Psychophysics and Neural Dynamics lab

Our focus

At the PND Lab, we investigate human perception and cognition through an integrative approach that combines psychophysics, computational modeling, and advanced imaging techniques, including EEG, source imaging, and brain network analysis. Our research aims to understand the unique aspects of each person’s perceptual and cognitive abilities and how they vary over time. To achieve this, we focus on how individuals use contextual information in perceptual decision-making and the role of neural activity fluctuations that characterize each individual brain. We study core areas such as visual perception, attention, memory, and decision-making, in both healthy and clinical populations.

Publications

Scholar

Code/Software

• DynetToolbox (Matlab version of the Self-Tuning Optimized Kalman filter)
• PyDyNet (Python version)

Team

Head of the lab

Postdoctoral Researcher

Ph.D. Student

Datasets

• Laws of concatenated perception: Vision goes for novelty, Decisions for perseverance
• The effects of visual distractors on serial dependence
• Datasets for "Serial dependence does not originate from low-level visual processing" Gizay et al., (2021). Cognition
• Datasets for "The anisotropic field of ensemble coding"
• Datasets and example code for "Serial dependence and representational momentum in single-trial perceptual decisions"
• Dataset for "Feature distribution learning by passive exposure"
• Datasets and example code for "Tuning perception and decisions to temporal context"
• Intact Serial Dependence in Schizophrenia: Evidence from an Orientation Adjustment Task
• Data for: Serial dependence: a matter of memory load
• Data for "The functional role of spatial anisotropies in ensemble perception"
• Unlocking crowding by ensemble statistics
• Spectral tuning and after-effects in neural entrainment
• Alpha peak frequency affects visual performance beyond temporal resolution
• Pre-stimulus alpha activity modulates long-lasting unconscious feature integration