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Artificial Intelligence and Neurocognition


Entry requirements

  • Introduction to Psychology

  • Cognitive Psychology or Consciousness (or similar courses)


Artificial Intelligence (AI) has become an important field of study and has already achieved many applications. The study of the brain plays an important role in this field because it can reveal how neural processes in the brain result in forms of intelligence such as pattern recognition. It can be expected that the influence of AI will increase in the coming years and decades, with potential effects also on work environments in psychology. This course gives a basic introduction into aspects of AI. It is divided over two approaches, one related to the more classical approach in AI (based on computing in standard computers) and one inspired by neural processing in the brain (Deep Learning). Both approaches are briefly described below:

Classical AI: In the first part of the course, an introduction is given to the history of the field of artificial intelligence, and how it distinguishes itself from the related fields of computer science, psychology, and mathematics. Several graph traversal algorithms and heuristics (branch-and-bound, hill climbing) are discussed, as well as broadly applicable techniques such as backtracking. Also, an introduction into cognitive robotics is given, illustrating how implementing simple decision rules in a physical embodiment can create artificially intelligent robots.

Neurocognition: In this part of the course we will discus pattern classification and recognition in neural networks (Deep Learning), based on a model of pattern classification and recognition in the brain. A number of topics and mathematical concepts will be discussed and presented. They include: information, frame of reference, pattern classification, vector representation, state space of a layer in a neural network, input space for a (neuron in a) network, perceptrons, linear separability and its relation to pattern classification, receptive fields, Gabor filters as models for receptive fields, unsupervised and supervised learning.

Course objectives

Students will acquire knowledge of:

  • aspects of the fields of artificial intelligence, robotics and neurocognition, and their potential influence on society and work environments (also in psychology);

  • examples of how complex behaviour and cognition emerges from different architectures of neural networks and forms of computation;

  • how neural networks can be trained;

  • basic analysis of forms of patterns classification.


For the timetables of your lectures, workgroups, and exams, select your study programme.
Psychology timetables

Lectures Exams



Students need to register for lectures, workgroups and exams.
Instructions for registration in courses for the 2nd and 3rd year


Elective students have to enroll for each course separately. For admission requirements contact your study advisor.

Exchange/Study abroad

For admission requirements, please contact your exchange coordinator.


Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date; students who are not registered will not be permitted to take the examination.
Registering for exams

Mode of instruction

The course consists of 8 2-hour lectures.

Assessment method

There is a written exam consisting of open questions. They are based on the literature and the lectures.

The Institute of Psychology uses fixed rules for grade calculation and compulsory attendance. It also follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these three policies.

Reading list

Articles from journals. The final list will be announced on the blackboard at the start of the course. An impression of articles that are used:

  • Oram, M.W. & Perrett, D. I. (1994). Modeling Visual Recognition From Neurobiological Constraints. Neural Networks, 7, 945-972.

  • DiCarlo, J. J. & Cox, D. D. (2007). Untangling invariant object recognition. Trends in Cognitive Sciences., 11, 333-341.

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX, 236, 433-460.

  • McClelland, J. L. (2009). Is a machine realization of truly human-like intelligence achievable? Cognitive Computation, 1, 17-21.

Contact information