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Generalization                                                        [back]
Learning and generalization go hand-in-hand. All fields in psychology dealing with learning and inference have also explored generalization. We study both dimensional generalization, or how learning about a stimulus is transferred to new stimuli that differ from the original along continuous dimensions, and compound generalization, or how learning about one stimulus is transferred to new compounds comprising that stimulus.

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In the past, these two forms of generalization have been studied largely independently, and researchers have shown little interest in developing a unified theoretical framework to understand both. We have recently developed just such a unified framework, by extending Shepard’s rational theory of dimensional generalization to the explanation of compound generalization. The model explains many results from the literature on causal and associative learning. However, to do so, it assumes a particular hypothesis about what distinguishes separable from non-separable (integral) dimensions, which is different from the explanation originally favored by Shepard. We have recently tested these two hypotheses in causal learning experiments and found that the assumptions of our model are correct.