Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up each example, and such architectures have not been as successful as deep neural networks in capturing language usage and meaning. More work is needed bringing contemporary deep learning architectures used in machine intelligence to the effort to understand human language processing. [For Ben Ambridge's "Against Stored Abstractions: A Radical Exemplar Model of Language Acquisition," see EJ1269951.]