Viewpoint-dependent mechanisms in visual object recognition: Reply to Tarr and Bülthoff (1995).

I. Biederman and P. C. Gerhardstein (1993) demonstrated that a representation specifying a distinctive arrangement of viewpoint-inv ariant parts (a geon structural description, GSD) dramatically reduced the costs of rotation in depth. M. J. Tarr and H. H. Bulthoff (1995) attempt to make a case for viewpoint-dependent mechanisms, such as mental rotation. Their suggestion that GSDs enjoy no special status in reducing the effects of depth rotation is contradicted by a wealth of direct experimental evidence as well as an inadvertent experiment that found no evidence for the spontaneous employment of mental rotation. Their complaint against geon theory's account of entry-level classification rests on a mistaken and unwarranted attribution that geon theory assumes a one-to-one correspondence between GSDs and entry-level names. GSDs provide a representation that distinguishes most entry- and subordinate-level classes and explains why complex objects are described as an arrangement of viewpoint-invariant parts. Consider the nonsense object in Figure 1. When first viewed, how did the reader know that the object was one never encountered previously? Why was the reader fairly confident that he or she would know what the object would look like if rotated 30°? The large central block would still look like a block and the vertical cylinder and wedge on top of the block would still be on top of the block. The zigzag cross brace connecting the tilting cylinder (ending in a cone) to the wedge would still enjoy the same relation if rotated 30°. These words denoting parts and relations are easily matched to the corresponding regions of the image. Geon theory (Biederman, 1987; Hummel & Biederman, 1992) seeks to account for these readily evident capacities and characteristics of human object recognition by positing that objects are represented as an arrangement of simple viewpoint-invariant parts (geons) and relations, termed a geon structural description (GSD). The resultant viewpointinvariant representation is designed to account for many of the entry-level shape-based classifications, such as distinguishing between a chair, an elephant, and a frying pan. The theory also provides an account of the vast majority of