CONTEXTUAL QUICK-LEARNING AND GENERALIZATION BY HUMANS AND MACHINES

In a previous study (1994 Network: Comput. Neural Syst: 5 203-27) we c ompared human quick-learning and generalization (quick modelling) with that of neural nets (feedforward architectures), symbolic algorithms (decision tree procedures), an pattern classifiers (truth-set descript ors). Those studies raised the question of the role of context in the nature and rapidity of human learning. Here we address that issue in t he setting of the same basic experiment (Quinlan classification proble m) used for the previous studies. A major implication of our findings is that humans overwhelmingly seek, create, or imagine context in orde r to provide meaning when presented with abstract or apparently incomp lete or contradictory or otherwise untenable situations.