ACTA GEOGRAPHICA SINICA Vol. 30, 1964

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ACTA GEOGRAPHICA SINICA Vol. 30, 1964

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Executive Committee Meeting,Washington, D.C. January 28–29, 1972

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.