Introduction

In the Brain and Cognition Lab, our focus lies in unraveling the physiological mechanisms that underlie cognition. We use various methods including intracranial recordings, EEG, eye tracking and psychophysics to study cognition in naturalistic settings. Through the utilization of cutting-edge tools such as machine learning and advanced signal processing, we delve into the intricate processes of active exploration, perception, and attention.

Active vision

In a natural environment, sensing is primarily an active process. The brain actively acquires most sensory inputs through motor sampling routines. In the context of visual sensing, we actively engage in saccadic eye movements and head/body rotations to explore the environment and identify objects of interest. At the Brain and Cognition Lab, we investigate the mechanisms underlying the impact of movements on perception and other cognitive processes.

Mind wandering

Whether we are driving a car or listening to a lecture, our attention fluctuates between periods of heightened and diminished focus. During these moments of reduced attention, our thoughts often drift away from the present, wandering into the past or the future. At the Brain and Cognition Lab, we investigate the neural mechanisms underlying these attentional states, explore their dynamics, transitions, the factors that trigger these shifts, and the potential functions they serve in organisms.

Attention and working memory

Attention and memory are interconnected: what we pay attention to shapes our memory, and in turn, our memories influence our attention. At the Brain and Cognition Lab, we delve into the mechanisms through which attention impacts memory. We explore the physiological mechanisms of attention and memory.

Methods

At the Brain and Cognition Lab, we employ electrophysiology recordings spanning various levels of neural organization, from micro-scale (cortical column, multi-unit activity) to meso-scale (intracranial EEG) and macroscopic (scalp EEG) signals combined with advanced signal processing techniques. These recordings are complemented by behavioral measures such as eye tracking. We adopt diverse methodologies to extract valuable insights from these combined recordings. For instance, we harness machine learning to uncover how the brain represents and processes information, functional connectivity analysis to explore large-scale neural dynamics.

Principal Investigator: Dr. Marcin Leszczyński