The Master Thesis reflects autonomous study on behalf of the student, and deals with a scientific topic of research interest in the area of cognitive systems. Emphasis can be on the psychological or computational side of things. Key elements are the innovation and scientific quality of the resulting research, contributing new knowledge to the scientific community.

The Master Thesis reflects autonomous study on behalf of the student, and deals with a scientific topic of research interest in the area of cognitive systems. Emphasis can be on the psychological or computational side of things. Key elements are the innovation and scientific quality of the resulting research, contributing new knowledge to the scientific community.

The goal of the course is to expose students to a comprehensive cross-section of the main elements of artificial cognitive systems. Inspired by artificial intelligence, developmental psychology, and cognitive neuroscience, our aim is to build systems that can act on their own to achieve goals: perceiving their environment, anticipating the need to act and the likely outcome of actions, learning from experience, adapting to changing circumstances, and interacting with humans. The course surveys the cognitivist, emergent, and hybrid paradigms of cognitive science and discusses cognitive architectures derived from them. Students will be guided through the main paradigms, modelling methods, and technologies involved in the design of cognitive artificial systems and will understand the theoretical and technical challenges involved in modelling and building systems that can reason, solve problems, acquire and use knowledge, make decisions. They will learn how and in which cases computational models can be used to help us in understanding human intelligence and in making progress towards more advanced AI systems. In this respect, the key issues of autonomy, embodiment, learning & development, memory, knowledge representation, decision making and evaluation are discussed. Concepts are introduced in an intuitive, natural order, with an emphasis on the relationships among ideas and building to an overview of the field, equipping students with sufficient knowledge and understanding to study specific topics in greater depth.

This module provides an in-depth survey of the extant data and models of a wide variety of human cognitive functions. Drawing on behavioral, neuropsychological, and neuroimaging research, the course explores the neural mechanisms underlying complex cognitive processes, such as attention, perception, memory, language, emotion and decision-making. Importantly, the module examines the logic and assumptions permitting the interpretation of brain activity in psychological terms. The module aims to provide students with a strong foundation in the field of Cognitive Neuroscience, a field that studies the intricate links between the mind, the brain, and behavior. Students first learn basics about brain anatomy and function, and about the methods used to study how the brain supports cognition and behavior. Then, the module explores the various functions of the brain by taking a journey from lower- to higher level cognitive processes: we are studying how we can sense and perceive the world, act in it, learn and think about it, and remember it. Current research issues (e.g., brain-behaviour interactions, modularity vs. brain connectivity) are discussed and supplemented with recent peer-reviewed journal articles. Class participation in presenting and critiquing these articles is expected. Moreover, this module provides an introduction into basic human functional neuroanatomy. Students will learn about the principles underlying the anatomical organisation and functional segregation of the human brain. Evolutionary and developmental origins of brain structure and function, as well as the relation between brain structure and function are also discussed.

The module aims to develop students’ knowledge and understanding of research design in experimental psychology. This course will provide students with theoretical knowledge and practical skills for designing experiments for behavioral research. The course will start by providing an in-depth analysis of experimental methodology and design. It will then proceed to provide students, by means of assignments, with hands-on experience in designing experiments, collecting data from human participants, analyzing and reporting the data, and interpreting statistical results. Through this course, students are expected to acquire all the necessary knowledge on how to investigate scientifically research questions of interest. Finally, students will develop the ability to analyze primary data, establish whether the data is statistically significant using appropriate tests, and communicate their findings in the form of scientific lab reports.

This interdisciplinary course aims to underline the importance of incorporating human factors in the design and development of adaptive interaction systems. Incorporating cognitive and emotional human factors into adaptive applications and processes enhances the user experience, usability and satisfaction while users interacting with hypermedia environments. Main thematic areas emphasize upon the convergence of psychological theories and contemporary research on computer-mediated information processing. It will initially cover topics in the field of cognitive psychology, to the extent that there is an analogy with hypermedia information; on psychological issues with regards to the role of cognitive processing and emotions on information assimilation and learning performance capabilities; on emotional intelligence within the context of Web-based interaction, etc. At a second level, topics on personalization; adaptivity; user modelling; multi-modal interactions; task and user analysis; (adaptive) user interface design principles; user interface evaluation and usability testing; will be covered, to support the adaptation issue in various application levels.

This course studies the development of cognitive agents with computational models of argumentation as the underlying foundation for cognitive human reasoning. It brings together elements from argumentation theory in AI with cognitive psychology of reasoning to study a new form of symbolic representation and reasoning that reflects cognitive reasoning processes in humans, which leads to a new style of cognitive programming for cognitive systems. Specific elements that are covered include: The logical nature of human reasoning — argumentative decision making. Argumentation theory and Argumentation Logic in CS. Conditional human logic and argumentation logic. Cognitive reasoning about actions and change. Cognitive Knowledge Representation of common sense world knowledge and Cognitive Programming. Explanation, justification and persuasion through human-system argumentative dialogues. Argumentation-based agent architectures for adaptive agents. Application of cognitive agents to personalized and adaptive recommender systems and to elements of story comprehension.

We will take Natural Language Processing in a wide sense to cover computer manipulation of natural language. At one extreme, it could be as simple as counting word frequencies to compare different writing styles. At the other extreme, NLP involves “understanding” complete human utterances, at least to the extent of being able to give useful responses to them. The course has also a practical sense of introduction to natural language processing. Students will learn to use and formulate methods and systems that analyze text, write real programs, and grasp the value of being able to test an idea through implementation. The course strikes a balance between theory and application, identifying the connections and the tensions. The course aims to be also pleasurable, including applications and examples that are interesting and entertaining, and sometimes whimsical.