Visual-spatial attention enhances the perception of behaviorally relevant stimuli. One issue that remains unclear is whether attention is preferentially allocated to stimuli that remain fixed in one reference frame (e.g., retina-centered), or whether it could be equally allocated to stimuli fixed in other frames. We investigated this issue by asking observers to covertly attend to sinusoidal gratings fixed in different reference frames and to discriminate changes in their orientation. First, we quantified orientation discrimination thresholds (ODTs) while subjects pursued a moving dot and either attended to a retina- or a space-centered grating. We then measured ODTs while subjects divided attention between the two gratings. We found that dividing attention proportionally increased ODTs for both target gratings relative to the focused attention condition. Second, we used the same stimulus configuration and conditions during a fixation task. Here, one grating was retina- and space-centered while the other moved in space and on the retina. Again, ODTs during divided attention proportionally increased for both gratings. These increases were similar to those measured during smooth pursuit. Our results show that humans can proportionally divide attention between targets centered in different reference frames during both smooth pursuit eye movements and fixations.

*mean luminance*= 41.5 cd/m

^{2}). They consisted of two identical sinusoidal gratings with a spatial frequency of 2 cycles per degree and a diameter of 1.6 degrees of visual angle. At the beginning of each trial, the two gratings appeared at the center of the screen superimposed on one another (Figure 1A). The smooth pursuit target was a black dot with a diameter of 0.6 degree of visual angle. Its initial position was either to the left, right, or above the two central sinusoidal gratings at an eccentricity of 8.75 degrees of visual angle.

*p*= 0.13, one-way ANOVA). We therefore considered the effect of saccades on ODT measurements negligible. One subject, however, was excluded from the analysis due to the presence of numerous saccades toward the target grating during the orientation change period.

*vel*) components were computed using the following equation:

*x*is the eye position in degrees at time

*i,*and Δ

*t*is the time window (50 ms) over which the velocity is calculated.

*angVel*) using

*dist*) from each single data point (

*i*) to the center of the space-centered target grating (which was also the center of the screen) was computed using

*x*is the horizontal eye position (deg), and

*y*is the vertical eye position at sampling point

*i*. In order to obtain an average radius per trial, the mean of the single point distances (radii) was calculated. The mean radii of individual trials were subsequently pooled across experimental conditions and used for statistical analysis. We also estimated the variability of the eye positions around the mean radius by computing, for every trial, the standard deviation (Std) of the distribution of single point distances around the radius. Thereafter, a mean Std was determined for each subject and condition.

*t*-test) were applied. To facilitate the interpretation of the eye position data and comparison with the existing literature, we used parametric statistics.

*p*< 0.0001 in all three conditions,

*t*-test). A possible explanation for this result is that subjects tried to minimize the distance between the smooth pursuit dot and the gratings by fixating the edge closer to the target, rather than the dot's center. More importantly, mean radii of the three conditions (

*retina-centered*= 8.59,

*space-centered*= 8.6,

*divided attention*= 8.56) were not significantly different from each other (

*p*= 0.79, one-way ANOVA). This indicates that subjects pursued the dot with similar accuracy in all conditions.

*retina-centered*= 0.233,

*space-centered*= 0.221,

*divided attention*= 0.227), considering that on average they fall within the size of the smooth pursuit dot (0.6-degree diameter, Figure 3A). We therefore conclude that the subjects' eye position trajectories are well described by a quarter of a circle. In addition, the mean Std values of the three groups closely resembled each other (

*p*= 0.89, one-way ANOVA), indicating that trajectories did not significantly change across the experimental conditions.

*retina-centered*= 1,

*space-centered*= 0.98,

*divided attention*= 0.99, black solid line) and were not significantly different between conditions (

*p*= 0.82, one-way ANOVA). These results suggest that subjects tracked the pursuit target with similar accuracy in the three conditions without making eye movements toward the corresponding target grating.

*mean*= 0.34,

*p*= 0.0002, Wilcoxon Rank Sign Test), confirming higher ODTs for space-centered relative to retina-centered targets. One likely cause of this difference in ODTs is that besides being centered in different frames, the two targets had different retinal velocity. Indeed, we performed a control experiment in three subjects and found that when increasing the retinal velocity of a target, the ODTs steadily increased (see Supplementary Figure 3). This indicates that when evaluating possible changes in ODTs in the divided relative to the focused attention condition, we must compensate for the effect of each target retinal velocity.

*retina-centered*= 0.1 [or 22%];

*space-centered*= 0.08 [or 17%]), indicating that dividing attention increases ODTs (

*retina-centered*:

*p*= 0.006;

*space-centered*:

*p*= 0.001, Wilcoxon Rank Sign Test). When comparing the mean values corresponding to both reference frames, we found no difference in magnitude of attentional modulation (

*p*= 0.5, Wilcoxon Rank Sign Test for paired data). These data show that the relative cost of dividing attention on ODTs was similar for both the retina-centered and space-centered targets.

*p*= 0.17, one-way ANOVA), suggesting that the subjects' eye positioning during fixation did not change across conditions. Supporting this finding we also found quasi-homogenous and low saccade detection rates across conditions (

*stationary*= 0.7%;

*moving*= 1%;

*divided attention*= 0.7%).

*smooth pursuit*= 0.38;

*fixation*= 0.65,

*p*= 0.016, Wilcoxon Rank Sign Test for paired data).

*stationary*= 0.13;

*moving*= 0.1,

*p*= 0.69, Wilcoxon Rank Sign Test for paired data) and during fixation (

*stationary*= 0.13;

*moving*= 0.09,

*p*= 0.69, Wilcoxon Rank Sign Test for paired data). We further conducted an ANOVA comparing all four AMIs and found no difference between the four groups (

*p*= 0.77, Kruskal–Wallis ANOVA, see gray and white bars in Figure 8C). This result indicates that dividing attention produced a similar increase in ODTs for the different target types during both fixation and smooth pursuit.