Abstract
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.
Highlights
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli
Electrophysiological evidence of statistical learning has been often assessed by identifying signal differences for standard and unexpected stimuli embedded in a sequence[7], or by looking for fluctuations in the measured signal associated with different surprise levels[8,9,10]
By employing the statistical procedure proposed in Duarte et al.[19], we show that context trees generating sequence of auditory stimuli can effectively be extracted from EEG data
Summary
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. A crucial point here is that the length of the relevant sequence of past choices of auditory stimuli governing the transition to the one does not need to be fixed: it can very well depend on the sequence of past symbols itself. This framework allows to address von Helmholtz’s classical conjecture as follows. This approach is based on a genial intuition presented in R issanen[20] He noted that in most sequences of data observed in the real world, each new data unit is chosen in a probabilistic way. To completely characterize the way the sequence of units is generated, a transition probability governing the choice of the unit is associated with each context
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