Center for Data Analysis Techniques in Cognitive Science
The Center for Data Analysis Techniques in Cognitive Science (CEDAT) has the mission of advancing the knowledge of PhDs, post-docs, and professors of the Department of Psychology and Cognitive Sciences (DiPSCo) of the University of Trento regarding the main methods of analysis of data when applied to the different sectors and scientific domains of psychology and cognitive science.
The center identifies itself as an entity dialoguing with other University realities related to social, economic, statistical, and computational research, relative to the exchange and integration of data analysis methodologies.
Data analysis in psychology and cognitive science relies on a complex set of techniques for measuring, describing, and understanding a wide variety of psychological processes: from personality traits and dimensions to cognitive development, from psychobiological processes to learning mechanisms, from dynamics in working environments to social development, from psychodynamic components to clinical assessment. In all these contexts, data analysis assumes a fundamental and synergistic role in the validation of psychological theories and the realization of competitive and impactful research projects.
To discover more about CEDAT or inquire about its activities, feel free to reach us at our institutional email: cedat.dipsco @ unitn.it.
A center giving value to data analysis in psychology and cognitive science.
Nowadays, the amount of data available to researchers is steadily growing, mainly thanks to the development of new laboratory protocols and innovative digital technologies. On the one hand, new datasets can inspire the creation of cutting-edge models to understand and simulate cognitive mechanisms and processes (e.g. cognitive modeling, psychometric and measurement models). On the other hand, data constitute the fundamental elements for identifying intersections and points of contact between innovative multidisciplinary fields such as machine learning and generalized artificial intelligence. Furthermore, the advent of advanced data collection techniques opens new horizons for the investigation and validation of psychological theories, constructs, and models.
Topics of Interest
Elements of Mathematics and Statistics
Elements of statistics
Elements of linear algebra, operators and vectorial spaces
Matrix algebra
Maximum Likelihood and Robust methods (Bootstrap, Corrected versions of ML)
Simulation studies (Monte Carlo Sampling)
Markov Processeschain
Frequentist vs Bayesian estimation methods
Probability
Computational Modeling, Data-driven Modeling, and Experimental Methods
EEG
Experimental Planning
Elements of Python
Elements of MATLAB
Cognitive Aspects of Data Visualisation
Big Data Technologies: SQL (databases and query language), NoSQL (e.g. managing JSON; Mongo DB), High Performance Computing (HPC), Spark, Cloud Computing (AWS, Azure, GCP)
Data Mining (e.g. social media mining from Twitter or Reddit psych. boards)
Machine Learning (Shallow and Deep Learning)
Text Analysis and Natural Language ProcessingNLP
Innovative Programming LanguagesNew Languages (e.g., Julia, Wolfram Language, NetLogo)
Web Scraping
Time Series Analysis
Network science and complex systems modeling
Robust Statistics for modelling with uncertainty
Topics of Interest
Data Wrangling, Open Science, and Theory-driven Modeling
Elements for enhancing open science: Project structure, Good Practices in coding, Workflow analysis, Git(Hub), Docker, LaTeX, RMarkdown (Quarto).
Data wrangling with R (e.g., tidyverse ecosystem)
Data wrangling with Python (e.g. dataframes with pandas)
Missing data imputation (e.g. chained equations, random forests)
General linear models
Multilevel Modeling
Mediation and Moderation
Classical Test Theory, IRT, Reliability, Validity
Structural Equation Modeling (SEM)
Advanced SEM (e.g., Measurement Invariance, Longitudinal SEM, Latent State-Trait Theory, Mixture, Multilevel SEM, Bayesian SEM, Exploratory SEM, Dynamic SEM)
Meta-analysis with R (e.g. metafor)
Power analysis
Data visualization techniques (e.g. ggplot2, Matplotlib, Power BI, Tableau)
Easing R (e.g. Jamovi)
Human-centric, interpretable Artificial Intelligence (SHAP scores)
Network psychometrics and identification of dimensions in psychological construct
BERT and transformer networks for the Lexical Hypothesis
Cognitive network science metrics
Redundancy, synergy and feature selection (e.g. LASSO)
Executive Scientific Board
Coordinator
Prof. Luigi Lombardi
luigi.lombardi @ unitn.it
Co-coordinator
Prof. Andrea Bizzego
andrea.bizzego @ unitn.it
Co-coordinator
Prof. Enrico Perinelli
enrico.perinelli @ unitn.it
Co-coordinator
Prof. Massimo Stella
massimo.stella-1 @ unitn.it
Scientific Activities
Incoming Workshops:
To Be Announced Soon.
Past Workshops:
- 18 April 2024 - A kind and practical introduction to data science - Giancarlo Ruffo (Professore Associato, Università degli Studi del Piemonte Orientale).
Teaching Resources: Video lectures, code and slides kindly made available by the speaker (available only to UniTN users) [HERE].
- 7-8 March 2024 - Introduction to generalised linear models - Giulio Costantini (Professore Associato, Università degli Studi Milano Bicocca).
Teaching Resources: Video lectures (available only to UniTN users) [HERE] (7 March) and [HERE] (8 March).
- 17 Nov. 23 - An introduction to Item Response Theory models with R - Ottavia M. Epifania (Dipartimento di Filosofia, Sociologia, Pedagogia e Psicologia Applicata, Università di Padova).
Teaching Resources: GitHub Repository with Slides and code [HERE] - Video lecture (available only to UniTN users) [HERE]
- 18 Sept. 23 - Introduction to data frames and multivariate frameworks in Python in psychology and cognitive science - Giulio Rossetti (Primo Ricercatore, KDD Lab)
- 19 Sept. 23 - Introduction to data visualization in Python in psychology and cognitive science: Networks and data frames - Salvatore Citraro (Ricercatore, KDD Lab)
- 20 Sept. 23 - Introduction to Jupyter Notebooks in Python: How to interface R and Python for obtaining data visualizations and basic analyses of cognitive data? - Katherine Abramski (PhD candidate, UniPisa & CNR).
Teaching Resources: GitHub Repository with Jupyter Notebooks [HERE] - Video lectures (available only to UniTN users) [Sept. 18] [Sept. 19] [Sept. 20].
