C9006 - Philosophy of Mind (1) (LM)

Learning outcomes

By the end of the course, students will know: the core commitments, motivations, and challenges of the principal theories of mind: dualism, physicalism (reductive and non-reductive), functionalism, and computational/connectionist accounts; some of the core contemporary proposals in the consciousness debate (including key scientific approaches to the study of consciousness) and the main issues raised by consciousness, including the easy problem, the hard problem, the metaproblem, and the leading; the central arguments concerning artificial consciousness, and the conceptual relations between minds, brains, and computation. Students will be able to: reconstruct arguments in clear premise–conclusion form, formalise key claims, and diagnose possible fallacies or equivocations; assess critically the validity and soundness of philosophical arguments compare and evaluate rival theories of the mind and consciousness using explicit criteria (parsimony, explanatory power, causal closure, interaction/pairing, mental causation); integrate relevant findings from cognitive science and neuroscience to assess philosophical claims about consciousness; formulate and defend a considered position on the possibility and criteria of AI consciousness, anticipating and responding to objections; produce concise, well-referenced essays and deliver clear oral presentations that state theses, arguments, and counter-arguments to postgraduate standards.

Course contents

The course is organised into two closely connected modules. The first critically examines the principal theories of mind—from dualism and physicalism to functionalist, computational, and connectionist approaches—focusing on their accounts of the relationship between minds, brains, and computation.

The second module is devoted to the philosophical problem of consciousness. We will examine some of the leading contemporary theories of consciousness, including scientifically informed approaches, and analyse the easy, hard, and meta-problems. The module concludes by considering whether artificial systems could be conscious, under what conditions, and on what grounds such a claim could be justified.

The detailed syllabus will be presented and discussed during the first lecture.

Readings/Bibliography

INDICATIVE BIBLIOGRAPHY

The following texts provide a general orientation to the main topics covered in the course. Students are not expected to read all the works listed below. Required and recommended readings will be assigned for individual lectures. A complete reference list, together with the detailed course programme, will be distributed in the official syllabus at the beginning of the course.

GENERAL INTRODUCTIONS

Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press.

Heil, J. (2004). Philosophy of Mind: A Contemporary Introduction (2nd ed.). Routledge.

Kim, J. (2011). Philosophy of Mind (3rd ed.). Westview Press.

Levin, J. (2022). The Metaphysics of Mind. Cambridge University Press.
https://doi.org/10.1017/9781108946803

ONLINE REFERENCE MATERIALS

Selected entries from the Stanford Encyclopedia of Philosophy:

“Dualism”
https://plato.stanford.edu/entries/dualism/

“The Mind/Brain Identity Theory”
https://plato.stanford.edu/entries/mind-identity/

“Physicalism”
https://plato.stanford.edu/entries/physicalism/

“Multiple Realizability”
https://plato.stanford.edu/entries/multiple-realizability/

“Functionalism”
https://plato.stanford.edu/entries/functionalism/

“Connectionism”
https://plato.stanford.edu/entries/connectionism/

“The Computational Theory of Mind”
https://plato.stanford.edu/entries/computational-mind/

“Consciousness”
https://plato.stanford.edu/entries/consciousness/

MINDS, ARTIFICIAL INTELLIGENCE, AND CONSCIOUSNESS

The final part of the course examines whether artificial systems could possess genuinely mental states or conscious experience. The following texts offer an accessible orientation to some of the central philosophical questions:

Borg, E. (2026). “LLMs, Turing Tests and Chinese Rooms: The Prospects for Meaning in Large Language Models.” Inquiry 69(6): 2807-2837.

Butlin, P. et al. (2023). “Consciousness in Artificial Intelligence: Insights from the Science of Consciousness.”
https://doi.org/10.48550/arXiv.2308.08708

Chalmers, D. J. (2023). “Could a Large Language Model Be Conscious?”
https://doi.org/10.48550/arXiv.2303.07103

Harnad, S. (1989) Minds, Machines and Searle. Journal of Theoretical and Experimental Artificial Intelligence 1: 5-25.

Harnad, S. (1990) The Symbol Grounding Problem, Physica D 42: 335-346

Searle, J. R. (1980). “Minds, Brains, and Programs.” Behavioral and Brain Sciences, 3(3), 417–457.

Turing, A. M. (1950). “Computing Machinery and Intelligence.” Mind, 59(236), 433–460.

Teaching methods

Lectures will be supported by handouts, slides, and supplementary materials, including videos and podcasts. Group work, guided activities, and structured discussion will promote peer learning and the critical examination of key topics. Large language models (LLMs) will be used selectively both to help design in-class activities and, during those activities, as interactive tutoring tools supporting argument reconstruction, conceptual clarification, and critical evaluation.

Assessment methods

Exam format

A written essay (approximately 1500 words) in which the student critically discusses one of the topics dealt with during the course (weight: 60% of the total mark).

An oral examination assessing both the student’s discussion of the essay and their knowledge and understanding of the core concepts covered during the course. The oral examination accounts for 40% of the final mark. For the integrated course Mind and Language, there will be a single joint oral examination covering the two short essays submitted for the two modules: Philosophy of Language and Philosophy of Mind.



Evaluation criteria

Concerning the essay, the basic criteria for the evaluation are: (i) whether and to what extent the essay shows an adequate knowledge and understanding of the main topics and arguments dealt with in the essay; (ii) whether the essay is adequately structured (as indicated by the guidelines made available during the course); (iii) clarity of exposition and argumentative rigor. Further criteria which, if present, may increase the evaluation are: (iv) some originality in either the content or the argumentative structure; (v) ability to critically assess in an autonomous manner the contents and arguments dealt with in the essay; (vi) ability to connect profitably the topic dealt with in the essay with some of the other topics discussed during the course; (vii) whether the student is able to autonomously perform bibliographical and thematic searches on the topic of the essay.

Concerning the oral part of the exam, the criteria for the evaluation are: (i) the extent to which the student knows and understands in a critical manner the topic of the essay, also in relation to the broader context of the course; (ii) the extent to which the student knows and understands the main topics discussed during the course—other than those discussed in the essay (this will weigh more for those students who didn’t attend the course).



Assessment Grid

30 (cum laude) — Excellent overall performance which demonstrates a solid knowledge as well as a deep and critical understanding of the topics dealt with during the course

30 — Very good overall performance which demonstrates solid knowledge and a very good understanding of the topics dealt with during the course

29-27 — Good overall performance which demonstrates a good knowledge and understanding of the topics dealt with during the course

26-24 — Fair overall performance which demonstrates adequate knowledge and understanding, but with detectable lacunae, of the topics dealt with during the course

23-20 – Sufficient overall performance which demonstrates barely adequate knowledge and understanding, with important lacunae, of the topics dealt with during the course

19-18 — Barely sufficient overall performance which demonstrates a rather superficial knowledge and understanding of the topics dealt with during the course

17 or less – Insufficient overall performance which demonstrates significant failures of understanding as well as absence of knowledge of significant parts of the topics dealt with during the course. Exam failed.



Students with disabilities and Specific Learning Disorders (SLD)

Students with disabilities or Specific Learning Disorders are entitled to special adjustments according to their condition, subject to assessment by the University Service for Students with Disabilities and SLD. Please do not contact teachers or Department staff, but make an appointment with the Service. The Service will then determine what adjustments are specifically appropriate, and get in touch with the teacher. For more information, please visit the page of the University of Bologna DSA service [https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students].

Generative AI may be used for brainstorming, outlining, testing objections, formative feedback, and linguistic revision. Students remain responsible for accuracy, originality, sources, and argumentative quality. Any use must be disclosed through a brief statement identifying the tool, stages of use, representative prompts, and the way outputs were checked or revised.

Teaching tools

Handouts, slides, the Virtuale platform, Wooclap, and selected videos and podcasts by experts on the core topics of the course. Large language models (LLMs) will also be used as teaching tools to support guided activities, conceptual clarification, argument analysis, and interactive tutoring.

Office hours

See the website of Filippo Ferrari