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Course unit
COGNITIVE, BEHAVIORAL AND SOCIAL DATA
SCP7079219, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
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 |
Hours of Individual study |
Shifts |
Lecture |
6.0 |
48 |
102.0 |
No turn |
Start of activities |
01/10/2018 |
End of activities |
18/01/2019 |
Examination board
Board |
From |
To |
Members of the board |
2 a.a 2018/2019 |
01/10/2018 |
30/09/2019 |
SARTORI
GIUSEPPE
(Presidente)
GAMBERINI
LUCIANO
(Membro Effettivo)
BEGLIOMINI
CHIARA
(Supplente)
BISIACCHI
PATRIZIA
(Supplente)
CASTIELLO
UMBERTO
(Supplente)
MONARO
MERYLIN
(Supplente)
SCARPAZZA
CRISTINA
(Supplente)
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Prerequisites:
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Notions of machine learning |
Target skills and knowledge:
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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:
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Written and oral exam |
Assessment criteria:
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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:
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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:
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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) |
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