This research area encompasses a diverse group of researchers from various fields such as psychology, neuroscience, computer science, engineering and education.
Cognitive technologies research aims to optimize human cognition through digital tools that can augment or externalize parts of human cognitive functions to make them more effective and efficient. The group's main focus is the design and evaluation of these tools to improve memory, decision-making, and problem-solving. The development of these technologies can help researchers gain insight into how human cognition works and refine cognitive training programs. Some examples of these tools include virtual reality simulations, cognitive modeling software, and computational cognitive architectures. The group is also interested in using cognitive technologies to enhance learning by providing students with immersive tools such as virtual and augmented reality technology that can simulate real-world scenarios and reinforce learning.
Moreover, the group is committed to investigating the influence of educational technology on learning experiences and outcomes. This research examines how different digital tools and platforms can enhance knowledge and skill acquisition, engagement, and retention in educational settings. Adaptive learning systems, intelligent tutoring software, and game-based learning are examples of these technologies, as are collaborative virtual learning environments, learning analytics, and personalized feedback mechanisms. The group aims to contribute to the advancement of innovative instructional methods that promote deep learning and adapt to the unique needs and preferences of individual learners. This will be achieved through the exploration of the intersection between cognitive technology and educational applications.
Research on computational methods emphasizes the development and application of mathematical and statistical tools to analyze complex data. The group uses a variety of computational methods such as network analysis, data-driven approaches and machine learning, structural equation modeling, and time series analysis, with a focus on physiological and neuroimaging data. Modern computational methods are of great importance for psychology and neuroscience research, enabling researchers to analyze vast and intricate datasets that are difficult to interpret using traditional statistical techniques. By utilizing machine learning algorithms and network analysis, computational methods can reveal hidden patterns and relationships between variables. Additionally, these methods allow researchers to model and simulate cognitive processes, ultimately leading to the development of interventions that improve cognitive functioning.