When we look at a scene, our gaze does not move continuously across it. Instead, our eyes move discontinuously, shifting gaze rapidly from point to point to focus on different locations in the scene. These eye movements are known as saccades, and during them the brain temporarily and selectively stops processing visual information.

In the brain, a particular area of a scene is represented by different neurons before and after a saccade. Paying attention to a relevant location in a scene across an eye movement therefore requires the brain to shift its attentional effects from the neurons that represented that location in the scene before the saccade to the set of neurons that do so after the saccade. Ideally, this shift should happen rapidly and be synchronized with the eye movement.

Exactly how long it takes for attention to emerge at a relevant location after a saccade was not clear because attention had not been recorded on a fine enough time-scale immediately after an eye movement. Yao et al. have now addressed this issue in a series of experiments that asked volunteers to focus their eyes on a fixed point. The volunteers had to follow the point with their eyes as it jumped to a new location, and at the same time had to look out for a change in the movement of a pattern of random dots.

The results reveal that attention is fully available at the relevant location within 30 milliseconds after the saccade. In fact, the 30-millisecond delay in the emergence of attention matches the period during which vision is suppressed during a saccade. Thus, the change in the brain’s focus of attention coordinates with the saccadic eye movement to ensure that attention can be fixed on a relevant location as soon as possible after the eye movement ends. More studies are now needed to investigate how the brain coordinates its attention and eye-movement processes to synchronize the shift in attention with the eye movement.

The processing of vision and visuospatial attention mostly proceeds via retinotopic representations in the brain (Cavanagh et al., 2010; Wurtz, 2008). Since each saccadic eye-movement leads to a change in the retinotopic representation of the visual scene, maintaining attention at a task-relevant spatial location across a saccade necessitates a rapid, saccade-synchronized shift of attentional modulation from the neuronal population representing the task-relevant location before the saccade to the one representing it after the saccade (Marino and Mazer, 2016; Yao et al., 2016). Currently, perceptual measurements in humans suggest a neuronal attention shift that starts before the saccade and continues after the saccade ends (Rolfs et al., 2011; Szinte et al., 2015; Golomb et al., 2008, 2010a, 2011; Jonikaitis et al., 2013). However, because these previous measurements used coarse temporal sampling and/or long-duration attentional probes, the precise time at which spatial attention becomes fully allocated to the task-relevant location after the saccade remains unclear. Here, using a fine-grained temporal analysis of human peri-saccadic detection performance in an attention task, we show that spatial attention is fully available at the task-relevant location within 30 milliseconds after the saccade. This rapid post-saccadic recovery of performance in our attention task indicates that retinotopic attentional shifts occur within the time required to recover from saccadic suppression of vision. Subjects almost never responded to the distractor change, indicating that they tracked the attentional target veridically throughout the task. Spatial attention and saccadic processing therefore co-ordinate well to ensure that relevant locations are attentionally enhanced soon after the beginning of each eye fixation.

We measured human peri-saccadic attentional allocation by combining an endogenous spatial attention task with a visually-guided saccade. Human subjects had to make a saccade to follow a fixation point when it jumped to a new location, and concurrently, pay attention throughout the trial to a target moving random-dot pattern (RDP) presented eccentrically among three or five physically similar distractor RDPs (Figure 1, and 'Materials and methods'). We measured the subjects’ attentional allocation by their ability to detect a brief (23.5 ms) change in target motion, while ignoring similar changes in the distractors. The target and distractor changes occurred at different times around the saccade, allowing us to report for the first time, peri-saccadic performance in an attention task with fine-grained temporal precision. The intervening saccade poses a challenge for the attentional system, because due to the retinotopic shift of the target location across the saccade, the attentional system needs to shift its modulatory influence from the neuronal population representing the target before the saccade to the neuronal population representing the target after the saccade. By using a fixed timing and location for the fixation point jump, we could isolate the dynamics of this attentional remapping process and minimize its interaction with the dynamics of attentional allocation to other exogenous visual events. We therefore made the saccade spatially and temporally predictable by having the fixation point jump at the same time and to the same location on each trial so that the subject could best focus on the target pattern.

Figure 1

Download asset Open asset

In Experiment 1, we looked at the peri-saccadic performance of 8 subjects (pooled data in Figure 2A, individual subject-data in Figure 2—figure supplement 1). At times well before and well after the saccade, subjects almost always detected the target change and their performance was near 100%. Performance began to drop around the time the fixation point jumped (dashed vertical line in Figure 2A), as expected from the previously reported diversion of pre-saccadic attentional resources towards the saccade task (Deubel and Schneider, 1996; Montagnini and Castet, 2007; Hoffman and Subramaniam, 1995) and the post-saccadic retinotopic location (Szinte et al., 2015). The performance then dropped steeply right before the saccade, as expected from the drop in visual sensitivity due to saccadic suppression (Diamond et al., 2000; Dorr and Bex, 2013; McConkie and Loschky, 2002). Importantly, our data show (for the first time in an attention task, to our knowledge) that performance recovered back to baseline within 30 ms of saccade offset (Figure 2A). The rapid post-saccadic recovery of performance indicates that attention is allocated to the task-relevant location within 30 ms after the saccade ends. The rapid time-course of recovery resembles that previously shown for saccadic suppression of visual performance in tasks where visual sensitivity was probed around a saccade using a briefly flashed change, but without any requirement to maintain attention on a target across a saccade (Diamond et al., 2000; Dorr and Bex, 2013; McConkie and Loschky, 2002). This suggests that while resumption of visual function after a saccade is constrained by the recovery from saccadic suppression (Krekelberg, 2010), the peri-saccadic attentional shift necessitated by retinotopic visual processing does not impose an additional temporal cost on this recovery. The rapid post-saccadic attentional availability at the target location that we infer from our data is consistent with the only physiological data on this issue: in a mental curve-tracing task similar to ours with a fixed attentional target, attentional effects in monkey V1 emerge approximately 80 ms after the end of the saccade (Khayat et al., 2004). Given an onset latency of approximately 30 to 50 ms in monkey V1, MT and LIP (Khayat et al., 2004; Bair et al., 2002; Bisley et al., 2004), a change occurring 30 ms after saccade offset would reach the visual cortex at approximately the time when its neurons representing the target after the saccade are attentionally enhanced.

Figure 2 with 5 supplements see all

Download asset Open asset

Figure 2—source data 1

Data plotted in Figure 2A and Figure 2—figure supplement 1.

https://doi.org/10.7554/eLife.18009.005

Download elife-18009-fig2-data1-v2.txt

Figure 2—source data 2

Data plotted in Figure 2B and Figure 2—figure supplement 2.

https://doi.org/10.7554/eLife.18009.006

Download elife-18009-fig2-data2-v2.txt

Figure 2—source data 3

Data plotted in Figure 2—figure supplement 3.

https://doi.org/10.7554/eLife.18009.007

Download elife-18009-fig2-data3-v2.txt

Figure 2—source data 4

Data plotted in Figure 2—figure supplement 4.

https://doi.org/10.7554/eLife.18009.008

Download elife-18009-fig2-data4-v2.txt

Figure 2—source data 5

Data plotted in Figure 2—figure supplement 4.

https://doi.org/10.7554/eLife.18009.009

Download elife-18009-fig2-data5-v2.txt

It is possible that though we report a rapid recovery in Experiment 1, the true recovery was actually slower, but was masked by the fact that performance had already reached its maximum value of 100% within 30 ms of saccade offset. We therefore performed a similar experiment (Experiment 2) with two task difficulties, where peak performance on the harder task was clearly below 100% (Figure 2B). Once again, performance recovered to baseline levels within 30 ms of saccade offset in both the easier and the harder task, indicating that our estimate of a rapid recovery time for performance was genuine and not an artifact due to a ceiling effect. The recovery time-course after the saccade also did not seem to depend on saccade latency (Figure 3). Very similar performance was observed when we grouped trials based on saccade latency into three groups: putative predictive saccades (latencies from 0 to 75 ms), express saccades (latencies from 75 to 125 ms) and regular-latency saccades (latencies from 125 to 250 ms). This indicates that though various differences between these different kinds of saccades have been noted and these different kinds of saccades have been speculated to arise via different neural pathways (Bronstein and Kennard, 1987; Chen et al., 2013; Cotti et al., 2009; Deubel, 1995; Gaymard et al., 1998; Pierrot-Deseilligny et al., 2002; Shelhamer and Joiner, 2003), peri-saccadic attentional shifts seem to proceed with a similar time-course in each case.

If the peri-saccadic attentional shift is not temporally well-synchronized with the saccade, attention will be peri-saccadically allocated to irrelevant spatial locations. In fact, prior findings measuring discrimination performance for attentional probes at different locations suggest that by about 75 ms before the saccade, attentional enhancement could be seen at the ‘post-saccadic’ retinotopic location (which would be the wrong pre-saccadic spatial location) (Rolfs et al., 2011). Other studies report that after the saccade, attention stays at the pre-saccadic retinotopic location (which would be the wrong post-saccadic spatial location) for up to 100 ms after the saccade (Golomb et al., 2008, 2010b). The predictive emergence of attention is consistent with single-neuron data from monkeys showing predictive responses in different attentional control areas of the brain (Wurtz, 2008; Duhamel et al., 1992), while imaging data from humans have been presented as evidence for persistent retinotopic neural activity (Golomb et al., 2010a). Results from a more recent detailed study indicate that peri-saccadic attentional spread and dynamics may show complex patterns: patterns consistent with predictive shifts, transient retinotopic persistence as well as rapid post-saccadic availability of attention at the task-relevant location were seen (Jonikaitis et al., 2013). In our data, we found no effect of predictive or delayed shifts on the rate of responding to distractor changes (false-positives). In both Experiments 1 and 2, overall, subjects responded to a distractor change on only 2.2% and 2% of trials respectively. Distractor changes occurred either at the distractor vertically below the target (a control) or at the distractor to the right of the target (that tested post-saccadic retinotopic persistence of the pre-saccadic attentional focus). In the time interval immediately after the saccade (0–150 ms), the data from both Experiments showed no statistically significant increase in the rate of false-positives due to retinotopic persistence (compared to the control location; all p-values>0.16, Boschloo’s test; Supplementary file 1A). An additional experiment (Experiment 3; Figure 2—figure supplement 3 and Supplementary file 1B) where we changed the task design to test both pre-saccadic predictive shifts and post-saccadic retinotopic persistence (while making distractor changes more numerous and salient) also led to a false-positive rate of less than 1.4% and no evidence for an effect of either predictive shifts or retinotopic persistence on the false-positive rate. Subjects thus tracked the attentional target veridically throughout our task, and the peri-saccadic spread of attention to irrelevant spatial locations reported in previous studies does not seem to have any manifest effects in our task. One important difference between our task and previous tasks was that we included only one attended location within each trial, and stimuli at all other locations were distractors that the subject had to ignore. In contrast, the previous tasks required subjects to report a probe stimulus that could appear at any of the stimulus locations. There were no distractor stimuli, and attention was instead manipulated by using a dual-task (Rolfs et al., 2011; Golomb et al., 2008, 2010b) or using an exogenous cue (Jonikaitis et al., 2013). The fact that all stimulus locations on each trial were potential targets in the previous studies may have led the subjects to adopt a different attentional-set compared to the subjects in our study. Alternatively, the previous results may have reflected only an attentional effect on probe visibility, while the results in our task may additionally reflect the effect of attention on distractor filtering. In current theoretical accounts of attention (Eckstein et al., 2009; Lu and Dosher, 1998), the effects of attention on distractor filtering and probe visibility correspond to the distinct effects of attention on selection/weighting and sensory signal enhancement respectively. In this scenario, distractor filtering due to the attentional selection/weighting of sensory signals across the visual field is well-synchronized to the saccade and therefore does not spread to irrelevant spatial locations. In contrast, attentional signal enhancement, but not distractor filtering, is influenced by the predictive shifts and post-saccadic retinotopic persistence of attentional modulation in the brain. As a result, in the previous tasks without a distractor filtering component, the perceptual visibility of probes at irrelevant locations was improved. In our task, any enhanced sensory signal from distractor locations would continue to be down-weighted and filtered out and the subjects would not respond to them. We emphasize that this is only one plausible explanation, and theoretical models of attention are sufficiently complex and flexible to admit alternative explanations. Even more generally, the observed differences could be a result of task-dependent (or even entirely different) attentional mechanisms operating in the different tasks. Extensive measurements and model-testing will be necessary to disambiguate the different possibilities.

Our data represent an important advance in the ongoing discussion about the shifts of spatial attention around the time of a saccade (Cavanagh et al., 2010; Marino and Mazer, 2016; Rolfs and Szinte, 2016; Mayo and Sommer, 2010; Melcher, 2010). We provide the first temporally fine-grained measurements of detection performance in an attention task in the critical immediate post-saccadic period (0 to 100 ms following saccade offset). Our data show that performance fully recovers soon after the end of the saccade, indicating that the correct stimulus is attended to during this immediate post-sacadic period when visual sensitivity is known to be highest (Ibbotson and Krekelberg, 2011). The rapid time-course of recovery resembles the time-course previously shown for the recovery of visual function from saccadic suppression, suggesting that the retinotopic attentional shift does not impose an additional temporal cost on the resumption of visual function after a saccade. Further, our data indicate that under our task conditions, subjects very rarely confuse a distractor stimulus for the target. Spatial attention and saccadic execution thus appear to co-ordinate well to ensure that relevant objects are attentionally enhanced soon after the beginning of each eye fixation. These findings are likely to lead to a much better understanding of the impact of peri-saccadic changes in neural activity on visual processing.

We measured peri-saccadic attentional allocation by combining a spatial attention task with a visually-guided saccade. We asked human observers to make a saccade to a visual target, and within the same trial, also report a speed and direction change in a target moving random-dot pattern (RDP), while ignoring a similar change in one of the simultaneously-presented distractor RDPs. The target and distractor changes occurred at different times around the saccade, allowing us to measure peri-saccadic attentional performance with fine-grained temporal precision.

Task design

We describe Experiment 1 first. Experiments 2 (Figure 2B) and 3 (Figure 2—figure supplement 3) are variants of Experiment 1.

Experiment 1

Request a detailed protocol

Each trial was started by the subject pressing the space-bar. A fixation point appeared on the screen and subjects maintained their gaze within 2° of this point. The subjects concurrently performed a spatial attention task and a saccade task on each trial: they were instructed to pay attention to the target RDP and make a saccade if the fixation point jumped to a new location. For the spatial attention task, after 647 ms of fixation, four circular moving random-dot patterns (RDPs: each presented within a circular aperture of 2° radius, with dots moving upwards with a speed of 8° of visual angle per second; dot density = 10 dots per deg²), were displayed on the screen. Individual RDP dot size was 0.15 × 0.15°. The subjects were instructed that the RDP at a pre-specified location (3.5° to the right and 4° above the fixation point: Figure 1) was the target: they had to pay attention to that stimulus throughout the trial in order to respond by pressing the downward-arrow key within 600 ms when they detected a brief 2-frame (23.5 ms) speed and direction-change in the target RDP. For these two frames, the RDP dots moved faster at 16° per second and horizontally either to the left or the right, and then resumed motion with the original speed and direction. Any changes in the distractor RDPs were to be ignored. The median reaction-time was 324 ms. The second RDP was placed 7° to the right of the target RDP so that post-saccadically, it had the same retinotopic location as the target RDP did pre-saccadically. The other two RDPs were placed at corresponding locations in the lower hemifield. A target RDP change occurred on about 90% of the trials and between 118 to 1882 ms after RDP onset; the remaining trials were catch trials and no change occurred. A distractor change occurred before the target change on about 39% of trials, over a similar range of times but at least 400 ms before the target change: the subject had to ignore these changes. Only one target change and possibly also one distractor change occurred on each trial. The distractor change could occur either at the RDP to the right of the target (with a post-saccadic retinotopic location identical to the target’s pre-saccadic retinotopic location) or the RDP below the target. For the saccade task, the fixation point jumped to a new location 7° horizontally to the right of the fixation point 1235 ms after the fixation point appeared. The subjects had to make a single saccade within 553 ms of the fixation point jump to fixate the saccade target location and then maintain their gaze within 2° of the saccade target for the remainder of the trial. Most saccades occurred with a much shorter latency (Figure 3). The use of a predictable time at which the fixation point jumped was advantageous because subjects could focus their attention better on the target RDP without worrying about the temporal uncertainty about when the fixation point would jump. Trials were terminated when the subject pressed the downward arrow key, broke fixation or failed to press a key within 600 ms of a change in the target RDP. Subjects received no other feedback about trial outcome. The use of a 2° fixation window during the two fixation periods was not critical. We also obtained similar results when using a narrower fixation window of 0.5°: we ensured that the eye did not deviate by more than 0.5° from the median horizontal and vertical eye-position during fixation on each trial (Figure 2—figure supplement 5). Using the median eye-position compensates for across-trial drifts in calibration and is based on the standard calibration assumption that normal-viewing subjects will foveate a visual target when asked to fixate on it and therefore, their eye-position variability will be centered on the fixated location.

Figure 3

Download asset Open asset

Figure 3—source data 1

Data plotted in Figure 3.

https://doi.org/10.7554/eLife.18009.016

Download elife-18009-fig3-data1-v2.txt

Experiment 2

Request a detailed protocol

This was similar to Experiment 1, with the following key differences. Two task difficulties were used and the change involved only a motion direction change, without a speed change. The two task difficulties were created by using two magnitudes of direction change for each subject; these magnitudes were chosen in a separate calibration session,where the overall detection performance was estimated for nine direction-change magnitudes between 20 and 90°. The calibration session used a fixation task similar to the task in Experiment 2 except that no saccade was required. The direction-changes that led to approximately 70% and 90% detection performance were chosen for Experiment 2. Across subjects, the direction-change varied between 35 and 60° for the hard task and between 50 and 90° for the easier task. Also, to make more of the target changes occur peri-saccadically, the timing of the task was slightly modified so that the RDPs came on at 412 ms after fixation point onset (Figure 1), and the target motion change occurred from 235 ms to 1647 ms after RDP onset; approximately 27% of trials had a distractor change before the target-change over a similar time-frame (118 ms to 1224 ms, with the same constraint of a 400 ms separation from the target-change as in Experiment 1). About 7% of trials were catch trials.

Experiment 3

Request a detailed protocol

This was also similar to Experiment 1, except that we used 2 additional distractor RDPs, giving a total of 6 RDPs instead of 4. One of the additional RDPs was placed seven degrees to the left of the target RDP, which is the location to which attention would be expected to predictively switch just before the saccade. The other RDP was placed eight degrees below this RDP, in line with the other RDPs in the lower hemifield. Further, to make the distractor changes more salient and improve the chances of a false-positive, the speed now increased during the motion change from 4 to 32° per second (instead of 8 to 16° per second in Experiment 1); the direction-change remained at 90° (vertically upward to horizontal towards the left or the right). The range of target change times was slightly delayed compared to Experiments 1 and 2 so that a distractor change could occur more often before a target change and a false positive potentially elicited: the target changes in Experiment 3 could occur from 412 to 2176 ms after RDP onset. The distractor change occurred from 470 to 941 ms after RDP onset so that the distractor changes now occurred more often (about 60% of target-changes were now preceded by distractor changes) and mostly before the saccade. Distractor changes occurred either to the right of the target (to measure post-saccadic retinotopic persistence) or to the left of the target (to measure pre-saccadic predictive shifts). About 6% of trials were catch trials.

1. 1. Bahill AT
   2. Clark MR
   3. Stark L

   (1975) Dynamic overshoot in saccadic eye movements is caused by neurological control signed reversals

   Experimental Neurology 48:107–122.

   https://doi.org/10.1016/0014-4886(75)90226-5

   • PubMed
   • Google Scholar
2. 1. Bair W
   2. Cavanaugh JR
   3. Smith MA
   4. Movshon JA

   (2002)

   The timing of response onset and offset in macaque visual neurons

   Journal of Neuroscience 22:3189–3205.

   • PubMed
   • Google Scholar
3. Report

   1. Berger RL

   (1994)

   Power comparison of exact unconditional tests for comparing two binomial proportions. Institute of Statistics Mimeo Series (2266)

   North Carolina State University.

   • Google Scholar
4. 1. Bisley JW
   2. Krishna BS
   3. Goldberg ME

   (2004) A rapid and precise on-response in posterior parietal cortex

   Journal of Neuroscience 24:1833–1838.

   https://doi.org/10.1523/JNEUROSCI.5007-03.2004

   • PubMed
   • Google Scholar
5. 1. Bronstein AM
   2. Kennard C

   (1987) Predictive eye saccades are different from visually triggered saccades

   Vision Research 27:517–520.

   https://doi.org/10.1016/0042-6989(87)90037-X

   • PubMed
   • Google Scholar
6. 1. Brown LD
   2. DasGupta A
   3. Cai TT

   (2001) Interval estimation for a binomial proportion

   Statistical Science 16:101–133.

   https://doi.org/10.1214/ss/1009213286

   • Google Scholar
7. Software

   1. Calhoun P

   (2015) Exact: unconditional exact test, version R package version 1.6

   Exact: unconditional exact test.

   https://CRAN.R-project.org/package=Exact
8. 1. Cavanagh P
   2. Hunt AR
   3. Afraz A
   4. Rolfs M

   (2010) Visual stability based on remapping of attention pointers

   Trends in Cognitive Sciences 14:147–153.

   https://doi.org/10.1016/j.tics.2010.01.007

   • PubMed
   • Google Scholar
9. 1. Chen M
   2. Liu Y
   3. Wei L
   4. Zhang M

   (2013) Parietal cortical neuronal activity is selective for express saccades

   Journal of Neuroscience 33:814–823.

   https://doi.org/10.1523/JNEUROSCI.2675-12.2013

   • PubMed
   • Google Scholar
10. 1. Cotti J
    2. Panouilleres M
    3. Munoz DP
    4. Vercher JL
    5. Pélisson D
    6. Guillaume A

    (2009) Adaptation of reactive and voluntary saccades: different patterns of adaptation revealed in the antisaccade task

    The Journal of Physiology 587:127–138.

    https://doi.org/10.1113/jphysiol.2008.159459

    • PubMed
    • Google Scholar
11. 1. Deubel H
    2. Schneider WX

    (1996) Saccade target selection and object recognition: evidence for a common attentional mechanism

    Vision Research 36:1827–1837.

    https://doi.org/10.1016/0042-6989(95)00294-4

    • PubMed
    • Google Scholar
12. Book

    1. Deubel H

    (1995)

    Is saccadic adaptation context-specific ?

    In: Findlay JM, Walker R, Kentridge RW, editors. Eye Movement Research — Mechanisms, Processes, and Applications, volume 6. Elsevier Science. pp. 177–187.

    • Google Scholar
13. 1. Diamond MR
    2. Ross J
    3. Morrone MC

    (2000)

    Extraretinal control of saccadic suppression

    Journal of Neuroscience 20:3449–3455.

    • PubMed
    • Google Scholar
14. 1. Dorr M
    2. Bex PJ

    (2013) Peri-saccadic natural vision

    Journal of Neuroscience 33:1211–1217.

    https://doi.org/10.1523/JNEUROSCI.4344-12.2013

    • PubMed
    • Google Scholar
15. 1. Duhamel JR
    2. Colby CL
    3. Goldberg ME

    (1992) The updating of the representation of visual space in parietal cortex by intended eye movements

    Science 255:90–92.

    https://doi.org/10.1126/science.1553535

    • PubMed
    • Google Scholar
16. 1. Eckstein MP
    2. Peterson MF
    3. Pham BT
    4. Droll JA

    (2009) Statistical decision theory to relate neurons to behavior in the study of covert visual attention

    Vision Research 49:1097–1128.

    https://doi.org/10.1016/j.visres.2008.12.008

    • PubMed
    • Google Scholar
17. 1. Gaymard B
    2. Ploner CJ
    3. Rivaud S
    4. Vermersch AI
    5. Pierrot-Deseilligny C

    (1998) Cortical control of saccades

    Experimental Brain Research 123:159–163.

    https://doi.org/10.1007/s002210050557

    • PubMed
    • Google Scholar
18. 1. Golomb JD
    2. Chun MM
    3. Mazer JA

    (2008) The native coordinate system of spatial attention is retinotopic

    Journal of Neuroscience 28:10654–10662.

    https://doi.org/10.1523/JNEUROSCI.2525-08.2008

    • PubMed
    • Google Scholar
19. 1. Golomb JD
    2. Marino AC
    3. Chun MM
    4. Mazer JA

    (2011) Attention doesn't slide: spatiotopic updating after eye movements instantiates a new, discrete attentional locus

    Attention, Perception, & Psychophysics 73:7–14.

    https://doi.org/10.3758/s13414-010-0016-3

    • PubMed
    • Google Scholar
20. 1. Golomb JD
    2. Nguyen-Phuc AY
    3. Mazer JA
    4. McCarthy G
    5. Chun MM

    (2010a) Attentional facilitation throughout human visual cortex lingers in retinotopic coordinates after eye movements

    Journal of Neuroscience 30:10493–10506.

    https://doi.org/10.1523/JNEUROSCI.1546-10.2010

    • PubMed
    • Google Scholar
21. 1. Golomb JD
    2. Pulido VZ
    3. Albrecht AR
    4. Chun MM
    5. Mazer JA

    (2010b) Robustness of the retinotopic attentional trace after eye movements

    Journal of Vision 10:19.1–19.2.

    https://doi.org/10.1167/10.3.19

    • PubMed
    • Google Scholar
22. 1. Hoffman JE
    2. Subramaniam B

    (1995) The role of visual attention in saccadic eye movements

    Perception & Psychophysics 57:787–795.

    https://doi.org/10.3758/BF03206794

    • PubMed
    • Google Scholar
23. 1. Ibbotson M
    2. Krekelberg B

    (2011) Visual perception and saccadic eye movements

    Current Opinion in Neurobiology 21:553–558.

    https://doi.org/10.1016/j.conb.2011.05.012

    • PubMed
    • Google Scholar
24. 1. Jonikaitis D
    2. Szinte M
    3. Rolfs M
    4. Cavanagh P

    (2013) Allocation of attention across saccades

    Journal of Neurophysiology 109:1425–1434.

    https://doi.org/10.1152/jn.00656.2012

    • PubMed
    • Google Scholar
25. 1. Khayat PS
    2. Spekreijse H
    3. Roelfsema PR

    (2004) Correlates of transsaccadic integration in the primary visual cortex of the monkey

    PNAS 101:12712–12717.

    https://doi.org/10.1073/pnas.0301935101

    • PubMed
    • Google Scholar
26. 1. Krekelberg B

    (2010) Saccadic suppression

    Current Biology 20:R228–R229.

    https://doi.org/10.1016/j.cub.2009.12.018

    • PubMed
    • Google Scholar
27. 1. Lu ZL
    2. Dosher BA

    (1998) External noise distinguishes attention mechanisms

    Vision Research 38:1183–1198.

    https://doi.org/10.1016/S0042-6989(97)00273-3

    • PubMed
    • Google Scholar
28. 1. Marino AC
    2. Mazer JA

    (2016) Perisaccadic updating of visual representations and attentional states: linking behavior and neurophysiology

    Frontiers in Systems Neuroscience 10:3.

    https://doi.org/10.3389/fnsys.2016.00003

    • PubMed
    • Google Scholar
29. 1. Mayo JP
    2. Sommer MA

    (2010) Shifting attention to neurons

    Trends in Cognitive Sciences 14:389.

    https://doi.org/10.1016/j.tics.2010.06.003

    • PubMed
    • Google Scholar
30. 1. McConkie GW
    2. Loschky LC

    (2002) Perception onset time during fixations in free viewing

    Behavior Research Methods, Instruments, & Computers 34:481–490.

    https://doi.org/10.3758/BF03195477

    • PubMed
    • Google Scholar
31. 1. Mehrotra DV
    2. Chan IS
    3. Berger RL

    (2003) A cautionary note on exact unconditional inference for a difference between two independent binomial proportions

    Biometrics 59:441–450.

    https://doi.org/10.1111/1541-0420.00051

    • PubMed
    • Google Scholar
32. 1. Melcher D

    (2010) The missing link for attention pointers: comment on Cavanagh et al

    Trends in Cognitive Sciences 14:473–475.

    https://doi.org/10.1016/j.tics.2010.08.007

    • PubMed
    • Google Scholar
33. 1. Montagnini A
    2. Castet E

    (2007) Spatiotemporal dynamics of visual attention during saccade preparation: Independence and coupling between attention and movement planning

    Journal of Vision 7:1–16.

    https://doi.org/10.1167/7.14.8

    • PubMed
    • Google Scholar
34. 1. Nyström M
    2. Hooge I
    3. Holmqvist K

    (2013) Post-saccadic oscillations in eye movement data recorded with pupil-based eye trackers reflect motion of the pupil inside the iris

    Vision Research 92:59–66.

    https://doi.org/10.1016/j.visres.2013.09.009

    • PubMed
    • Google Scholar
35. 1. Pierrot-Deseilligny C
    2. Ploner CJ
    3. Muri RM
    4. Gaymard B
    5. Rivaud-Pechoux S

    (2002) Effects of cortical lesions on saccadic: eye movements in humans

    Annals of the New York Academy of Sciences 956:216–229.

    https://doi.org/10.1111/j.1749-6632.2002.tb02821.x

    • PubMed
    • Google Scholar
36. Software

    1. R Core Team

    (2016) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.R-project.org/
37. 1. Rolfs M
    2. Jonikaitis D
    3. Deubel H
    4. Cavanagh P

    (2011) Predictive remapping of attention across eye movements

    Nature Neuroscience 14:252–256.

    https://doi.org/10.1038/nn.2711

    • PubMed
    • Google Scholar
38. 1. Rolfs M
    2. Szinte M

    (2016) Remapping attention pointers: linking physiology and behavior

    Trends in Cognitive Sciences 20:399–401.

    https://doi.org/10.1016/j.tics.2016.04.003

    • PubMed
    • Google Scholar
39. 1. Shelhamer M
    2. Joiner WM

    (2003) Saccades exhibit abrupt transition between reactive and predictive; predictive saccade sequences have long-term correlations

    Journal of Neurophysiology 90:2763–2769.

    https://doi.org/10.1152/jn.00478.2003

    • PubMed
    • Google Scholar
40. 1. Szinte M
    2. Carrasco M
    3. Cavanagh P
    4. Rolfs M

    (2015) Attentional trade-offs maintain the tracking of moving objects across saccades

    Journal of Neurophysiology 113:2220–2231.

    https://doi.org/10.1152/jn.00966.2014

    • PubMed
    • Google Scholar
41. 1. Wurtz RH

    (2008) Neuronal mechanisms of visual stability

    Vision Research 48:2070–2089.

    https://doi.org/10.1016/j.visres.2008.03.021

    • PubMed
    • Google Scholar
42. 1. Yao T
    2. Treue S
    3. Krishna BS

    (2016) An attention-sensitive memory trace in macaque MT following saccadic eye movements

    PLoS Biology 14:e1002390.

    https://doi.org/10.1371/journal.pbio.1002390

    • PubMed
    • Google Scholar

Author details

1. Tao Yao

   Cognitive Neuroscience Laboratory, German Primate Center, Goettingen, Germany

   Contribution

   TY, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   taoyao@dpz.eu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-2060-690X

2. Madhura Ketkar

   1. Cognitive Neuroscience Laboratory, German Primate Center, Goettingen, Germany
   2. European Neuroscience Institute, Goettingen, Germany

   Contribution

   MK, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

3. Stefan Treue

   1. Cognitive Neuroscience Laboratory, German Primate Center, Goettingen, Germany
   2. Bernstein Center for Computational Neuroscience, Goettingen, Germany
   3. Faculty of Biology and Psychology, Goettingen University, Goettingen, Germany

   Contribution

   ST, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

4. B Suresh Krishna

   Cognitive Neuroscience Laboratory, German Primate Center, Goettingen, Germany

   Contribution

   BSK, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   skrishna@dpz.eu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0383-054X

• 1,685

  views

• 288

  downloads

• 18

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.