Educational objectives This course aims to promote students’ theoretical and practical competences to get knowledge of the main theoretical models in the study of temperament and personality-related characteristics across the life-span, with particular attention to the role of individual differences in those interventions which goal is promoting well-being and preventing maladjustment across the life-span.
Expected learning goals are: competences in critical comprehension of theoretical models in Psychology of temperament and personality across the life-span, competences in the identification and analysis of individual differences in temperament and personality across the life-span, competences in preparation and management of universal intervention promoting well-being and preventing maladjustment.
Frontal lessons will promote students’ knowledge of basic principles that guide them in identifying indicators connected with individual differences’ examination aimed to promote well-being and prevent maladjustment across the life-span. In addition, students will have the opportunity to examine in depth recent studies about temperamental factors and personality development, as well as about the most effective universal evidence-based interventions.
Moreover the laboratory activities offers students the opportunity to acquire advanced practical knowledge that refers to the assessment procedures, as well as to the analysis of individual cases. Consistently with dimensional approach, the students will examine the main tools for assessing personalities and the different types of personality profiles associated with adaptive and maladaptive functioning.
The laboratory therefore guarantees the acquisition of practical and technical skills in the area of personality assessment in health, labor and legal forensic issues. Attendance at workshop classes is mandatory.
Specific aims. Knowledge and comprehension: passing the exam guarantee to be able to comprehend and to handle scientific tools to evaluate temperament and personality dimensions, as well as evidence-based intervention programs. Skills to apply knowledge and comprehension: passing the exam guarantee to be able to identify the indicators to evaluate personality across the life-span, and to be able to plan intervention programs directed to children, adolescents, and adults. Independent judgment: passing the exam imply getting the capacity to critically evaluate theoretical models and evaluation tools, being able to recognize correspondent advantages and disadvantages. In addition, passing the exam promotes capabilities related to planning promotion and prevention intervention programs. Those skills are acquired during the lessons through students’ exposure to scientific reports and case-studies on personality profiles during frontal lessons, during in-class group activities and also during laboratory activities. Communicative skills: passing the exams imply the capacity to effectively use communicative tools to present profiles and scientific reports focusing on temperament and personality factors. Learning skills: passing the exam imply acquisition of transverse learning skills that will allow students to examine theoretical and practical models, and related intervention programs, in depth across the course of their professional and academic career. Such learning skills are acquired during this course, with particular emphasis – especially during frontal lessons – to the discussion on theoretical models and profile analysis, and the presentation of alternative methods to prepare and to write promotion and prevention intervention programs and proposing - in the laboratory - case studies of children, adolescents and adults.
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Educational objectives Foreign students may use part of the available credits (6 CFU) to acquire linguistic knowledge in Italian enabling a B2 level.
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Educational objectives General Objectives
The course provides theoretical foundations for understanding cognitive processes through the practi-cal lens of computational modelling. Cognitive functions will be interpreted as computational problems and solved by information-processing architectures in particular connectionist (neural network) models. Students will acquire essential R programming skills to implement, simulate, analyse these models, and designing experiments to collect empirical data on key cognitive domains such as memory, perception and language. The course will endow students with concrete computer programming tools to build, run, analyse, and interpret computational models and learn how to relate their behaviour to empirical data. At the end of the course, students should have developed critical skills to design, evaluate and compare computational models that could predictively bridge theoretical principles and empirical evidence from behavioural and cognitive neuroscience.
Specific Objectives
Knowledge and understanding
EN: Students will acquire core concepts in Computational Cognitive Science, including Marr's levels of analysis and the motivations for computational modelling. They will gain an understanding of the principles and commitments of the three major modelling paradigms: Symbolic processing (overview), Connec-tionism (Neural Networks - deep dive), and the Bayesian framework for inference and learning (only briefly). They will learn about fundamental algorithms relevant to these approaches, such as the concep-tual basis of neural network training (Backpropagation) and acquire knowledge of computational architectures (e.g., associative networks or Convolutional Neural Networks; CNNs) to tackle cognitive problems in perception (e.g., categorisation), or memory (e.g., association) as well as simulate effects of altered pro-cessing in neural networks, which is relevant to examining neurodegenerative disorders. They will also gain knowledge of computational techniques in natural language processing, including N-grams, Distribu-tional Semantics (Word Embeddings), and Recurrent Neural Networks (RNNs). They will understand how these different models attempt to capture and predict patterns of human responses.
Applying knowledge and understanding
During the laboratory sessions, students will acquire practical skills in R programming, covering syn-tax, data structures, control flow, functions, data handling, and visualization. They will learn to implement key neural network models in R (e.g., perceptron, MLPs, CNNs) using libraries like “keras” and apply it to simulate cognitive processes (e.g., memory through associative networks). When looking at language, students will apply classic computational text processing techniques (N-grams), explore word embeddings and understand core concepts such as distributional semantics. They will also acquire practical skills in using experiment builder software (e.g., OpenSesame) to run simple cognitive experiments to obtain relevant data to be modelled. Overall, students will learn to develop computational models in the R language and understand how apply them to data from cognitive experiments.
Making judgments
By actively participating in lectures, reading assigned papers, and engaging in practical laboratory ac-tivities, students will develop critical thinking skills applied to computational cognitive science. They will learn to critically evaluate the theoretical assumptions, computational mechanisms, strengths, and limita-tions of the different modelling paradigms (e.g., connectionist vs. Bayesian) in explaining specific cogni-tive phenomena across perception, memory, and language domains. Students will learn to interpret the results and behaviour of computational models and critically assess their ability to account for empirical data from behavioural experiments and findings from cognitive neuroscience.
Communication skills
Students will develop written and oral scientific communication skills relevant to computational cognitive science research throughout the course. They will practice summarising and critically discussing research papers that employ computational modelling techniques. In class discussions, students will learn to clearly and effectively present the rationale, computational methods, results, and critical evaluation (including theoretical implications, strengths, weaknesses, and open questions) of studies involving computational models (Connectionist, Bayesian, Symbolic) applied to Memory, Perception, and Language, and their relevance to cognitive neuroscience.
Learning skills
Besides core course materials, students must read, understand, and critically engage with key scientific papers from the computational cognitive science literature. This experience will foster their skills in autonomous learning and critical analysis of primary research. They will develop the ability to extract the core computational ideas from different modeling paradigms (Connectionist, Bayesian, Symbolic), evaluate the methods and conclusions in the context of empirical evidence from Memory, Perception, and Language research, and identify potential future directions for research on human cognition and its neural basis using computational approaches.
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Educational objectives General aims
This course aims at providing an up-to date overview of the theories and research areas in the field of Social Neuroscience and offering a comprehensive view of the methods used in the field. The course has two main teaching goals. The first one is to promote an understaning of the social, clinical and technological potentiality of studies concerning social functions in neurotypical individuals, along the typical and atypical development as well as in psychiatric patients or patients with brain lesions. The second teaching goal is to strengthen the student ability to search, understand, report, and utilize information offered by scientific papers from the field of social neuroscience. Part of the course aims at presenting and let the students acquire knowledge concerning methods of social psychology (experimental designs, experimental manipulations, statistical approaches) through the study and discussion of scientific papers on social decisions and the influence of social variables, personality traits, emotions on decision processes.
Specific aims
- Understanding the cognitive and neural mechanisms supporting social functions (knowledge and under-standing).
- Understanding the relevance of social functions for the development of higher order cognitive functions, in clinical settings and for technological purposes (applying knowledge and understanding).
- Critically evaluate the methodological approach and theoretical impact of papers from the field of Social Neuroscience (making judgements).
- Ability to report and comment the content of scientific papers from social neuroscience field (communication skills).
- Development of general skills in deepening studies in fields related to social neuroscience such as neuropsychology, psychophysiology, behavioral neuroscience (learning skills).
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Educational objectives Apply knowledge, understanding, and problem-solving abilities in new or unfamiliar topics within multidisciplinary contexts.
Learning skills to study in a manner largely self-directed or autonomous.
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Educational objectives Students will be provided a solid background on the main techniques used to image the human brain in vivo, and of their application in the cognitive neuroscientific field; a critical view of the validity and the limits of knowledge on the human mind derived by the application of such methods; a set of practical abilities in planning and analyzing cognitive neuroimaging experiments; and a series of conceptual tools to personally and critically evaluate results obtained by research in the field of cognitive neuroimaging.
Knowledge and understanding: Students will understand the historical and conceptual foundations of cognitive neuroscience; will be able to fully appreciate the potentials and the limits of recording brain signals as a tool for understanding the functional architecture of the human mind; will know the basic technical characteristics of the main neuroimaging techniques; will master the main experimental paradigms employed in functional neuroimaging experiments; will understand the statistical foundations of data analysis as applied to neuroimaging data.
Applying knowledge and understanding: Students will become competent in planning and implementing cognitive tasks to be associated with neuroimaging techniques and research protocols for studying neurophysiological mechanisms underlying cognitive functions in clinical and pre-clinical fields and in the interpretation of imaging results, in designing full experiments, including the data analysis strategy, while avoiding common pitfalls and methodological problems.
Making judgements: Students will be able to read and fully understand papers in the cognitive neuroimaging literature and to critically evaluate their methods and conclusions, identifying their potential impact and conceptual and methodological issues.
Communication skills: Students will become competent in writing short project proposals and in presenting their proposals orally in a limited amount of time with the help of slides.
Learning skills: Students will develop instrumental and research skills useful for acquiring further knowledge.
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Educational objectives General aims
The curricular internship (TPV) is made of 20 CFU of practical activities contextualized and finalized to learning the abilities typical of the profession of psychologist. The TPV is divided in activities performed in the Institution of the course as well as in structures in convention (IRCCS Fondazione Santa Lucia, Istituto di Scienze e Tecnologie della Cognizione, Istituto di Biologia Cellulare e Neurobiologia). As for regulations, the activities will comprise the use of the instruments for intervention and prevention, diagnosis, habilitation and rehabilitation activities, psychological support for single individuals, groups, social bodies and communities, as well as for experimental, research and didactic activities.
Specific aims
Knowledge and understanding
Students will acquire knowledge and understanding of the different contexts in which psychologists work.
Applying knowledge and understanding
Students will develop the ability to autonomously apply professional methods and techniques and to put in practice their comprehension of typical cases and activities handled by professional psychologists in different contexts.
Making judgements
Students will become able to autonomously judge and evaluate the decisions that are taken in different contexts and with different aims put in place by psychologists.
Communication skills
Students will learn to properly communicate with patients, their relatives, and institutions as well as with professionals of different background in order to efficiently cooperate in different work settings.
Learning skills
Students will develop skills to learn in different practical contexts typical of the profession of psychologist ranging from the management of ethical and deontological issues with patients, to the management of research activities.
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