91247 - Cognition and Neuroscience

Academic Year 2023/2024

  • Moduli: Francesca Starita (Modulo 1) Giuseppe Di Pellegrino (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Artificial Intelligence (cod. 9063)

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 survey 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 tools 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 the-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
  • 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
  • Model-based vs model-free 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 biology of the Central Nervous System.

In addition, it is highly recommended to attend to online video lectures on Neuroscience Core concepts, freely available at the Society for Neuroscience website: https://www.brainfacts.org/core-concepts


Scientific articles and lecture material will be available on Teaching resources on Virtuale and will represent the core material needed to pass the exam.

Teaching methods

Lectures on the different topics of the modules will be approached in an interactive way, through the discussion of neuroscientific experiments led by the teacher. The student will therefore be required to participate actively during the lectures, ask questions about the topics discussed, stimulate the debate and critically discuss the scientific data reviewed during the course.

Assessment methods

The exam tests the acquired knowledge of the core arguments 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:

  • 2 open questions on the topics covered in Module 1 (Prof. Starita).
  • 10 multiple choice questions on the topic covered in Module 2 (Prof. di Pellegrino). Each multiple choice question has 5 possible answers of which only one is correct.

The final score (out of thirty) is given by the sum of the points obtained to open questions (20 points) and multiple choice questions (10 points).

For open questions, up to 10 points are assigned for each open question for a total of 20 points. The following criteria are applied to evaluate each answer: 

  • the answer demonstrates extensive knowledge and understanding of tested topic (up to 10 points);
  • the answer reveals critical analysis and ability to integrate different aspects of the literature (up to 8 points);
  • the answer is complete, well-written, and logically articulated (up to 6).

For multiple choice questions, the following criteria are applied to evaluate each answer:

  • The correct answer to each multiple choice question yields 1 point, for a total of 10 points.
  • Each wrong or not given answer is scored 0 points.

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

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.

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

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, Video Clips, scientific articles

Office hours

See the website of Francesca Starita

See the website of Giuseppe Di Pellegrino


Good health and well-being Industry, innovation and infrastructure

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.