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Relational thinking plays a central role in human cognition. However, it is not known how children and adults acquire relational concepts and come to represent them in a form that is useful for the purposes of relational thinking (i.e., as structures that can be dynamically bound to arguments). The authors present a theory of how a psychologically and neurally plausible cognitive architecture can discover relational concepts from examples and represent them as explicit structures (predicates) that can take arguments (i.e., predicate them). The theory is instantiated as a computer program called DORA (Discovery Of Relations by Analogy). DORA is used to simulate the discovery of novel properties and relations, as well as a body of empirical phenomena from the domain of relational learning and the development of relational representations in children and adults.

A striking aspect of the human capacity for object recognition is that it is largely unaffected by where the object's image falls on the retina, the size of the image, or the orientation in depth from which the object is viewed. A fundamental challenge for theories of human object recognition is to explain how to achieve this invariance with viewpoint. In this article, the author reviews four general approaches to this problem in the computational modeling literature and discuss evidence bearing on each as an account of human object recognition. Some of the earliest computer models of object recognition were based on structural descriptions in 3-D object-centered reference frames. According to most early structural description theories, object recognition is accomplished by using the 2-D image of an object to derive a 3-D structural description, which is then matched against similar descriptions stored in memory. Some view-matching models exploit specific transformations, such as translation, scaling, and rotation, to bring novel images into correspondence with a small number of stored views.