Uniwersytet Mikołaja Kopernika w Toruniu - Centralny punkt logowania
Strona główna

Philosophy of Cognitive Science

Informacje ogólne

Kod przedmiotu: 2401-CS-PoCS-s2
Kod Erasmus / ISCED: (brak danych) / (0223) Filozofia i etyka Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu: Philosophy of Cognitive Science
Jednostka: Katedra Kognitywistyki
Grupy: Cognitive Science s2 - I i II rok przedmioty do wyboru
Punkty ECTS i inne: 4.00 Podstawowe informacje o zasadach przyporządkowania punktów ECTS:
  • roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS;
  • tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h;
  • 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się;
  • tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS;
  • nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta.
Język prowadzenia: angielski
Pełny opis:

I. Course description

The course aims to introduce the students to the philosophical foundations of cognitive science. This academic year, the course will focus on two subjects: the nature of consciousness and the status of Large Language Models (LLMs) as models of cognition. The course is comprised of two parts. During the first half of the semester, the instructor will give a series of lectures (with in-class discussion encouraged) to familiarize the students with basic philosophical concepts and ideas about consciousness and AI/LLMs. The second half of the semester will be devoted to student presentations.

II. Attendance

Students can miss no more than two meetings without consequences. If the number of missed meetings exceeds 2, the student must complete an additional assignment (e.g. write a summary of a selected article or chapter). An attendance list will be collected each week.

III. Student presentations

During the second part of the course, the students will break into five study groups (each consisting of 3-4 people) to deliver a series of in-class presentations, one per group. The plan is to have one weekly presentation during the second half of the semester, with each presentation lasting approximately 45-60 minutes and followed by a Q&A session. The presentation should consist of a synthetic and critical discussion of a given subject based on a thorough reading of multiple sources. Each study group should try to seriously engage with the relevant material (i.e. read through and develop a good understating of it), discuss it together, and try to recognize the most critical or recurring ideas in order to discern the “big picture” that emerges from the material (but also noting any conflicting evidence or points of contention between different authors). For presentation subjects and suggested reading lists, consult the course schedule below. Please note that participation in study groups and the preparation of presentations is mandatory to finish the course.

IV. Grading

The grades will be determined by (1) student presentations and (2) in-class participation.

V. Course schedule

(L – lecture, SP – student presentation)

1. Introduction

2. L1: Consciousness, part I: Conceptual distinctions and the Hard Problem

3. L2: Consciousness, part II: Zombies and Mary the Neuroscientist

4. L3: Consciousness, part III: Illusionism vs panpsychism

5. L4: AI and the computational mind, part I: Introduction

6. L5: AI and the computational mind, part II: Philosophy of Large Language Models

7. Study break: no class this week but additional office hours to allow the students to consult the instructor about their presentations if needed

8. SP1: Global Workspace theory

• Preliminary readings:

i. Seth, A., Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23, 439–452.

ii. Wu, W. (2018). The neuroscience of consciousness. In: E. Zalta (e.). The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/consciousness-neuroscience/

• Dehaene, S. (2014). Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. Chapter 5 (optionally chapters 4 & 6).

• Dehaene, S., Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70, 200-227.

• Dehaene, S., Changeux, J-P., Naccache, L., Sackur, J., Sergent, C. (2006). Conscious, preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Sciences, 10(5), 204–211.

• Dehaene, S., Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework, Cognition 79(1), 1–37.

• Mashour, G. A., Roelfsema, P., Changeux, J. P., Dehaene, S. (2020). Conscious processing and the Global Neuronal Workspace hypothesis. Neuron, 105, 776–798.

• VanRullen, R., Kanai, R. (2021). Deep learning and the global workspace theory. Trends in Neurosciences, 14, 692–704.

9. SP2: Higher Order & Reality Monitoring theory

• Preliminary readings:

i. Seth, A., Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23, 439–452.

ii. Wu, W. (2018). The neuroscience of consciousness. In: E. Zalta (e.). The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/consciousness-neuroscience/

• Brown, R, Lau, H., LeDoux, J.E. (2019). Understanding the higher-order approach to consciousness. Trends in Cognitive Sciences, 23, 754–168.

• Dijkstra, N., Kok, P., Fleming, S. (2021). Perceptual reality monitoring: Neural mechanisms dissociating imagination from reality. Neuroscience & Biobehavioral Reviews, 135, 104557.

• Fleming, S. (2020). Awareness as inference in a higher order state space. Neuroscience of Consciousness, 6, niz020.

• Gershman, S. J. (2019). The generative adversarial brain. Frontiers in Artificial Intelligence, 2, 18.

• Lau, H. (2008). A higher order Bayesian decision theory of consciousness. Progress in Brain Research, 168, 35–48.

• Lau, H. (2019). Consciousness, metacognition, & perceptual reality monitoring. PsyArXiv preprint. https://psyarxiv.com/ckbyf/

• Lau, H., Rosenthal, D. (2011). Empirical support for higher-order theories of conscious awareness. Trends in Cognitive Sciences, 15, 365–373.

• Simons, J. S., Garrison, J. R., Johnson, M. K. (2017). Brain mechanisms of reality monitoring. Trends in Cognitive Sciences, 21, 462–473.

10. SP3: Exploring new frontiers in consciousness science: Fetus consciousness & contentless experiences in “white dreams” and meditation

• Bayne, T., Frohlich, J., Cusack, R., Moser, J., Naci, L. (2023). Consciousness in the cradle: on the emergence of infant experience. Trends in Cognitive Sciences, 27, 1135–1149.

• Ciaunica, A., Safron, A., Delafield-Butt, J. (2021). Back to square one: the bodily roots of conscious experiences in early life. Neuroscience of Consciousness, 2021, niab037.

• Fazekas, P., Nemeth, G., Overgaard, M. (2019). White dreams are made of colours: What studying contentless dreams can teach about the neural basis of dreaming and conscious experiences. Sleep Medicine Reviews, 43, 84–91.

• Frohlich, J., Bayne, T., Crone, J.S., DallaVecchia, A., Kirkeby-Hinrup, A., Pedro A.M. Mediano, P.A.M., Moser, J., Talar, K., Gharabaghi, A., Preissl, H. (2023). Not with a “zap” but with a “beep”: Measuring the origins of perinatal experience. NeuroImage, 273, 120057.

• Moser, J., Schleger, F., Weiss, M., Sippel, K., Semeia, L., Preissl, H. Magnetoencephalographic signatures of conscious processing before birth. Developmental Cognitive Neuroscience, 49, 100964.

• Woods, T.J., Windt, J.M. & Carter, O. (2022). Evidence synthesis indicates contentless experiences in meditation are neither truly contentless nor identical. Phenomenology and the Cognitive Sciences.

• Woods, T.J., Windt, J.M. & Carter, O. (2022). The path to contentless experience in meditation: An evidence synthesis based on expert texts. Phenomenology and Cognitive Sciences.

• Woods, T.J., Windt, J.M., Brown, L., Carter, O., Van Dam, N.T. (2023). Subjective experiences of committed meditators across practices aiming for contentless states. Mindfulness, 14, 1457–1478.

11. SP4: Meaning and understanding in LLMs? [Leading questions: Do sentences generated by LLMs possess meaning in the same sense as human-produced speech? Do those sentences refer to things in the world by being appropriately related to them (symbol grounding problem)? Do advanced LLMs pass the Turing test? If they did, would it mean that they “understand” what they say? Or maybe the Turing test is obsolete in the LLM era? Are LLMs more like a Blockhead or a Chinese room (those concepts will be discussed during lectures) than a human? What is “understanding”, anyway?]

• Chalmers, D. (2023). Does thought require sensory grounding? From pure thinkers to large language models. Proceedings and Addresses of the American Philosophical Association, 97, 22–45.

• Chiang, T. (2023). ChatGPT is a blurry JPEG of the web. New Yorker: https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web

• Jones, C., Bergen, B. (2023). Does GPT-4 pass the Turing test? https://arxiv.org/abs/2310.20216

• Mitchell, M., Krakauer, D.C. (2023). The debate over understanding in AI’s large language models. PNAS, 120, e2215907120.

• Mollo, C.D., Milliere, R. (2023). The vector grounding problem. https://arxiv.org/abs/2304.01481

• Schwitzgebel, E., Schwitzgebel, D., Strasser, A. (2023). Creating a large language model of a philosopher. Mind & Language.

• Van Rooij, I., Guest, O., Adolfi, F., de Haan, R., Kolokolova, A., Rich, P. (2023). Reclaiming AI as a theoretical tool for cognitive science. https://osf.io/preprints/psyarxiv/4cbuv

• Yang, R., Narasimhan, K. (2023). The Socratic method for self-discovery in large language models. https://princeton-nlp.github.io/SocraticAI/

12. SP5: LLMs versus (?) biological cognition [Leading questions: What are the similarities and differences between how LLMs and biological humans (as well as non-human animals) work? Can LLMs reason using world models (e.g., theory of mind)? Do they acquire knowledge like human babies do? Could they ever be conscious? If the answer to such questions is “no”, then what is it precisely that LLMs lack? Is this missing piece related to embodiment, the capacity for action, the lack of innate knowledge which humans supposedly possess, or maybe something else entirely?]

• Aru, J., Larkum, M.E., Shine, J.M. (2023). The feasibility of artificial consciousness through the lens of neuroscience. Trends in Neurosciences, 46, 1008-1017.

• Chalmers, D. (2023). Could a large language model be conscious? https://arxiv.org/abs/2303.07103

• Chemero, A. (2023). LLMs differ from human cognition because they are not embodied. Nature Human Behavior, 7, 1828–1829.

• Frank, M.C. (2023). Bridging the data gap between children and large language models. Trends in Cognitive Sciences, 27, 990–992.

• Mahovald, K., Ivanova, A., Blank, I.A., Kanwisher, N., Tenenbaum, J.B., Fedorenko, E. (2023). Dissociating language and thought in large language models. https://arxiv.org/abs/2301.06627

• Pezzulo, G., Parr, T., Cisek, P., Clark, A., Friston, K. (2024). Generating meaning: active inference and the scope and limits of passive AI. Trends in Cognitive Sciences, 28, 97–112.

• Trott, S., Jones, C., Chang, T., Michaelov, J., Bergen, B. (2023). Do large language models know what humans know? Cognitive Science, 47, e13309.

13. Summary

Zajęcia w cyklu "Semestr letni 2022/23" (zakończony)

Okres: 2023-02-20 - 2023-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Wykład, 30 godzin więcej informacji
Koordynatorzy: Paweł Gładziejewski
Prowadzący grup: Paweł Gładziejewski
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie na ocenę
Wykład - Zaliczenie na ocenę

Zajęcia w cyklu "Semestr letni 2023/24" (w trakcie)

Okres: 2024-02-20 - 2024-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Konwersatorium, 30 godzin więcej informacji
Koordynatorzy: Paweł Gładziejewski
Prowadzący grup: Paweł Gładziejewski
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie na ocenę
Konwersatorium - Zaliczenie na ocenę
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Mikołaja Kopernika w Toruniu.
ul. Jurija Gagarina 11, 87-100 Toruń tel: +48 56 611-40-10 https://usosweb.umk.pl/ kontakt deklaracja dostępności USOSweb (2024-04-02)