Applied Practical Activity - TPV
According to the provisions of Interministerial Decree No. 654 of 05/07/2022, starting from the academic year 2023/24, all students enrolled in a master's degree program in psychology (LM-51), must obligatorily obtain 20 credits through participation in applied practical internship activities as part of their study program.
For the completion of the applied practical activity (TPV), students enrolled in the Master's Degree Program In Psychology, Neuroscience, And Human Sciences can choose ONE of the following three tracks.
The TPV CANNOT start before students have completed their first year of the Master program
START HERE:
This suggested track includes two parts:
500 hours internship to be carried out at an external institution
FIELD LABS
Students earn credits for Field Labs by attending the course (with a minimum attendance of 80%), which is in seminar format. These are interactive courses whose assessment is performed during the exercises themselves, through the analysis and discussion of practical cases.
To register you will have to express your preferences filling out a registration form for each of the course you wish to attend. You will find the google forms by clicking on enrollment for each field lab.
Please remember that Field Labs are activities held in presence and will not be recorded and uploaded on Kiro. So enroll accordingly to the Calendar for each class.
Since the nature of the courses is practical each course has a participants limit of 25 students to allow said participation, if the course selected as preference is full, you will be asked to select other courses. You will remain in a waiting list if any of your colleagues withdraws from the course you are interested in.
Students in the waiting list will be contacted if any seats will become available.The number of courses available is planned to be enough to allow all of you to complete the ECTS required from your study plan during the course of the entire year, please do not register in all the courses of the first slot if this is not for interest or specific ECTS needs.
The following table shows the list of the field labs that will be delivered in June 2026. Other Field Labs will take place in a February session.
REGISTRATION WILL CLOSE ON May 17th, 12:00 p.m
FIELD LABS CALENDAR JUNE 2026 --> here
From June 8th to June 19th
| Lecturer | Title | Abstract | enrollment | |
| De Luca | Vanni | Memory Techniques and Strategic Learning: Advanced Tools for University Study | The course offers a practical and structured introduction to the main memory techniques and learning strategies used by memory professionals, adapted to the academic context. Students will learn how to organize and encode information effectively, significantly improving their study skills, comprehension, and recall abilities. | https://forms.gle/15cs7eBe5gMar9TV9 |
| Fiorina | Maria Laura | Interaction between law and neuroscience. Focus on punishment | This course explores how advances in neuroscience challenge and reshape concepts of criminal responsibility, free will, and punishment within modern legal systems. Drawing on contemporary debates in neurolaw, students will examine how neuroscientific evidence can influence the understanding of human behavior, criminal liability, and judicial decision-making. | https://forms.gle/ziFxaftn5mdL2DjNA |
| Garbuglia | Francesca | Behavioral Economics: Some Elements of Theory and Practice | a brief introductory practice course on behavioral economics which explores how psychological factors shape real-world economic decision-making. It focuses on how individuals deviate from full rationality due to heuristics and cognitive biases. The course examines key concepts such as loss aversion, present bias, and social preferences through practical examples and experiments. It also highlights real-world applications in policy, marketing, and finance, including the use of nudges to influence behavior | https://forms.gle/aaJ8wcZ2pk5aL1tG6 |
| Gelli | Velia | Co-constructing the Story of Neuroscience: a dialectical approach to historical narrative. | Hosted by the Camillo Golgi Museum, we will be discussing the influence of our relationships and surroundings in our activities as neuroscientists. This is a highly interactive and experiential group foray into the personal and anthropological contexts of what we research. Please bring your laptops, as we will need them to prepare - and entertain with - short presentations | https://forms.gle/tprjJZFL53M7EEU1A |
| Lanciano | Tiziana | Flashbulb Memories: From Emotional Experience to Applied Contexts | Flashbulb memories (FBMs) are vivid and long-lasting recollections of the circumstances in which individuals learn about emotionally significant events. This course will explore the cognitive and emotional mechanisms underlying their formation, focusing on phenomenological features such as specificity and confidence, and their relationship with memory accuracy. Adopting an applied perspective, the course will discuss how research on FBMs can inform real-world contexts such as eyewitness testimony, credibility assessment, and the evaluation of autobiographical reports. Students will also engage with experimental paradigms and tools used to investigate true and fabricated memories (e.g., aIAT, FBM checklist), reflecting on their methodological and practical implications. | https://forms.gle/z32F4yb3zjJAvS6e9 |
| Mastrotheodoros | Stefanos | Introduction to Structural Equation Modeling with Mplus and R | Structural Equation Modeling (SEM) is a sophisticated statistical approach applied in the social and behavioral sciences to examine causal pathways and interdependencies among variables. This seminar focuses on the assumptions and possible applications of SEM for developmental research and provides you with hands-on knowledge on how to test these models in both Mplus and R software packages. This seminar is aimed at researchers with a basic understanding of statistical assumptions and a general experience in the use of either or both software programs. To engage in the practical exercises, we recommend you have a dataset on which to practice and at least one of these programs installed on your computer. Longitudinal data is often of utmost importance to developmental scientists. Analyzing longitudinal data not only allows us to better understand how young people develop, but also to better comprehend within-person processes controlling for between-person differences. In this way, longitudinal data can help distinguish developmental processes from individual differences. Furthermore, longitudinal data can inform us about individual differences in developmental processes. Therefore, applying techniques to analyze longitudinal data is a necessary skill for researchers studying development and developmental processes. In this seminar, you will acquire hands-on knowledge on conducting advanced SEM analyses to study developmental order and processes controlling for individual differences (Random-Intercept Cross Lagged Panel Models), growth (Latent Growth Curve Models), and individual differences in developmental processes (Latent Class Growth Analysis/Growth Mixture Models). We will use both Mplus and R. In light of recent critical discourses, we will discuss between-person and within-person models, and how to choose the right analyses for your research questions. We will further provide a glimpse into the modelling of intense longitudinal data (e.g., DSEM). This seminar is aimed at researchers who want to extend their knowledge about SEM modelling techniques, and learn how to use these models in their own analyses. Participants should be familiar with basic SEM models (e.g., path analysis, growth models) and should have experience with running analyses in Mplus and/or R. To follow the course and practical exercises, at least one of these programs should be installed on your computer. | https://forms.gle/2yb83e2g4ZPny1XDA |
| Pasqualotto | Angela | Digital-Based Tools for 'Learning to Learn': Practical Applications and Research Insights. | This course explores the potential of digital-based tools to foster 'learning to learn' skills, with a focus on their application in education and cognitive development. Participants will gain insights into how digital interventions, such as game-based learning and adaptive systems, can enhance cognitive and literacy skills. Through a combination of lectures and interactive workshops, the course will explore the design principles behind effective digital tools, strategies for personalizing learning experiences, and practical methods to evaluate their impact. Collaborative activities will foster critical thinking and hands-on experience, ensuring an engaging and practical learning journey. | https://forms.gle/jFW71byNLfvrDmWK6 |
| Riva | Valentina | Individual Variability and Developmental Trajectories in Autism During the First Three Years of Life: Clinical and Experimental Markers for Early Detection | Early identification of autism is crucial for the development of targeted interventions to be applied at a young age. The heterogeneity of autism is evident in individual variation, not only in the severity of core symptoms but also in cognitive, language, and behavioral skills, which exhibit different developmental trajectories. In this course, I will present studies on the early identification of autism during the first three years of life, with a specific focus on high-risk populations, particularly siblings of children with autism. These studies follow infant siblings of autistic children and neurotypical controls from 6 to 36 months of age using a multi- observational protocol that integrates both clinical and experimental measures. Students will explore both experimental and clinical approaches to the early detection of autism, including behavioral markers, observational techniques, and standardized assessment tools. A key component of the course will be the introduction of teleNIDA, a novel instrument for the early identification of autism at around 24 months of age. The course will also include a practical session in which participants will engage with clinical case studies, applying the teleNIDA tool to real-life cases. | https://forms.gle/uUJLjdtTFoDwCrkR9 |
| Zang | Lei | Hierarchical Bayesian computational modeling for psychological science | Computational modelling provides an insightful quantitative framework that allows researchers to precisely inspect internal cognitive variables and to understand hidden mechanisms. However, many researchers, especially early career researchers (ECR), often find this approach too technical and have difficulties adopting it for their own purpose. This three-part workshop is designed to bridge this gap. In the first part, I will quickly recap the basic principles of Bayesian modelling, and introduce a relatively new statistical language, Stan, that implements (hierarchical) Bayesian model estimation using Markov chain Monte Carlo (MCMC). In the second part, I will introduce the simple reinforcement learning (RL) model, one of the most commonly used models in the field of learning and decision-making, as one example, and how it can be executed within the hBayesDM package (Ahn, Haines, Zhang, 2017). In the third part, I will discuss how to write user-defined models, taking the advantage of Stan's flexibility, with the combined RL drift-diffusion model (RLDDM) as one example. Last, I will discuss in-depth topics of constructing hierarchical Bayesian models accommodating various research designs, from group comparisons, repeated measurement (e.g., within-subject, longitudinal), to full factorial design. Students are encouraged to bring their own model for bespoke modelling conversations. | https://forms.gle/cm2q8TQJAHKXNqiv7 |
| Brown | Kevin | Modeling and Applied Data Analysis for Psychologists and Cognitive Scientists (credit for 2 Field Labs) | The course will cover the following topics: the Python programming language and good programming practices; building models from observational data using the Maximum Likelihood method; the Bayesian approach to statistics, including data fitting and hypothesis testing; dimensionality reduction methods, such as PCA and t-SNE, for the visualization and exploration of high-dimensional data; data clustering methods for discovering patterns in observational datasets; and modeling with neural networks, both “classical” (association networks, interactive activation) and “modern” (convolutional models, gated recurrent networks, and transformer models). | https://forms.gle/2gJeUeC5QDuDo18Z6 |
| Amore | Mario | Progressi in Psichiatria (valido come 2 Field Lab, insegnato in lingua italiana) | l corso affronterà le profonde trasformazioni in atto nel campo della salute mentale, dalle più re- centi acquisizioni in ambito neuroscientifico, ai cambiamenti di paradigma sociale che influenza- no profondamente le relazioni umane e, insieme a queste, l’espressione della sofferenza mentale. Il percorso formativo aspira a promuovere uno sguardo unitario che muove dalle più recenti ac- quisizioni in ambito biochimico, immunologico, di neuroimaging, psicofarmacologico e giunge fino all’incontro con il paziente, così da orientare la relazione terapeutica alla comprensione reci- proca, alla comunicazione efficace e a un eleva- to standard etico. | https://forms.gle/BdHqx7P7dR1ayPvSA |
| Bigand | Emmanuel | From Sound to Sens: why Music is so Important for the Human Brain (credit for 2 Field Labs) | Why is music so important for the brain? The course aims to illustrate the psychobiological mechanisms underlying musical cognition, exploring the relationship between music and different cognitive systems. It will examine how music is able to stimulate three main categories of processes: those related to cognition, emotions, and actions. Through a journey that begins in the inner ear and reaches the areas of the cerebral cortex, the course will address the interaction among these processes, showing how a musical sound can become a meaningful stimulus with strong psychological power for human beings. | https://forms.gle/QAjgE2BXgsTpntZ18 |