The decomposition by the human visual system of visual scenes into a range of spatial frequencies is necessary for the categorization of the objects present in the visual scene. This decomposition of spatial frequencies may be particularly important for the processing of emotions. Experiments in the field of behavioral (Schyns & Oliva, 1999) and cognitive neuroscience (Vuilleumier, Armony, Driver, & Dolan, 2003) suggest that low spatial frequencies (LSF) are better than high spatial frequencies (HSF) for the categorization of emotional facial expressions (EFE). The aim of this study was to determine whether LSF information is more useful than HSF information for the categorization of emotions. We tested this hypothesis using artificial neural networks (ANN) subject to both unsupervised and supervised learning. The results indicated better emotion categorization with LSF information, thus suggesting that the HSF signal, which is also present in the BSF signal, acts as a source of noisy information during classification tasks in artificial neural systems.
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