91247 - Cognition and Neuroscience

Learning outcomes

At the end of the course, the student knows state-of-art human and animal research that uses neuroscience techniques to understand the cognitive and emotional aspects of the human mind and behavior. The student is able to critically read experimental and theoretical studies of cognitive and affective neuroscience, to evaluate their methods and results, explain their significance, and apply such notions in the study and development of artificial intelligence systems.

Course contents

How do neurons in the brain give rise to mind – to our abilities to sense and perceive the world, to act and think about it, to learn and remember it? This course will provide an accessible but highly challenging overview of the empirical evidence, theories and methods in cognitive neuroscience exploring how cognition is instantiated in neural activity. Drawing on a wide variety of investigative methods available to cognitive neuroscience, the course explores the neural mechanisms underlying complex cognitive processes.

At the end of the course the student is be able to:

• get in-depth understanding of the neural substrates and functional mechanisms of mental processes;
• get knowledge of state of the art methodologies and novel approaches of current research in cognitive neuroscience;
• critically review and discuss the theoretical and empirical contributions of the current literature, understand and analyze the methods employed, interpret their results and critically assess their conclusions;
• exercise the ability to engage in creative thinking leading to formulations of new hypotheses and planning of their empirical testing.

The course is divided into two teaching modules, which will cover the following topics:

Module 1 (4 CFU)

• What is cognitive neuroscience?
• From single neurons to neural networks and systems
• Signal transmission within and between neurons
• Introduction to animal reinforcement learning (RL): Pavlovian/prediction learning and instrumental/control learning
  
• Mechanisms of RL 1: contiguity, contingency & surprise
• Mechanisms of RL 2: the reward prediction error hypothesis of dopamine neurons
• From reinforcement learning to decision-making
  

Module 2 (2 CFU)

• Reinforcement learning (RL): from cognitive neuroscience to artificial intelligence

Previous knowledge required:

Prerequisite involves high-school knowledge of the anatomy and physiology of the Nervous System.

It is recommended to view the videos on Neuroscience Core concepts, freely available at the Society for Neuroscience website: https://www.brainfacts.org/core-concepts

Readings/Bibliography

Lecture slides and scientific articles will be available on Virtuale and will represent the core material needed to pass the exam.

For Module 1, the following readings are also recommended:

Papers

• Brooks R, Hassabis D, Bray D, Shashua A. Turing centenary: Is the brain a good model for machine intelligence? Nature. 2012 Feb 22;482(7386):462-3. doi: 10.1038/482462a. PMID: 22358812.
• Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
• Schultz, W. (2016). Dopamine reward prediction error coding. Dialogues in clinical neuroscience, 18(1), 23-32.
  
• Dolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron, 80(2), 312–325. https://doi.org/10.1016/j.neuron.2013.09.007
  

Book chapters

• Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (2014). Cognitive Neuroscience, The biology of the mind. Chapters: 1, 2
• Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., Hudspeth, A. J., & Mack, S. (Eds.). (2000). Principles of neural science. New York: McGraw-hill. Chapters 2, 4, 6, 7, 8, 15, 48, 65
• Daw, N. D., & O’Doherty, J. P. (2014). Multiple systems for value learning. In Neuroeconomics (Chapter 21, pp. 393-410). Academic Press.
• Daw, N. D., & Tobler, P.N. (2014). Value Learning through Reinforcement: The Basics of Dopamine and Reinforcement Learning. In Neuroeconomics (Chapter 15, pp. 283-298). Academic Press.
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press. Chapter 14, 15
  

Teaching methods

Lectures will be approached in an interactive way, through

• the discussion of neuroscientific experiments led by the teacher
• the use of interactive polls and quizzes
• the completion of in-class excercises
  

Thus, the students will be required to:

• participate actively during the lectures
• ask questions about the topics discussed
• stimulate the debate
• critically discuss the scientific data reviewed during the course

Assessment methods

The exam tests the knowledge of the topics discussed during the course. Answers must be provided in English.

A total time of 60 minutes is allowed for the exam. The exam consists of a written exam including 3 open questions

• 2 open questions on the topics covered in Module 1 (Prof. Starita).
  
• 1 open question on the topics covered in Module 2 (Prof. di Pellegrino)
  

Up to 30 points are assigned for each open question, and the final score (out of thirty) is given by the average of the points obtained on each question.

During the exam, students are not allowed to use any lecture material nor books, scientific articles, personal notes, or electronic media.

The exam will be taken on the lab computers through EOL.

Exam enrollment

Student must enroll in the exam using the Almaesami application, strictly by the deadline. Those who fail to enroll for technical issues by the due date are required to report the problem to the “segreteria didattica” (and in any case before the deadline) and send an email to Prof. Starita, who will ultimately decide whether to admit the student to take the exam.

Evaluation criteria

The following criteria will be applied to evaluate each answer: 

Analysis/critical thinking

• Ability to select, consider, evaluate, the course material relevant to answering the question.
• Use of appropriate definitions for the concepts prompted by the exam questions.
• Understanding of relevant concepts, through proper analysis of the course material.
• Ability to synthesize and employ in an original way ideas from across the course.
• Discussion of relevant evidence to support assertions (e.g. discussion of experimental evidence, use of citations/references).

Structure

• Clarity of introduction, body, and conclusion
• Clear, logical and well-organized flow of information.

Style

• Precision of vocabulary and use of academic tone.
• Clarity and conciseness of sentences, with minimal verbosity.
• Use of appropriate grammar, sentence construction, paragraph structure.

The exam grade will be registered on the 5th working day following the date of publication of the results. Students who want to refuse their grade need to write an email to Prof. Starita stating the whish to refuse, otherwise their grade will be registered. You can refuse the grade as many times as you want, but you must do it within the 5 working days following the date of publication of the results, otherwise your grade will be registered.

Teaching tools

• PowerPoint slides and video clips
• Scientific articles and book chapters
• In-class discussion, activities
• In-class polls, and quizzes

Office hours

See the website of Francesca Starita

See the website of Giuseppe Di Pellegrino