This elective course aims to provide an interdisciplinary approach to the notion of Consciousness. We will discuss whether consciousness is necessary for a variety of cognitive functions and will explore how neuroscience can contribute to the better understanding of conscious (or unconscious) experience.
Goals:
Description/Content:
The course will include lectures and presentations of research articles with class discussions.
Description/Content:
This course is an in depth exploration of the cellular, synaptic and network basis of brain function. The lectures will build upon the material covered in the Introductory Neuroscience course and will focus on selected topics such as: synaptic mechanisms of learning and memory, the role of neuromodulation, excitation-inhibition balance in health and neuropathology, neurobiology of neurological and mental disorders.
We will also present and discuss state-of-the-art methodologies such as optogenetic manipulation of neuronal activity, deep brain stimulation, transcranial magnetic stimulation, etc.
The course will include lectures and presentations of research articles with class discussions.
Student assessment:
Suggested bibliography:
Selected articles, such as:
Learning is the process by which an individual or an organism acquires knowledge, skills, attitudes, or behaviors through experience, instruction, or study. We will examine the psychological mechanisms of learning and memory, as well as how these mechanisms are implemented in the brain. We will also focus on critical periods during development, brain plasticity, the role of mental representations, and the influence of attention, active engagement, errors, and consolidation of information on the learning process.
The course presents the evolving discipline of neuropsychology and its main current practices today. Topics include: the functional organization of the brain, the basic elements of neuroanatomy, and definitions of cognitive functions (e.g., attention, memory, language). Particular emphasis will be placed on theories of executive functions, their definitions, and their prominent role in regulating behavior. The role and basic principles of clinical neuropsychological assessment will be developed and discussed through the presentation of assessment methodology (psychometrics, data collection) and its usefulness in various settings. Clinical syndromes (e.g., amnesia, aphasia, agnosia, neglect) will be presented and discussed, and selected case studies will comprise the examination, data collection, results, interpretation, and clinical impressions. The contribution of Neuropsychology to Psychiatry and Neurology will be documented and its current status will be presented.
Learning Outcomes
Students will:
Become familiar with the basic principles and main applications of neuropsychology.
Be able to distinguish between the clinical and research practices of neuropsychology.
Will learn about the role of neuropsychology as a specialty in a modern interdisciplinary environment.
Will learn about cognitive domains and neuroanatomy through the presentation of syndromes and case studies.
They will learn about the usefulness of popular neuropsychological tools.
They will be able to distinguish the central role of executive functions and the particularities of their assessment.
The objective of the course is to familiarize students on the one hand with the fundamental approaches, methodologies and techniques involved in the synthesis and the design of intelligent individuals that exist and act within a social group, and on the other hand with the collective, emergent intelligence of the social group itself. All the major distributed/collective approaches are surveyed: distributed AI, decentralized AI, multi-agent systems, artificial life, social simulation, complex systems, complex networks. The investigated systems and models span a broad range of phenomena: biological, social, economic, communicative, evolutionary, human group models, social structure models, such as norms or regulations, etc. Besides essays and assignments, the students must carry out substantial experimental work with real systems and models related to synthesis, simulation and experimental analysis of computational social systems (agent-based or network-based)
Course Objective
The aim of the course is for students to become familiar with the modern field of computational neuroscience and to acquire modeling skills related to cognitive/perceptual behavior and the encoding and processing of information in neural systems. It is important that students already possess basic programming skills. Indicative topics include:
This course is an introduction to the technologies of the Semantic Web and Linked Data. The topics covered are:
Open data. Knowledge graphs.
Ontologies. Modern applications. Linked data. The RDF model. RDFS ontology model. The SPARQL query language. Geospatial data. The GeoSPARQL and stSPARQL query languages. Description logics. The OWL ontology language.
Rule languages. Ontology mechanics.
The Machine Learning course covers both the foundations and recent developments in the area of machine learning from a statistical learning perspective, with an emphasis on the interplay between statistical data modeling and mathematical optimization. The course content is organized as follows. Initially, basic concepts related to data representation (e.g., images, audio, text) and feature extraction are introduced.
As a closure, statistical models and optimization algorithms for regression, classification, clustering, dimensionality reduction and feature selection are presented as well as local methods, kernel methods and support vector machines. The second part of the course focuses on deep learning networks (deep neural networks). The elements of neural networks, training algorithms and network architectures will be discussed.
This course discusses main concepts in phonetics and phonology in relation to Greek and other languages. These include the articulation, perception and acoustic properties of sounds, the International Phonetic Alphabet (IPA), the differences between phonemes and allophones, the analysis of language data, the syllable and other suprasegmental features of speech such as stress, rhythm and intonation as well as connected speech processes such as assimilation, deletion, and insertion.
This course serves as an introduction to the theoretical analysis of syntax. It presents how phrases and sentences are structured and generated in natural languages, while delving into methodologies for analyzing syntactic phenomena. Topics covered include phrase structure, case marking, functional categories and their mapping, movement and its constraints, and binding theory.
The primary theoretical framework for description is the Principles & Parameters Theory (Chomsky, 1981), as expressed in its latest formulation within the Government & Binding framework (Chomsky & Lasnik, 1993) and its modern developments in the Minimalist Program (Chomsky, 1993, 1995).
The aim of this course is to introduce students to the interdisciplinary fields of Psycholinguistics and Neurolinguistics. More specifically, the course addresses topics related to language production and comprehension, the development of spoken language in children, second language acquisition and bilingualism, language communication disorders —both developmental and acquired— the relationship between language and other cognitive systems, and the neural bases of language. Particular emphasis is placed on both methodological and theoretical approaches within these two fields.
This course presents theoretical and methodological issues that concern the field of First Language Acquisition. It analyzes the Logical Problem of Language Acquisition, the role of innate predisposition and the environment in acquiring a first language. Moreover, the main stages of children’s language development are presented according to the levels of linguistic analysis, along with the major theoretical approaches that attempt to explain this development. Specifically, the course examines:
Finally, the course discusses the main methodological approaches for collecting linguistic data in the study of language acquisition, including the collection of child language data, as well as timed and untimed experimental tasks of language production and comprehension.
The course lectures focus on Natural Language processing as a special type of input data in Human-Machine/Robot Interaction and Communication Systems (HCI, HRI, Dialog Systems), Speech Processing (ASR-TTS, Prosodic Modelling), Information Extraction, Retrieval and Mining (Information Extraction, Information Retrieval, Data Mining) and the Processing of information concerning the intention, opinion and/or emotional state of a User (Sentiment Analysis, Opinion Mining). Natural Language is examined in relation to monolingual and multilingual applications as well as User Modelling.
This course can be only attended by students who are familiar in programming with MATLAB and want to learn more about this way it can be applied in basic research. They will learn how to use MATLAB in order to design and perform experiments, as well as to acquire, process and present experimental data. An emphasis will be given in the field of cognitive science, where students will be asked to solve specific tasks such as presenting stimuli in behavioral experiments, collecting responses, data processing & presentation etc.
The philosophy of perception explores the nature of our sensory experience and its relationship to reality. The course introduces the subject thematically, outlining the main theories of perception, their motivations, and the associated problems. This comprehensive overview focuses on both a historical background to debates in the field and recent presentations and defenses of different theories. The course is divided into two main parts: the first deals with philosophical theories of perception, and the second covers basic themes and questions of perception as they are discussed in philosophy, cognitive science, and psychology.
This course will have a variable and flexible structure so that it can include the latest developments in the field of Cognitive Science, and/or lectures by visiting professors with specializations related to Cognitive Science that are not covered in other courses, or lectures related to courses not taught that year.
General discussion on the concept of "Knowledge".
Brief presentation of the prolog language.
Knowledge-based systems (KRR: the "traditional approach").
Methodologies of representation and reasoning: Rule-based systems, Semantic networks, Frames, the Object-oriented model.
Brief presentation of the CLIPS system. Brief presentation of the FLEX system.
Propositional logic. First-Order Logic.
Non-valid inference rules (abduction, induction, analogical inference).
Description logics - Semantic Web (KRR: the "modern approach").
Brief presentation of the Protégé system.
Deductive databases: logic as a data model (the case of datalog).
Reasoning under uncertainty: Probability theory, the PROSPECTOR system's model, Buchanan and Shortliff's Certainty Theory, Dempster-Shafer's Evidence Theory, Possibilistic logic, Fuzzy logic, Many-valued logics.
Aim of the course: Students are introduced into the fundamentals of knowledge representation and reasoning, the knowledge-based systems and their development environments. The above also refer to knowledge under uncertainty.
Finally, students get acquainted with modern knowledge representation applications areas.
The purpose of the course is the study of the acquisition/learning of semantic phenomena in natural language in preschool and school-age children. More specifically, we will focus on phenomena such as anaphora resolution, the distinction between count and mass noun phrases, definite and indefinite determiners, quantifiers, conversational implicatures, etc. Our study tools will include cutting-edge articles and theoretical and experimental studies on the phenomena under consideration. Students' output will involve: a) an examination of how these phenomena are presented in school or non-school grammars, and b) the creation of audiovisual material to enhance understanding and teaching of these phenomena.
The rapid development of internet technologies has resulted in them becoming an essential part of how we live our daily lives. Based on the findings of recent research, this course attempts to answer several important questions, such as: a) what is the impact of digital technologies on learning, memory, attention, problem solving, and decision making, b) will the widespread use of the internet and its applications change the way we think, and c) can human cognition keep pace with technology? At the same time, the effects of multitasking and excessive use of the internet on the anatomy and function of the brain, the relationships between social networks and the socially networked brain, as well as the cyberpsychology of digital games are explored.
The course has two main objectives: (a) to examine the cognitive processes we activate when using internet applications and how they differ from those we use in the real world, and (b) to study how the digital world affects us psychologically, especially in terms of our cognitive processes.
The aim of this course is to investigate the cognitive mechanisms involved in the learning that requires conceptual change -that is, learning that challenges and restructures individuals' initial intuitive theories and beliefs. Various theoretical perspectives on conceptual change are presented, such as constructivist and sociocultural approaches. The course also examines the different research methodologies used to investigate conceptual change and how these methods inform our understanding of the learning processes. Examples of conceptual change in different scientific fields are discussed. Finally, the course addresses the types of instructional interventions that facilitate conceptual change and foster scientific thinking.
The course focuses on the study of specific topics in Human Computer Interaction (HCI) through the design and development of interactive systems. Emphasis is placed on advanced and modern interaction environments, such as immersive Virtual Reality, Augmented Reality, Mixed Reality and computer games, as well as related topics, e.g. interaction patterns & techniques (selection, manipulation, navigation, multimodal interaction), spatial & temporal perception, immersion & presence, etc. As part of the course, students are asked to design, prototype and implement a two-dimensional or three-dimensional application (e.g. interactive game, virtual reality application), following all stages of iterative design with the user at the center (user-centered design).
In this course, students will gain an in-depth understanding of contemporary methods for analyzing complex systems, as well as skills in modeling neural activity and processes related to cognitive/perceptual behavior and information processing in neural systems. Students are expected to have basic programming knowledge.
This course aims to introduce students to both the theoretical foundations and the practical applications of cognitive science in education. Teaching practices aligned with various learning theories are presented, including constructivism, sociocultural theories, information-processing theories, motivational theories, developmental theories, and core-knowledge theories. The course examines key cognitive issues relevant to thinking and learning in complex academic domains such as mathematics, physics, and engineering. More specifically, the course explores (i) the types of knowledge and cognitive processes required for effective learning and academic success, (ii) the cognitive challenges learners face when dealing with unfamiliar concepts or when they have naive background knowledge, (iii) instructional methods that support learners in acquiring essential knowledge and thinking skills, and (iv) strategies for educators to implement these methods in classroom setting.
