To better understand the computational steps that the brain performs during reading, we used a convolutional neural network as a computational model of visual word recognition, the first stage of reading. In contrast to traditional models of reading, our model directly operates on the pixel values of an image containing text, and has a large vocabulary of 10k Finnish words. The same stimuli can thus be presented unmodified to both the model and human volunteers in a magnetoencephalography (MEG) scanner. In a direct comparison between model and brain activity, we show that the model accurately predicts the amplitude of three evoked MEG response components commonly observed during reading. We conclude that the deep learning techniques that revolutionized models of object recognition can also create models of reading that can be straightforwardly compared to neuroimaging data, which will greatly facilitate testing and refining theories on language processing in the brain.
In rheological terms foams are time independent yield stress fluids, displaying properties of both solid and liquid materials. Here we measure the propagation of a 2D dry foam in a...
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