My research investigates how statistical learning (SL)—the brain’s ability to extract regularities from sensory and emotional environments—acts as a unifying mechanism underlying cognitive and emotional adaptation across the lifespan.
In older adults, I examine how SL contributes to cognitive maintenance, emotional well-being, and neuroplasticity, particularly in contexts of cognitive stimulation, MCI, and dementia.
In children and youth, I explore how SL supports the development of attention, executive function, and emotion regulation, and how brain–computer interfaces (BCIs) or neurofeedback may enhance these processes.
Together, these studies aim to build a mechanistic bridge between implicit learning, neural adaptation, and emotional functioning, explaining how regularity detection contributes to resilience, well-being, and lifelong cognitive health.
This research combines experimental psychology with neurophysiological and affective neuroscience tools—including EEG/ERP and eye-tracking paradigms, BCI-based neurofeedback, and network analysis—to examine how the brain encodes, predicts, and adapts to patterns in cognitive and emotional domains.
By applying comparable paradigms across age groups, this work aims to identify shared and age-specific neural mechanisms of statistical learning and to design interventions that promote adaptive cognition and emotion throughout life.