Proceedings of the Annual Meeting of the Cognitive Science Society

Fractions are a critical building block for the development ofhuman mathematical cognition, but the origins of this conceptare not well-understood. Recent work has found that a wholenumber sense is present in deep convolutional neural networks(DCNNs) pre-trained for object recognition and uses them asa model for investigating human numerical cognition. Do DC-NNs also have a fraction sense? If so, is it dependent or in-dependent of whole number processing? We investigated theneural sensitivity of a pretrained DCNN to both whole num-bers and fractions. We replicated and extended previous re-search that the sense of whole number emerges in a differentDCNN architecture. Further, we showed that DCNN is alsosensitive to fraction value, i.e., the ratio of numerosities. Test-ing this model, our results suggest that the fraction sense relieson the whole number sense.