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Cognitive Processes

Code: 106577
Credits: 6
2026/2027
Degree programme Type Course
Bachelor in Artificial Intelligence FB 2

Contact lecturer

Name :
Daniel Pacheco Estefan
Email :
daniel.pacheco@uab.cat

Teaching staff

Daniel Pacheco Estefan

Group languages

You can consult this information at the end of the document.

Prerequisites

No prerequisites are required.

Objectives

The goal of this course is to provide a multidisciplinary approach to AI research by integrating the latest advances in cognitive neuroscience. Today, the human brain is the most advanced and efficient information processor known, making it a crucial biological model for current and future AI systems. Students will explore cutting-edge theories on how the brain processes information and performs various cognitive functions, such as learning, memory, perception, language, decision-making, and emotion. The course will also examine how these functions are currently implemented in AI models, alongside a detailed description of the corresponding human cognitive processes.

Learning outcomes

  • CM11 (Integrate knowledge of biases derived from human cognitive and social processes in project management and development to ensure an equitable and non-discriminatory contribution from all team members.) Integrate knowledge of biases derived from human cognitive and social processes in project management and development to ensure an equitable and non-discriminatory contribution from all team members.
  • KM26 (Identify the biological underpinnings of the main cognitive functions involved in human behavior related to sensory modes of information capture and processing, verbal and nonverbal language, thinking and learning, and emotions.) Identify the biological underpinnings of the main cognitive functions involved in human behavior related to sensory modes of information capture and processing, verbal and nonverbal language, thinking and learning, and emotions.
  • SM28 (Relate concepts of cognitive processes and human neural mechanisms to the design and analysis of machine learning and reasoning algorithms.) Relate concepts of cognitive processes and human neural mechanisms to the design and analysis of machine learning and reasoning algorithms.

Contents

1. An introduction to cognitive psychology (3 sessions)



  1. Historical evolution of cognitive neuroscience

  2. The brain as an information processing system

  3. Introduction to the methodology in cognitive neuroscience


2. Attention (1 session)



  1. What is attention?

  2. Models of attention


3. Perception (2 sessions)



  1. What is to perceive?

  2. Perceptual organization

  3. Sensory modalities


4. Learning (1 session)



  1. What is learning?

  2. Types of learning

  3. Mechanisms of learning


5. Memory (2 sessions)



  1. What is memory? (2 sessions)

  2. Memory systems

  3. Neurobiology of Memory

  4. Memory models

  5. Memory and Spatial Navigation


6. Decision-making (1 session)



  1. The brain as a decision-making agent.

  2. Perceptual and value-based decisions.

  3. Neural mechanisms of decision

  4. Neuroeconomics and heuristics


7. Emotion & Motivation (1 session)



  1. What are emotions?

  2. Theories of emotion

  3. Emotion and mood

  4. Mood disorders


8. Language & Consciousness (1 session)



  1. Theories of Consciousness

  2. The language system in the brain

  3. Large Language Models


 

Learning activities and methodology

Title Hours ECTS Learning outcomes
Team work 20 0.8
Individual Study 50 2
Master classes 24 0.96
Seminars 24 0.96
Tutoring (group and individual) 20 0.8

Teaching Methodology

The teaching methodology is based on various formative activities. Over the 12.5 weeks of the course, lectures, seminars, workshops, supervised activities, and independent activities will be scheduled.

Type: Directed (50 hours)

  • Lectures
  • Seminars (PAUL)
  • Assessment

Type: Supervised (20 hours)

  • Tutorials (group and individual)

Type: Independent (55 hours)

  • Study
  • Teamwork
  • Preparation of oral presentations

Within the schedule established by the center or the curriculum, 15 minutes of one class will be reserved for students to evaluate the teaching staff and the subjects or modules through questionnaires.

Use of Artificial Intelligence

For this subject, the use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks, such as bibliographic or information searches, text correction, or translations.
The student must clearly identify which parts were generated using this technology, specify the tools used, and include a critical reflection on how these tools influenced the process and the final result of the activity.
Lack of transparency in the use of AI in an assessable activity will be considered academic dishonesty and may result in partial or total penalties in the activity's grade, or more severe sanctions in serious cases.

Gender Perspective

All teaching materials provided by the teaching staff, as well as students’ written work and oral presentations, must avoid the use of sexist language.
Inclusive and non-discriminatory language must be used consistently to promote gender equality and respect for all identities.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Laboratory practices (PLAB) 30% 4 0.16 CM11
Seminars (PAUL) 20% 4 0.16 CM11, KM26
Second Partial Exam 25% 2 0.08 KM26, SM28
First Partial Exam 25% 2 0.08 KM26, SM28

Assessment

The evaluation of this subject is conducted continuously and has a clear formative purpose. The competencies associated with this subject will be assessed through follow-up activities, group presentations and reports, as well as exams. The learning evidence that students must submit will reflect the content and competencies addressed in theoretical classes, seminars, and laboratory practicals.

The evaluation system is structured around five types of evidence, each contributing a specific weight to the final grade:

  • Evidence 1 (Master Class Evaluation): Exams
  • A) Midterm exam (25%)
  • B) Final exam covering the second half of the semester (25%)
  • Evidence 2 (Seminar Evaluation):
  • Group presentation of a manuscript (20%)
  • Evidence 3 (Laboratory Session Evaluation):
  • A) Presentation sessions (15%)
  • B) Written report (15%)

Passing Criteria

To pass the subject, students must meet the following conditions:

  1. Achieve an average score of at least 3.5 in the exams under Evidence 1.
  2. Obtain an overall average score of 5.0 or higher across all five evidences.
  3. Submit at least 4 out of the 5 required evidences.

Failure to meet the third criterion (i.e., submitting fewer than 4 evidences) will result in the subject being marked as Not Assessable.

Attendance

Attendance is mandatory for all PLAB and PAUL sessions. Any absences must be justified with an official document.

Recovery Test

To be allowed to opt for a recovery test the student has to fulfill the following

requisites: 1) having presented 4 of the 5 evidences; 2) to have a grade equal to or

greater than 3.5 points and less than 5 points on average in the exams that

correspond to Evidence 1.

The recovery test will consist of an exam about all the subject, that will contain

questions about all the theoretical contents. The maximum grade that can be

obtained in the course, in case of passing the recovery test, will be Approved (5

points).

Single Assessment

This subject does not offer the option of a single assessment (i.e., a single final evaluation).

Academic Integrity

In cases of copying, plagiarism, or similar misconduct, the grade for the affected activity will be zero (0), without prejudice to any additional disciplinary actions deemed appropriate.

Bibliography

Eysenck, M.W. & Keane, M.T. (2020). Cognitive Psychology. A Student’s Handbook. Routledge.

Eysenk, M.W. & Groome, D. (2015). Cognitive Psychology: Revisiting the classic studies.

Gazzaniga, M. S., & Mangun, G. R. (Eds.). The cognitive neurosciences (5th ed.). Boston Review.

Churchland, P. S., & Sejnowski, T. J. (1992). The computational brain. The MIT Press.

Goldstein, E. B. Sensation and perception (8th ed.). Wadsworth.

Software

Bringing a personal laptop may be required for some lectures. Specific dates will be communicated by the lecturer.

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 71 English first semester afternoon