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Philosophy of Neuroscience


Admission requirements

  • BA students in Filosofie, who have successfully completed at least 70 ECTS credits of the mandatory components of the first and second year of their bachelor’s programme, including History of Modern Philosophy, Logica, Epistemologie or Wetenschapsfilosofie, Analytische filosofie, OR History of Modern Philosophy, History of Political Philosophy or Griekse en Romeinse filosofie, Ethiek, Politieke filosofie / Political Philosophy.

  • BA students in Philosophy: Global and Comparative Perspectives, who have successfully completed at least 70 ECTS credits of the mandatory components of the first and second year of their bachelor’s programme, including World Philosophies: Modern Europe, Logic, Epistemology or Philosophy of Science, Language of Thought, OR including World Philosophies: Greek and Roman Antiquity, World Philosophies: Modern Europe, Ethics, Political Philosophy.

  • Pre-master’s students in Philosophy who are in possession of an admission statement and who have to complete an advanced seminar, to be selected from package C.


This course presents an introduction to key debates in philosophy of neuroscience. We will approach these issues by closely reading and discussing relevant papers in the philosophy of neuroscience. Students will be assigned short papers for group presentations in which they will be required to reconstruct arguments, and outline relations between various philosophical positions in the literature. In this way, students can form a solid ground that they can later broaden into a comprehensive overview of the field as they move forward through the course. The course is hence very interactive.

Throughout the course we will draw on examples from neuroscience, cognitive neuropsychology, and artificial intelligence. Students will be encouraged to apply general philosophical ideas and analyses to their own specific fields of study.

Course objectives

Students should acquire knowledge about core concepts, principles, methods, and fundamental questions unique to philosophy of neuroscience, and thereby also gain a new critical perspective on foundational issues within neuroscience.


The timetables are available through MyTimetable.

Mode of instruction

  • Lecture

  • Seminar

Assessment method

  • Written examination with essay questions.

  • Active Participation/cooperation inclass/group.

  • Oral presentation.


1 mid-term essay (1000 words); 1 final essay (2500 words).

To qualify for the final essay, all students will have to give a group presentation, which will be graded only as passed/failed.

The resit exam will consist of a written essay of 3,500 words.


  • 1 mid-term essay 30%;

  • 1 final essay 70%.

To qualify for the final essay, all students will have to give a group presentation, which will be graded only as passed/failed.


To qualify for the resit, all students will have to give a group presentation, which will be graded only as passed/failed.

The resit exam essay 100%.

Inspection and feedback

How and when an exam review will take place will be disclosed together with the publication of the exam results at the latest. If a student requests a review within 30 days after publication of the exam results, an exam review will have to be organized.

Reading list

The copies of all the required readings will be provided in Brightspace.
Literature [ total = 580 pages ]

  • Bickle, J. (2022). Tinkering in the Lab. In The Tools of Neuroscience Experiment (pp. 13-36). Routledge.

  • Bogen, J. (2002). Epistemological custard pies from functional brain imaging. Philosophy of Science, 69(S3), S59-S71.

  • Burnston, D. C. (2016). Computational neuroscience and localized neural function. Synthese, 193(12), 3741-3762.

  • Chalmers, D. J. (2003). Consciousness and its place in nature. Blackwell guide to the philosophy of mind, 102-142.

  • Chalmers, D. J. (1999). Précis of The Conscious Mind. Philosophy and Phenomenological Research, 59(2), 435-438.

  • Chirimuuta, M. (2018). Explanation in computational neuroscience: Causal and non-causal. The British Journal for the Philosophy of Science.

  • Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience. Synthese, 191(2), 127-153.

  • Churchland, P. S. (1989). Neurophilosophy: Toward a unified science of the mind-brain. MIT press. General introduction, pp .1-13

  • Craver, C. F. (2016). The explanatory power of network models. Philosophy of Science, 83(5), 698-709.

  • Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford University Press.

  • Craver, C. F. (2006). When mechanistic models explain. Synthese, 153(3), 355-376.

  • Cummins, R. (2000). “How does it work?” vs. “What are the laws?” Two conceptions of psychological explanation. In F. Keil & R. Wilson (Eds.), Explanation and Cognition. Cambridge: Cambridge University Press.

  • Egan, F. (2018). Function-theoretic explanation and the search for neural mechanisms. In Explanation and Integration in Mind and Brain Science (pp. 145-163). Oxford University Press.

  • Farah, M. J., Illes, J., Cook-Deegan, R., Gardner, H., Kandel, E., King, P., ... & Wolpe, P. R. (2004). Neurocognitive enhancement: what can we do and what should we do?. Nature reviews neuroscience, 5(5), 421-425.

  • Farah, M. J. (2005). Neuroethics: the practical and the philosophical. Trends in cognitive sciences, 9(1), 34-40.

  • Farah, M. J. (1994). Neuropsychological inference with an interactive brain: A critique of the “locality” assumption. Behavioral and Brain Sciences, 17(1), 43-61.

  • Favela, L. H. (2020). Dynamical systems theory in cognitive science and neuroscience. Philosophy Compass, 15(8), e12695.

  • Gervais, R. (2015). Mechanistic and non-mechanistic varieties of dynamical models in cognitive science: Explanatory power, understanding, and the ‘mere description’worry. Synthese, 192(1), 43-66.

  • Gold, Ian, and Adina L. Roskies. 2008. Philosophy of Neuroscience. Oxford University Press.

  • Haggard, P., Clark, S., & Kalogeras, J. (2002). Voluntary action and conscious awareness. Nature neuroscience, 5(4), 382-385.

  • Khalifa, K., Islam, F., Gamboa, J. P., Wilkenfeld, D. A., & Kostić, D. (2022). Integrating philosophy of understanding with the cognitive sciences. Frontiers in Systems Neuroscience.

  • Kostić, D. and Khalifa, K. (2022). "Decoupling Topological Explanations from Mechanisms.” Philosophy of Science (2022), 22, 1–24 doi:10.1017/psa.2022.29.

  • Kostić, D. (2020). General theory of topological explanations and explanatory asymmetry. Philosophical Transactions of the Royal Society B, 375(1796), 20190321.

  • Libet, B. (1999). Do we have free will?. Journal of consciousness studies, 6(8-9), 47-57.

  • Mele, A. R. (2013). Vetoing and consciousness. Decomposing the will, 73-86.

  • Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183(3), 283-311.

  • Piccinini, G. (2006). Computational explanation in neuroscience. Synthese, 153(3), 343-353.

  • Rosenberg, A. (2005). Philosophy of science: a contemporary introduction. Routledge.

  • Roskies, A. (2002). Neuroethics for the new millennium: Commentary. Neuron 35: 21-23.

  • Tovino, S. A. (2007). Imaging body structure and mapping brain function: A historical approach. American Journal of Law & Medicine, 33(2-3), 193-228.


Enrolment through MyStudyMap is mandatory.


  • For substantive questions, contact the lecturer listed in the information bar at the right hand side of the page.

  • For questions about enrolment, admission, etc., contact the Education Administration Office Huizinga


Not applicable.