COGNITIVE, BEHAVIORAL AND SOCIAL DATA

Second cycle degree in DATA SCIENCE

Campus: PADOVA

Language: English

Teaching period: First Semester

Lecturer: GIUSEPPE SARTORI

Number of ECTS credits allocated: 6


Syllabus
Prerequisites: Notions of machine learning
Examination methods: Written and oral exam
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)