes.

Introduction:
introduction to cognitive sciences as studies of the mind and intelligence from the interdisciplinary viewpoint;
introduction to the mind, consciousness, feelings, subconsciousness, qualia, psychology, other approaches;
relation to artificial intelligence and intelligent systems.

Cognitive paradoxes and concepts:
overview of major cognitive paradigms;
Turing test, TT, TTT, TTTT;
Chinese Searl's room, Einstein's book;
mind-body problem, theories of consciousness;
principle and paradox of multiple knowledge;
can GPT be conscious, generative and intelligent;
easy and hard question;
trends.

Cognitive architectures:
theoretical foundations, overview;
subsystem architectures;
type 1 architectures;
type 2 architectures;
low/ and high/level architectures.

Cognitive techniques and methods:
cognitive neuroscience methods;
modeling of cognition;
logic, rules, concepts, analogies, associations, connections;
cognitive agents;
practical use of cognitive techniques and tools;

Practical exercises:
practical use of selected cognitive techniques and tools.

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
M. W. Eysenck, M. T. Keane. Cognitive Psychology, A Student's Handbook, 8th Edition, 2020, Psychology Press, DOI 10.4324/9781351058513.
P. Dutta, S. Pal, A. Kumar, K. Cengiz. Artificial intelligence for cognitive modeling, Theory and Practice, 2023, CRC Press.
D. Poeppel, G. R. Mangun, M. S. Gazzaniga. The Cognitive Neurosciences, MIT, 2020, 6th edition. ISBN 9780262356176.
M. T. Banich, R. J. Compton. Cognitive Neuroscience, 2023, Cambridge University Press, DOI 10.1017/9781108923361
H. J. Levesque. Common Sense, the Turing Test, and the Quest for Real AI. 2018, MIT Press, ISBN 9780262535205

Cilji in kompetence:

Objectives and competences:

Razviti znanje in sposobnost konkretne vpeljave kognitivnih metod in tehnik v računalniške programe, softverske ali podprte z robotskimi sistemi, je osnovni cilj predmeta.

Seznanitev z osnovnimi pristopi in arhitekturami je tudi pomemben cilj. Osnovna znanja s področja so dodatni cilj.

Pomembno je razumevanje interdisciplinarnih pogledov na vrsto kognitivnih konceptov, od nižjenivojskih do visokonivojskih kognitivnih sistemov, arhitektur in modelov.

Tehnike in metode kognitivnih modelov omogočajo poznavanje računalniških metod, še posebej kognitivnih agentov.

Študenti bodo obvladali osnove kognitivnih znanosti in bodo usposobljeni za praktično uporabo izbranih orodij, metod, tehnik in arhitektur kognitivnih sistemov.

The basic goal is to foster knowledge and capability of applying cognitive methods and techniques into computer and robotic systems.

The second goal is to improve knowledge of cognitive approaches and architectures.

One of the course objectives is to improve knowledge of interdisciplinary viewpoints on selected cognitive concepts from lower-level to higher-level systems, architectures and modules.

Various cognitive techniques and methods including cognitive agents enable constructing computer methods simulating cognitive functions.

The students will master the basics of cognitive sciences and will be capable of using selected tools, methods, techniques and architectures of cognitive systems.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- osnove znanstvenega pristopa v kognitivnih znanostih,
- osnovna znanja o kognitivnih znanostih,
- pregled obstoječih konceptov in metod kognitivnih znanosti,
- obvladana uporaba izbranih metod in tehnik kognitivnih sistemov,
- boljše znanje izdelovanja kognitivih IT in AI sistemov,
- usposobljenost za praktično implementiranje kognitivnih sistemov.

Students successfully completing this course will acquire:
- Basic scientific approach in cognitive sciences
- Basic knowledge about cognitive sciences
- Overview of existing contexts and methods in cognitive sciences
- Mastering selected methods and techniques of cognitive systems
- Improved knowledge about designing AI and AI cognitive systems
- Capability of practical use of selected cognitive architectures and systems

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo.

Lectures, seminar, consultations, individual work.

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Seminarska naloga

80 %

Seminar work

Ustni zagovor

20 %

Oral defense

Reference nosilca / Lecturer's references:

1.	T. Kolenik, M. Gams. Persuasive Technology for Mental Health: One Step Closer to (Mental Health Care) Equality?, IEEE Technology and Society Magazine 2021, 40 (1), 80-86, DOI 10.1109/MTS.2021.3056288.
2.	T. Kolenik, M. Gams. Intelligent Cognitive Assistants for Attitude and Behavior Change Support in Mental Health: State-of-the-Art Technical Review, Electronics 2021, 10 (11), 1250, DOI 10.3390/electronics10111250,
3.	M. Gams, T. Kolenik. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules, Electronics 2021, 10(4), MDPI, DOI 10.3390/electronics10040514
4.	M. Gjoreski, B. Mahesh, T. Kolenik, J. Uwe-Garbas, D. Seuss, H. Gjoreski, M. Luštrek, M. Gams, V. Pejović. Cognitive Load Monitoring with Wearables—Lessons Learned from a Machine Learning Challenge. IEEE Access 2021 9, 103325-103336, DOI 10.1109/ACCESS.2021.3093216
5.	P. Kocuvan, A. Hrastič, A. Kareska, M. Gams. Predicting a fall based on gait anomaly detection: a comparative study of wrist-worn three-axis and mobile phone-based accelerometer sensors. Sensors 2023 23(19), 8294, 10.3390/s23198294