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Anisotropies in the cortical representation of various stimulus parameters can reveal the fundamental mechanisms by which sensory properties are analysed and coded by the brain. One example is the preference for motion radial to the point of fixation (i.e. centripetal or centrifugal) exhibited in mammalian visual cortex. In two experiments, this study used functional magnetic resonance imaging (fMRI) to explore the determinants of these radial biases for motion in functionally-defined areas of human early visual cortex, and in particular their dependence upon eccentricity which has been indicated in recent reports. In one experiment, the cortical response to wide-field random dot kinematograms forming 16 different complex motion patterns (including centrifugal, centripetal, rotational and spiral motion) was measured. The response was analysed according to preferred eccentricity within four different eccentricity ranges. Response anisotropies were characterised by enhanced activity for centripetal or centrifugal patterns that changed systematically with eccentricity in visual areas V1-V3 and hV4 (but not V3A/B or V5/MT+). Responses evolved from a preference for centrifugal over centripetal patterns close to the fovea, to a preference for centripetal over centrifugal at the most peripheral region stimulated, in agreement with previous work. These effects were strongest in V2 and V3. In a second experiment, the stimuli were restricted to within narrow annuli either close to the fovea (0.75-1.88°) or further in the periphery (4.82-6.28°), in a way that preserved the local motion information available in the first experiment. In this configuration a preference for radial motion (centripetal or centrifugal) persisted but the dependence upon eccentricity disappeared. Again this was clearest in V2 and V3. A novel interpretation of the dependence upon eccentricity of motion anisotropies in early visual cortex is offered that takes into account the spatiotemporal "predictability" of the moving pattern. Such stimulus predictability, and its relationship to models of predictive coding, has found considerable support in recent years in accounting for a number of other perceptual and neural phenomena.