Retrieving the structure of probabilistic sequences of auditory stimuli from electroencephalographic (EEG) signals
De
Claudia Vargas, Guilherme Ost
It has long been conjectured that the brain learns statistical regularities from sequences of stimuli. The ability to extract and memorize these regularities over time plays a crucial role in perception, motor control and decision-making. Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and EEG signals acquired while participants are exposed to those sequences of stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, also called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. If the brain assigns probabilistic models to samples of stimuli then the context tree generating the sequence of stimuli should be encoded in the brain activity. In this talk we will present and discuss an innovative procedure allowing us to retrieve these context trees from EEG signals.
