Training and Research

PhD Programme Courses/classes - 2024/2025

This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!

Instructions for teachers: lesson management

To be objective in Psychology

Credits: 41

Language: English

Teacher:  Margherita Pasini; Giorgio Erle, Riccardo Sartori, Roberto Burro, Margherita Pasini, Tommaso Tuppini, Giorgio Erle, Massimiliano Badino, Alessandro Stavru, Markus Georg Ophaelders, Markus Georg Ophaelders, Marcella Milana, Alessandro Stavru, Lorenzo Bernini

Advanced Research and Methods about Primary Students‘ and Teacher Students’ Dealing with Emotions

Credits: 0,5

Language: English

Teacher:  Daniela Raccanello

Writing

Credits: 1

Language: Italian

Teacher:  Marcella Milana, Elena Trifiletti, Carlo Chiurco, Elena Florit, Marinella Majorano, Daniela Raccanello

After the PhD

Credits: 1

Language: Italian

Teacher:  Massimo Prearo

Literature reviews

Credits: 4

Language: Italian

Teacher:  Paola Dusi, Michele Scandola

Physical measures and psychological measures

Credits: 1

Language: Italian

Teacher:  Luigi Tronca, Agostino Portera, Roberta Silva, Roberto Burro, Sandro Stanzani, Elena Trifiletti

Padronanza delle metodologie di ricerca in psicologia: un seminario di sintesi

Credits: 0,5

Language: English

Teacher:  Margherita Pasini

Rethinking Educational Programs for Communities through the Lens of Social Sustainability

Credits: 0,5

Language: English

Teacher:  Prof. Matt Sparke, University of California Santa Cruz.

Action research: theoretical framework and practical examples

Credits: 1

Language: English

Teacher:  Anna Maria Meneghini

Being a researcher

Credits: 1

Language: Italian

PhD school courses/classes - 2024/2025

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

1. PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
More information regarding CFUs is found in the Handbook for PhD Students: https://www.univr.it/phd-vademecum

2. Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, instructions will be sent well in advance. No confirmation e-mail will be sent after signing up. Please do not enquiry: if you entered the requested information, then registration was silently successful.

3. When Zoom links are not explicitly indicated, courses are delivered in presence only.

4. All information we have is published here. Please do not enquiry for missing information or Zoom links: if the information you need is not there, then it means that we don't have it yet. As soon as we get new information, we will promptly publish it on this page.

Summary of training activities

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

COMPUTATIONAL MECHANISMS UNDERLYING SENSORIMOTOR LEARNING  (2024/2025)

Teacher

Matteo Bertucco

Referent

Matteo Bertucco

Credits

4.5

Language

English

Class attendance

Free Choice

Location

VERONA

Seminars 0

Learning objectives

Motor learning has traditionally focused on how predictive or feedforward control is updated by past errors and has generally assumed that adaptation is driven by a single process, that variability is undesirable, and that learning, and control are shaped by a trade-off between speed and accuracy. The course will review recent research that examined how learning shapes both predictive and reactive control mechanisms and has shown that adaptation involves multiple, interacting processes, that the motor system tunes variability to the learning task, and that accuracy is not necessarily gained at the expense of speed. Course contents: - Skills acquisition in sensorimotor learning - Variability in sensorimotor learning - Reward in sensorimotor learning - Bayesian processing in sensorimotor learning

Prerequisites and basic notions

Bases of muscle neurophysiology and motor control

Program

Motor learning has traditionally focused on how predictive or feedforward control is updated by past errors and has generally assumed that adaptation is driven by a single process, that variability is undesirable, and that learning, and control are shaped by a trade-off between speed and accuracy. The course will review recent research that examined how learning shapes both predictive and reactive control mechanisms and has shown that adaptation involves multiple, interacting processes, that the motor system tunes variability to the learning task, and that accuracy is not necessarily gained at the expense of speed.
Course contents:
- Skills acquisition in sensorimotor learning
- Variability in sensorimotor learning
- Reward in sensorimotor learning
- Bayesian processing in sensorimotor learning

Bibliography

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

The teacher will use:
a) Lecture-style instruction
b) Group style activity through the discussion of scientific articles related to the topics covered during lectures.

Learning assessment procedures

The full attendance (100%) at the course will be required to obtain the CFU.
There will not be a final assessment for the course.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

The full attendance (100%) at the course will be required to obtain the CFU.
There will not be a final assessment for the course.

Criteria for the composition of the final grade

There will not be a final assessment for the course.

PhD students

PhD students present in the:
BartoliniAnnachiara

Bartolini Annachiara

BonviciniGiulia

Bonvicini Giulia

FioranzatoVeronica

Fioranzato Veronica

GrassoGiuseppe

Grasso Giuseppe

IernaAndrea

Ierna Andrea

IeroncicEnrico

Ieroncic Enrico

foto, October 25, 2024

Muslumogullari Yunus Emre

symbol email emre.muslumogullari@univr.it
VenturiniElisa

Venturini Elisa

symbol email elisa.venturini_02@univr.it
Course lessons
PhD Schools lessons

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Guidelines for PhD students

Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.

Documents

Title Info File
File octet-stream Annual credit sheet octet-stream, it, 20 KB, 05/04/24
File pdf Basic expected outcomes pdf, en, 131 KB, 05/04/24
File pdf Competenze attese pdf, it, 129 KB, 05/04/24
File pdf Prodotti minimi attesi pdf, it, 126 KB, 05/04/24
File pdf Specific learning outcomes pdf, en, 133 KB, 05/04/24