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

Course Description

How does neural activity give rise to the mind? How do billions of neurons enable us to perceive the world, learn from experience, remember the past, make decisions, and guide our actions?

This course provides an advanced introduction to cognitive neuroscience, exploring how mental functions emerge from the activity of neural circuits. Through the integration of experimental evidence, computational theories, and state-of-the-art research methods, students will examine how cognition is implemented in the brain.

Particular emphasis is placed on the neural mechanisms of learning, prediction, and decision-making, from the cellular basis of neuronal communication to reinforcement learning, dopamine signaling, and contemporary computational approaches inspired by neuroscience. The course also introduces how principles derived from cognitive neuroscience have influenced modern artificial intelligence.

Learning Outcomes

Upon successful completion of the course, students will be able to:

• explain the physiological and neural mechanisms underlying perception, learning, memory, and decision-making across multiple levels of analysis, from individual neurons to large-scale brain networks;
• describe the computational principles of reinforcement learning and discuss their neural implementation;
• understand and critically evaluate contemporary theories and empirical findings in cognitive neuroscience;
• interpret experimental designs and results from cognitive neuroscience studies, appreciating the strengths and limitations of different methodological approaches;
• formulate scientifically grounded hypotheses and propose experimental strategies to test them;
• appreciate the reciprocal influence between cognitive neuroscience and artificial intelligence.

Course Content

The course is divided into two teaching modules.

Module 1 (4 CFU)

• Introduction to cognitive neuroscience: the relevance of cognitive neuroscience to Artificial Intelligence and the relationship between brain structure and cognitive function.
• Neurons, neural circuits, and neural systems: anatomy and physiology of the nervous system, neuronal communication, and the organization of large-scale neural networks.
• Reinforcement learning: principles of Pavlovian (prediction) and instrumental (control) learning.
• Neural mechanisms of learning and memory: neural plasticity.
• Computational mechanisms of learning: contiguity, contingency, surprise, and prediction errors.
• The reward prediction error hypothesis of dopamine 
• From reinforcement learning to decision-making: neural mechanisms underlying goal-directed and habitual behaviour.

Module 2 (2 CFU)

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

Examination

The examination assesses students' knowledge and understanding of the topics covered during the course.

The exam consists of a 60-minute written test comprising three open-ended questions:

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

All answers must be written in English.

Each question is worth a maximum of 10 points, for a total of 30 points. The final grade is the sum of the scores obtained on the three questions. The "30 cum laude" distinction will be awarded only to students who obtain the maximum score (10/10) on each question.

During the examination, students may not consult lecture materials, textbooks, scientific articles, personal notes, electronic devices, or any other external resources.

The exam will be completed on the laboratory computers via the EOL platform, where students will write and submit their answers.

Students with Disabilities or Specific Learning Disorders (DSA)

Students with temporary or permanent disabilities or specific learning disorders (DSA) are encouraged to contact the relevant University office as soon as possible:

https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students

Requests for examination adaptations must be submitted at least 15 days before the exam date to Prof. Starita. The proposed adaptations will be evaluated to ensure they are compatible with the learning objectives of the course.

Exam Registration

Students must register for the examination through the AlmaEsami system by the published deadline.

Students who experience technical difficulties preventing timely registration must contact the Segreteria Didattica before the registration deadline and notify Prof. Starita by email. Admission to the examination in these cases will be evaluated on an individual basis.

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.

Grade Registration

Examination results will be published on AlmaEsami, and grades will be officially registered five working days after publication.

Students wishing to refuse their grade must notify Prof. Starita by email within five working days of the publication of the results. If no communication is received within this period, the grade will be automatically registered.

Students may refuse a grade any number of times.

Use of Artificial Intelligence (AI)

Students are encouraged to use AI tools responsibly to support their learning, for example by generating summaries, clarifying concepts, creating practice questions, or self-assessing their understanding.

The use of AI or any other unauthorized assistance during the examination is strictly prohibited and constitutes a violation of academic integrity.

Teaching tools

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

Office hours

See the website of Francesca Starita

See the website of Giuseppe Di Pellegrino