keywords: Hidden Markov Model, window size / receptive field, bayesian statistics, information theory Abstract: Consider an input which has the structure of a Hidden Markov Model (HMM). The values of each of the hidden variables probabilistically depends only on the values of the neighboring ones, and these are accessible only through noisy channels. In the case of a finite HMM, using belief propagations algorithms, we calculated analytically in simple cases the probability of correct detection of a given hidden variable as a function of the size of the HMM. Using information theory, one thus defines a window size / receptive field of the HMM, which corresponds to the set of observations which are relevant to the detection of the given hidden variable.