The main research directions of the Cognitive Modeling Laboratory focus on creating complex models and simulations that allow for the reproduction and analysis of multi-level cognitive processes. The laboratory leverages advanced tools in artificial intelligence, natural language processing, and virtual reality technology, ensuring an integrated approach to studying cognitive and behavioral phenomena across various contexts.
- Modeling of Mental Processes, Individual, and Group Behavior
In this area, the laboratory utilizes AI technologies, machine learning, and specialized software to simulate fundamental cognitive processes, such as perception, attention, memory, and planning, as well as social interactions. Research within this scope focuses on developing computational models that describe how individual cognitive processes are formed and adapted in social contexts. In particular, the lab studies how cognitive processes such as decision-making, motivation, and emotional regulation are influenced by collective interactions, where cooperation and competition must be balanced.
Using artificial intelligence and multi-agent modeling, the laboratory evaluates the impact of individual psychological traits, such as resilience or emotional stability, on behavioral patterns and goal achievement at both individual and group levels. The lab develops simulations using virtual agents that recreate these processes, enabling the study of social adaptation mechanisms, stress resilience, and predictive insights into factors influencing experiences of happiness and satisfaction within group interactions.
- Natural Language Processing and Cognitive Linguistics
This research direction aims to create neural network models for understanding and generating human language. Using natural language processing (NLP) methods and cognitive linguistic principles, the laboratory seeks to develop systems capable of semantic text analysis, dialogue simulation, and the detection of linguistic patterns that reflect cognitive processes. Deep learning-based models are employed to analyze communicative acts, explore pragmatic aspects of language, and model language comprehension processes in social contexts. These models enable the development of programs for monitoring social processes, detecting changes in societal moods and trends, and enhancing human-machine interaction systems, bringing them closer to natural dialogue.
The application of **eye-tracking** technology in this area enables a deeper understanding of visual perception processes related to text and non-verbal communication. Eye movement analysis during text reading or image viewing allows for evaluating which linguistic elements capture the most attention, how information is processed in real-time, and how the structure of textual or graphical content impacts comprehension. Such data can help optimize dialogue systems to make them more intuitive for users and improve language learning methods aimed at enhancing reading comprehension and understanding.
- Virtual Reality for Cognitive Modeling
The use of virtual and augmented reality (VR/AR) provides unique opportunities for exploring the impact of psychological factors on decision-making, memory mechanisms (including traumatic memory), social behavior, and mental disorders such as depression, PTSD, and anxiety disorders. The laboratory employs VR/AR to develop diagnostic and therapeutic methods, facilitating the creation of individualized treatment strategies for mental health disorders based on patients’ behavioral patterns and emotional responses within virtual environments.
Laboratory Equipment
– Electroencephalography (EEG) – a system for measuring and analyzing electrical brain activity during cognitive tasks. With high temporal resolution, EEG enables the study of the timing and sequence of neural network activation, which is critical for understanding transient cognitive processes.
– Virtual Simulation Environments – platforms for creating interactive, high-realism scenarios for modeling behavioral experiments. These environments provide precise control over conditions, allowing researchers to examine participants’ responses to a wide range of stimuli in simulated scenarios.
– Eye-Tracking Systems – Technologies for monitoring and analyzing eye movements during tasks requiring focused attention and decision-making. The system tracks fixations, saccades, and other indicators of cognitive processes, such as attention allocation, visual information processing, and choice selection.
These research directions and equipment collectively support the laboratory’s goal of comprehensively investigating cognitive processes, offering reliable and innovative approaches to deepen our understanding of brain and mental function.