 This division-of-labor is extremely efficient at overcoming nuisance variation, compared to shallow Golden Age networks. Furthermore, limiting the inputs of the filter nodes to a small window significantly lowers the number of weights that must be learned at each level, compared to a fully-connected network. If features usually depend only on local relations (i.e. in the sense that one normally does not need to look at someone’s feet to read their facial expression), then this gain comes at no cost to classification accuracy. Furthermore, pooling the outputs of several different filter nodes helps detect the same feature across small differences in nuisance variables like pose or location. There is special enthusiasm for this kind of neurocomputational division-of-labor in cognitive science, because it was originally inspired by anatomical studies of mammalian neocortex (Hubel &amp; Wiesel 1965; Fukushima 1980). Other sources of empirical evidence have demonstrated the potential of such networks as models for perceptual similarity and object recognition judgments in primates (Khaligh-Razavi &amp; Kriegeskorte 2014; Hong et al. 2016; Kubilius, Bracci, &amp; Beeck 2016; Lake, Zaremba et al. 2015; Yamins &amp; DiCarlo 2016; and Guest &amp; Love 2019 [Other Internet Resources, hereafter OIR]). These points also interface with the innateness controversy discussed in Section 6. For example, Buckner (2018) has recently argued that these activation functions combine to implement a form of cognitive abstraction which addresses problems facing traditional empiricist philosophy of mind, concerning the way that minds can efficiently discover abstract categorical knowledge in specific, idiosyncratic perceptions.