This effectively biases categorization decisions towards exemplars most similar to the to be categorized entity. The flexibility of the SUSTAIN model is realized through its ability to employ both supervised and unsupervised learning at the cluster level. Therefore, subsequent exposures to the stimulus would be assigned to the correct cluster. SUSTAIN also exhibits flexibility in how it solves both simple and complex categorization problems. Outright, the internal representation of SUSTAIN contains only a single cluster, thus biasing the model towards simple solutions.
Computational models of categorization have been developed to test theories about how humans represent and use category information. To accomplish this, categorization models can be fit to experimental data to see how well the predictions afforded by the model line up with human performance. Based on the model’s success at explaining the data, theorists are able to draw conclusions about the accuracy of their theories and their theory’s … Read More