When a wheel rolls along a flat surface, a point on the wheel's perimeter follows a cycloid trajectory. Subjects, however draw the curtate cycloid, characterized by bottom loops, rather than the cycloid to depict the path that a point on a static wheel's perimeter would trace if the wheel were rolling. This is the curtate cycloid illusion. In Experiment 1, we show that animating the wheel does not dispel the illusion and that subjects high in spatial ability are less susceptible to the illusion than are low-spatials. Experiments 2, 3a, and 3b supported the hypothesis that the illusion occurs when subjects reallocate cognitive resources from processing a rolling wheel's translation to computing its instant centers, the point about which the wheel is rotating at a given instant in time. This reallocation occurs only when a reference point on the wheel's perimeter contacts and leaves the surface. We conclude that the illusion does not reflect fundamental perceptual biases, but rather stems from transient shortages of cognitive resources during the higher-level processing of the wheel's translation and rotation.

People who understand mechanical systems can infer the principles of operation of an unfamiliar device from their knowledge of the device's components and their mechanical interactions. Individuals vary in their ability to make this tjrpe of inference. This paper describes studies of performance in psychometric tests of mechanical ability. Based on subjects' retrospective protocols and response patterns, it was possible to identify rules of mechanical reasoning which accounted for the performance of subjects who differ in mechanical ability. The rules are explicitely stated in a simulation model which demonstrates the sufficiency of the rules by producing the kinds of responses observed in the subjects. Three factors are proposed as the sources of individual differences in mechanical ability: <1| ability to correctly identify which attributes of a system are relevant to its mechanical function, (2) ability to use rules consistently, and (3| ability to quantitatively combine information about two or more relevant attributes.

There have been a number of production system models which have recently made substantial advances in modeling higher-level cognition. These type of model offers only comprehensive approaches to the modeling of higher level cognition. This symposium will involve presentations by four exemplars of this approach to cognitive modeling (ACT, CAPS, EPIC, and SOAR). The presentations will try to illustrate the range of applications to which such models are appropriate, what the similarities and differences are among the various architectures, and what some of the interesting research questions are within each architecture.