The same thing happens in vision, not just in humans but in animals' visual systems generally. Brains are made up of neurons which "fire" by emitting electrical signals to other neurons after being sufficiently "activated". These neurons are malleable in terms of how much a signal from other neurons will add to the activation level of the neuron (vaguely speaking, the weights connecting neurons to each other end up being trained to make the neural connections more useful, just like the parameters in a linear regression can be trained to improve the mapping from input to output). Our biological networks are arranged in a hierarchical manner, so that certain neurons end up detecting not extremely specific features of the world around us, but rather more abstract features, i.e. patterns or groupings of more low-level features. For example, the fusiform face area in the human visual system is specialized for facial recognition.