Proceedings of the Annual Meeting of the Cognitive Science Society

In a classic paper, Miller (1956) summarized findings showing that people can only identify a limited number of distinctstimuli at a time. One puzzling aspect of this capacity limitation is that it is approximately invariant to range. Thatis, the number of accurately identifiable stimuli is approximately the same regardless of how far apart the stimuli arespaced. Models of this phenomenon have suggested that people operate in a context-coding mode when performing thesetasks, effectively carrying out a form of contextual normalization, but why such normalization might take place is unclear.Here, we propose an explanation by appealing to a tradeoff with generalization. Specifically, we implement contextualnormalization in a recurrent neural network and show that this normalization enables stronger generalization in a relationalreasoning task, but also results in a perceptual capacity limitation which captures many of these classic phenomena.