Cognitive neuroscience is the study of the mind and brain, investigating how and why people perceive, think and act the way they do. The knowledge and skills gained in the Master's course will provide a foundation for advanced scientific research, but also prepare for professional applications in the fields of education, consumer and economic decision making, psychology and clinical research.
The Master's course in Cognitive Neuroscience at the University of Trento provides research-focused training with a diverse, international group of faculty and researchers.
The two year program combines courses in neuroscience, cognition, statistics, advanced signal and data analysis with hands-on training in cutting-edge research techniques. These include functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), magnetic encephalography (MEG), computational modeling, comparative cognition (animal models), EEG, eye tracking, cinematic motion tracking and psychophysics.
Courses (mandatory and elective)
Note: According to the Sorbonne (1998) and the Bologna (1999) Declarations, degree courses in the European Higher Education Area are structured in university credits = ECTS. A university credit corresponds to 25 hours of student workload, time for personal study included. The average annual workload of a full-time student is conventionally fixed at 60 credits.
Foundations of Cognitive Psychology (9 ECTS, mandatory)
Cognitive Psychology is the study of the mental processes underlying our ability to perceive, pay attention, think, categorize, use language and remember. Historically, cognitive psychology began with the information processing approach but we will also explore recent research on topics such as emotions and numerical cognition, and will include insights from neuropsychology, neuroimaging and lifespan development. The teaching methods will include demonstrations, class discussion and lectures and will emphasize the critical link between theory and experimentation.
Basic Biology (6 ECTS, mandatory)
This course will cover the biological foundations of neuroscience, including cell biology, neuroanatomy, neurophysiology and neuropharmacology. The aim is to give the student a firm background in the biological basis of brain function. These fundamental principles will be taught using examples from recent studies pertinent to the field of cognitive neuroscience.
Foundations of Cognitive Neuroscience (9 ECTS, mandatory)
This course will examine the neural basis of higher mental functions, including brain systems supporting perception, object recognition, attention, memory, spatial functions, language, and decision making. We will explore the neuroanatomical and neurophysiological basis of cognitive functions, considering evidence form functional neuroimaging and patients studies. Cognitive neuroscience approaches to disorders such as autism, schizophrenia, and Alzheimer’s disease will also be explored. The teaching methods will include lectures, demonstrations, patient videos, class discussion and practical sessions in different neuroimaging labs. The course will concentrate on language, memory, perception and attentional mechanisms.
Neuroscience (6 ECTS, mandatory)
This course will look at a number of the major neural systems in detail, examining their structure and function. Contemporary studies will provide much of the teaching material and a strong emphasis will be placed on the latest developments in each field. Subjects to be covered will include the visual system, the auditory system, motor pathways, attention mechanisms, eye movements and memory.
Research Design (9 ECTS, mandatory)
This course will cover some fundamentals of algebra, probability theory, and statistics. Furthermore the course will cover all aspects of a research project, such as , sample sizes, measures, and type of experimental designs. Students will present and comment on their own research projects in progress. Discussions also include presentations of research to various audiences, abstracts, reviews, grant process, and scientific ethics.
Computational methods for data analysis (6 ECTS, mandatory)
Machine learning is the essential key in solving complex problems in several research area such as HCI, pattern recognition, natural language processing, computer vision, etc. The goal of this course is to provide the basic elements in machine learning that will allow the students to understand further problems in the abovementioned areas. The course will cover supervised and unsupervised learning and will focus on clustering, classification, Support Vector Machines, and Bayesian Networks.
Intro to Human Language (6 ECTS, mandatory)
This module is an introduction to language science (linguistics) covering phonetics and phonology, morphology and lexical knowledge, syntax, phrase semantics, discourse, and anaphora. No previous knowledge of linguistics is required.
Foundations of Brain Imaging (6 ECTS, elective)
This course will cover basic methodology and application of the main neuroimaging techniques used in cognitive neuroscience, such as functional and structural Magnetic Resonance, Transcranial Magnetic Stimulation, Magnetoenchelalography and EEG.
Independent Studies (6 ECTS, elective)
This course will entail reading and presenting papers and experiments from the recent literature that cover topics studied in the courses fundamentals of cognitive psychology and neuroscience.
Cognitive Linguistics (6 ECTS, elective)
The course focuses on how language semantically organizes information in the mind. The course presents the fundamental notions of CL: space and cognitive grammar,radial and schematic networks, conceptual metaphors, image-schema, mental space, base-and prototype categorization. Knowledge from this course can be applied to the following fields: bilingualism (L1 and L2 teaching), information retrieval based on images, branding and web marketing, construction of web pages, virtual reality systems, visual communication of visual interfaces, and language of signs.
Hands on fMRI data analysis (6 ECTS, elective)
This class focuses on fMRI data analysis, i.e. the statistics of fMRI data analysis and how that should influence your design decisions. By understanding the statistical concepts of fMRI data analysis, students will understand the rationale of the preprocessing pipeline in fMRI and the types of choises fMRI reserachers have to make when designing their esperiments. By actually modelling and analyzing fMRI data students will get a deeper understanding of fMRI data analysis and at the same time gain experience that will make it easier for them to read fMRI papers and to perform their own imaging studies in the future.
Neural decoding (6 ECTS, elective)
The neural basis of decision making (9 ECTS, elective)
Throughout the course, the primary goals are to: (1)Learn about the academic field of decision neuroscience, its major theories, results, and debates.(2)Become a critical consumer of research findings by learning the methodological standards for evaluating the soundness of such studies.(3)Develop the ability to effectively write and speak about decision theories, results, and debates.(4)Acquire some practical skills for designing and analyzing an experiment in the field of decision neuroscience.
For the second year an extensive internship and a research project leading to the Master's dissertation are scheduled.