Proceedings of the Annual Meeting of the Cognitive Science Society

One of the classical problems faced by theories of mental imagery is the Indeterminacy Problem: a certain level of detail seems to be required to construct an image from a generating description, but such detail might not be available from abstract, categorical descriptions. If we commit to unjustified details and incorporate them into an image, subsequent queries of the image might indiscriminately report not only information implied by the description but also information that was arbitrarily fixed. The Indeterminacy Problem is studied in a simplified domain, and a computational model is proposed in which images can be incrementally adjusted to satisfy a set of inter-constraining assertions as well as possible. In this model, queries can discriminate between those details in an image which are necessary (implied by the generating description) and those which are incidental (consistent but arbitrarily fixed). The computational model exploits the graded prototypicality of the categorical relations in the simplified domain, and suggests the importance of a grounded language for reasoning with categories.