First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP7079219, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination COGNITIVE, BEHAVIORAL AND SOCIAL DATA
Website of the academic structure
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge GIUSEPPE SARTORI M-PSI/02
Other lecturers MERYLIN MONARO M-PSI/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses M-PSI/06 Psychology of Work and Organisations 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Board From To Members of the board
3 a.a 2019/2020 01/10/2019 28/02/2020 SARTORI GIUSEPPE (Presidente)
MONARO MERYLIN (Membro Effettivo)

Prerequisites: Notions of machine learning
Target skills and knowledge: At the end of the course, students will be able to understand complex issues in cognitive, social and behavioural sciences, choose the appropriate methodology and instruments to extract information from cognitive, behavioural and social data and integrate data science knowledge to social, brain, mind and behavioural aspects. They will acquire:
- Basic concepts of cognitive psychology, social psychology and behavioural science.
- Instruments and methodologies for cognitive, behavioural and social data analysis.
- Practical skills of data analysis applied to cognitive, social and behavioural problems.
Examination methods: Oral exam and project
Assessment criteria: It will be evaluated the knowledge of the arguments proposed during classes, the acquisition of concepts and methodologies proposed and the ability to apply them.
Course unit contents: The aim of the course is to provide an overview of concrete data science applications in behavioural science, cognitive science, neuroscience and social science. The course gives an underground of methods to analyse and learn behavioural, cognitive and brain functional/structural data. It provide a review of studies, with several examples of recent practical applications, also according with the students interests. Limits in the state of the art and future directions will be discussed. The course contents are the following:

• Basic concepts of human brain cognitive functioning (attention, memory, learning, language, etc.) and how to measure it

• Basic concepts of social psychology and social behaviour (preferences, judgments, group identity, etc.) and how to measure it

• What are behavioural measures and how to measure them (e.g., RT); implicit and explicit behavioural measures (e.g., the IAT)

• Extracting and predicting information from behaviour (e.g. lie detection, predicting malicious behaviour from social networks activity, fake online reviews, security applications, etc.)

• What are psychophysiological measures and how to measure them (e.g., HR variability, SCR, facial expressions, EEG, fRMI, etc.)

• Extracting and predicting information from psychophysiological measures

• Extracting and predicting information from brain activity: mind reading applications (e.g., psychopathology detection, reconstructing visual experiences from brain activity, brain computer interface devices, etc.)

• Social and behavioural data for marketing application (e.g. skill assessment and prediction, psychology of taxes, predicting preferences and personality from social networks activity, sentiment analysis, etc.)

• Issue related to the application of machine learning in behavioural research (e.g. the problem of reproducibility)
Planned learning activities and teaching methods: The lecturer will introduce each topic discussing the relevant issues and the most interesting and recent experimental evidences and applications
Textbooks (and optional supplementary readings)