Over the course of my academic journey, I have developed a versatile set of skills that allow me to bridge theory, data, and application in the study of social cognition and behaviour. Below is an overview of the methods, tools, and techniques I use in my research.
EXPERIMENTAL DESIGN & DATA COLLECTION
Behavioural Experiments: Extensive experience designing and implementing interactive tasks, including multiplayer coordination games and human–computer interaction setups, with precise stimulus control and behavioural logging.
Eye-Tracking: Setup, calibration, and analysis of gaze and pupil dilations patterns for studying attention and social inference.
EEG: Acquisition and preprocessing of EEG data, with expertise in model-based EEG analysis, ERP extraction, and time-frequency methods.
fMRI: Conducted fMRI experiments and applied connectivity and pattern analysis to investigate motor control and cognitive networks.
COMPUTATIONAL MODELLING
Theory of Mind (ToM) Modelling: Developed recursive agent-based models simulating mental state inference in novel communication tasks.
Bayesian Modelling: Applied Bayesian Mixed Linear Non-Gaussian Acyclic Models (BMLiNGAM) to fMRI data to assess effective connectivity.
Reinforcement Learning & Utility-based Models: Familiar with decision modelling in social and non-social contexts.
Linear Mixed-Effects Models: Used for complex behavioural and eye-tracking data analysis.
Model Validation & Simulation: Strong experience in validating models against empirical data, parameter fitting, and simulating agent behaviour under different cognitive assumptions.
DATA ANALYSIS & STATISTICS
Statistical Inference: Hypothesis testing, regression modelling, ANOVAs, and generalized linear models.
Multivariate Pattern Analysis (MVPA): Applied to both EEG and fMRI data.
Meta-analysis: Contributed to published meta-analyses on sense of agency and sensory attenuation.
Open Science Practices: Pre-registration, code sharing, reproducibility standards (GitHub, OSF).
PROGRAMMING & SOFTWARE
MATLAB: Advanced level, stimulus programming (e.g., Psychtoolbox), data analysis, model implementation.
Python: Advanced, data processing, modelling, simulation, and visualization (NumPy, SciPy, pandas, matplotlib).
R: Intermediate, particularly for statistical analysis and data visualization (ggplot2, lme4).
CREATIVE & TECHNICAL DESIGN
Adobe Illustrator & Adobe XD: For figure design, scientific schematics, and layouting.
Adobe Lightroom: For analogue and digital photography editing.
Graphic Design & Communication: Used in outreach, academic posters, and science visualizations.
LANGUAGES
Georgian: Native
English: Advanced (academic and professional)
German: Intermediate (B1.1)
Spanish: Beginner