Figure 3. Unimodal data showing significant motion priming of the current trial’s perceived velocity by the previous trial’s velocity. (A) For each modality, trials were binned into two groups based on the speed of the preceding trial (‘previous slower’ = 20, 40, 50 °/s; ‘previous faster’ = 70, 80, 100 °/s) and psychometric functions were fitted to each group. For both modalities, the PSE for the ‘previous faster’ group was significantly lower than for the ‘previous slower’ group. Error bars show ±1 standard deviation based on 10,000 bootstraps. (B) Motion priming for all levels of previous velocity. PSEs decline (more “perceived faster” responses) as preceding velocity increases. Bootstrap sign-tests confirmed the slopes of the best-fitting lines for audition and vision were both significantly negative but did not differ from each other (p = .2078). Error bars show 95% confidence intervals based on 10,000 bootstraps.
With the priming effect established at the level of fast vs slow, we went further and analysed the PSE shift more finely. To do this, we split the ‘previous slower’ group into three smaller groups corresponding to its three levels of previous velocity (i.e., 20, 40, 50 °/s) and fitted psychometric functions to each of the groups. We did the same for the ‘previous larger’ data (splitting it into three previous velocities: 70, 80, 100°/s). Figure 3b shows the PSEs as a function of these previous velocities, for both audition and vision. Dividing the data in this way reduces the observations in each group by a factor of three yet there is still a clear and significant negative slope for each modality (audition: slope = -.0918, p = .0008; vision: slope = -.0594, p = .0181), thus showing the same priming relationship as in Figure 3A (previous faster speeds consistently shifting PSEs to the left, indicating increasing levels of “perceived faster” responses). The priming functions for auditory and visual motion are very similar. To test if they were different, we bootstrapped the data for each level of previous speed 10,000 times and re-calculated the best linear fit across the previous speed levels 10,000 times. If the auditory and visual data have different slopes, then the differences between the bootstrapped slopes for audition and vision should be consistently different from zero. A bootstrap sign-test confirmed this was not the case (p = .2078). Thus, motion priming effects as a function velocity are very similar for both auditory and visual motion.