Keeping track of objects in our environment across body and eye movements is essential for perceptual stability and localization of external objects. As of yet, it is largely unknown how this perceptual stability is achieved. A common behavioral approach to investigate potential neuronal mechanisms underlying spatial vision has been the presentation of one brief visual stimulus across eye movements. Here, we adopted this approach and aimed to determine the reference frame of the perceptual localization of two successively presented flashes during fixation and smooth pursuit eye movements (SPEMs). To this end, eccentric flashes with a stimulus onset asynchrony of zero or ± 200 ms had to be localized with respect to each other during fixation and SPEMs. The results were used to evaluate different models predicting the reference frame in which the spatial information is represented. First, we were able to reproduce the well-known effect of relative mislocalization during fixation. Second, smooth pursuit led to a characteristic relative mislocalization, different from that during fixation. A model assuming that relative localization takes place in a nonretinocentric reference frame described our data best. This suggests that the relative localization judgment is performed at a stage of visual processing in which retinal and nonretinal information is available.

*p*value. The test with the lowest

*p*value is tested first with a Bonferroni correction factor representing the total number of tests performed. The test with the second lowest

*p*value is then tested with a Bonferroni correction factor involving one test less and so on for the remaining tests. This approach is less conservative than the standard Bonferroni correction and thus more powerful in detecting truly significant effects (Abdi, 2010). In the Results section, we will provide

*p*values that were significant after applying Holm's correction for each type of test. Furthermore, we calculated Cohen's

*d*from the

_{z}*t*value of the statistical tests, divided by the square root of the number of participants as a measure of effect size (Lakens, 2013).

*t*(7) = –5.91,

*p*< 0.001,

*d*= 2.09, two-tailed paired-sample

_{z}*t*test; “Behind” vs. Fixation:

*t*(7) = 11.56,

*p*< 0.001,

*d*= 4.09, two-tailed paired-sample

_{z}*t*test). In addition, the difference of the PSEs in the “Ahead” compared to the “Behind” condition was statistically significant (

*t*(7) = –18.63,

*p*< 0.001,

*d*= 6.59, two-tailed paired-sample

_{z}*t*test).

*t*(7) = 3.00,

*p*= 0.01,

*d*= 1.06, one-tailed one-sample

_{z}*t*test). Compared to fixation, the mislocalization increased when the eyes were moving toward the flashed stimuli (Figure 4H; PSE = +1.3° ± 0.63°) and decreased for pursuit away from the flashes (Figure 4I; PSE = +0.14° ± 0.32°). This time, for a positive SOA, the shift of the PSE was significantly different between “Pursuit Behind” and the fixation condition (

*t*(7) = –5.28,

*p*= 0.001,

*d*= 1.87, two-tailed paired-sample

_{z}*t*test) and showed a trend when comparing “Pursuit Ahead” and fixation (

*t*(7) = 2.29,

*p*= 0.055,

*d*= 0.81, two-tailed paired-sample

_{z}*t*test). Furthermore, the difference between PSE values for the “Ahead” as compared to the “Behind” condition was statistically significant (

*t*(7) = 4.67,

*p*= 0.002,

*d*= 1.65, two-tailed paired-sample

_{z}*t*test). Similar to the fixation paradigm, during “Pursuit Ahead,” the average PSE value was significantly higher during the “pos. SOA” condition as compared to the “neg. SOA” (

*t*(7) = 13.57,

*p*< 0.001,

*d*= 4.80, two-tailed paired-sample

_{z}*t*test). On the other hand, in the “Pursuit Behind” condition, the average PSE value was significantly smaller for positive SOAs as compared to negative SOAs (

*t*(7) = –3.90,

*p*= 0.006,

*d*= 1.38, two-tailed paired-sample

_{z}*t*test).

*t*(7) = 2.95,

*p*= 0.021,

*d*= 1.03, two-tailed paired-sample

_{z}*t*test with averaged data of all subjects). In addition, due to the pursuit direction, this perceptual shift was not away from the fovea or the center of the screen but toward it (mean perceived location = 6.55° when presented at 7.5°). For more eccentric flash positions, this perceptual shift toward the fovea became smaller. Just as for the “Ahead” condition, in the “Behind” condition, the slope of the linear regression curve describes the magnitude of the absolute mislocalization as a function of retinal eccentricity. In this case, a slope of 0.26 indicates that the target presented in the “Behind” condition was perceived on average 0.52° more eccentric than the reference stimulus when presented with a positive SOA of 200 ms, resulting in an eye position shift of 2° within that time. To compensate for this potential eccentricity effect, the PSE has to be shifted by –0.52° with respect to the PSE of the “Pos. SOA” condition in the “Fixation Relative” paradigm. Likewise, the eccentricity effect on the PSE values in the “Ahead” and in the “Behind” condition was determined for each subject (Table 2).

*r*= 0.84, Pearson correlation) and the slope of the linear regression with a value of 0.92 was close to 1. Furthermore, the pointwise distance of the data points from identity was very small (d = 0.25 ± 0.14), indicating a high similarity of data and model. In comparison, Model A underestimated the measured data with a slope of 0.21. Consequently, the pointwise distance from identity (d = 2.38 ± 1.39) was larger than for Model C. Likewise, Models B and D did not predict the pursuit PSE positions very well. These two models overestimated the data with slopes of 1.74 for Model B and 2.45 for Model D. This led to considerable deviations from the identity line with an average pointwise distance of d = 2.23 ± 1.3 for Model B and d = 4.37 ± 2.55 for Model D.

*Encyclopedia of research design*(pp. 574–578). Thousand Oaks, CA: Sage.

*Psychologica,*84, 135–159.

*Nature Neuroscience,*8(7), 941–949. [CrossRef]

*Brain and Cognition,*68, 309–326. [CrossRef]

*On the side of the reflection optics: Books nine*. Naples, Italy: Carlinum and Pacem.

*Advances in neural population coding*(pp. 175–190). Durham, NC, USA: Elsevier Science.