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Abstract:
Previous research suggested that the shape variability of objects from the same basic level category can be conceptualized by transformations which continuously change object shape (topological transformations). Experiments with line drawings (2D outline shapes) demonstrated that categorization latencies and error rates increase with increasing amount of shape transformation (Graf, 2001). We investigated whether these results generalize to more realistic gray-level images rendered from 3D object models. We also studied the effects of combined shape transformations and image-plane rotations on categorization performance.
New category members were produced by morphing between objects from the same basic level category. Subjects were required to decide whether two sequentially presented objects belonged to the same basic level category or not. In Experiment 1 the amount of shape transformation was varied, while in Experiment 2 topological distance and image-plane orientation were manipulated.
Categorization performance (latencies and accuracy) deteriorated systematically with increased shape transformation, both for upright (Exp. 1) and for rotated (Exp. 2) objects. Furthermore, Exp. 2 showed that categorization latencies increased with increasing amount of orientation change. There was no interaction between shape transformation and object orientation.
The results confirm that categorization performance is systematically related to the amount of shape transformation, both for line drawings and gray-level images, as well as for upright and plane rotated objects. In addition, orientation dependency was corroborated with a basic level categorization task. Finally, categorization processes which compensate for shape changes and plane rotations seem to be independent, confirming previous evidence of independent effects for other combinations of spatial transformations (e.g. Lawson et al., 2000). The results support an image-based model of basic level categorization.
