[PS-2.2] Neural oscillations as a brain signature of statistical learning?

Bogaerts, L. ¹ , Landau, A. N. ¹ , Richter, C. G. ² & Frost, R. ^{1, 2, 3}

1 The Hebrew University of Jerusalem
2 The Basque Center on Cognition, Brain and Language
3 Haskins Laboratories

Since the seminal demonstration of Saffran and colleagues (1996) that infants display a remarkable sensitivity to the statistical properties of sensory input, extensive research has focused on the ability to discover regularities in the environment. Recent neuroimaging studies have associated Statistical learning (SL) with domain-general regions responsible for binding temporal and spatial contingencies in different modalities (hippocampus, medial temporal lobe), as well as with domain-specific visual and auditory cortical networks. Here we aim to go beyond the neurobiological ?where? of SL and use electroencephalography (EEG), which can reveal oscillatory activity. Neuronal oscillations reflect rhythmic fluctuations in the inhibition/excitation balance of neuronal populations and have been proposed to be instrumental to account for sensory processing, attentional selection and memory formation. We will present data from a classical visual SL task with triplets of complex shapes. The intriguing possibility we explore is that pre-stimulus neural oscillations in the Alpha-Beta range may provide a brain signature of the anticipation of the predictable stimuli in a sequence and hence of regularity learning. Such a spectral signature of learning holds the promise of offering an online learning measure and also of providing critical insights regarding the mechanisms of SL.