Feature-tracking explanations of 2D motion perception are fundamentally distinct from motion-energy, correlation, and gradient explanations, all of which can be implemented by applying spatiotemporal filters to raw image data. Filter-based explanations usually suffer from the aperture problem, but 2D motion predictions for moving plaids have been derived from the intersection of constraints (IOC) imposed by the outputs of such filters, and from the vector sum of signals generated by such filters. In most previous experiments, feature-tracking and IOC predictions are indistinguishable. By constructing plaids in apparent motion from missing-fundamental gratings, we set feature-tracking predictions in opposition to both IOC and vector-sum predictions. The perceived directions that result are inconsistent with feature tracking. Furthermore, we show that increasing size and spatial frequency in Type 2 missing-fundamental plaids drives perceived direction from vector-sum toward IOC directions. This reproduces results that have been used to support feature-tracking, but under experimental conditions that rule it out. We discuss our data in the context of a Bayesian model with a gradient-based likelihood and a prior favoring slow speeds. We conclude that filter-based explanations alone can explain both veridical and non-veridical 2D motion perception in such stimuli.