Article | Open | Published:

# Remapping high-capacity, pre-attentive, fragile sensory memory

## Abstract

Humans typically make several saccades per second. This provides a challenge for the visual system as locations are largely coded in retinotopic (eye-centered) coordinates. Spatial remapping, the updating of retinotopic location coordinates of items in visuospatial memory, is typically assumed to be limited to robust, capacity-limited and attention-demanding working memory (WM). Are pre-attentive, maskable, sensory memory representations (e.g. fragile memory, FM) also remapped? We directly compared trans-saccadic WM (tWM) and trans-saccadic FM (tFM) in a retro-cue change-detection paradigm. Participants memorized oriented rectangles, made a saccade and reported whether they saw a change in a subsequent display. On some trials a retro-cue indicated the to-be-tested item prior to probe onset. This allowed sensory memory items to be included in the memory capacity estimate. The observed retro-cue benefit demonstrates a tFM capacity considerably above tWM. This provides evidence that some, if not all sensory memory was remapped to spatiotopic (world-centered, task-relevant) coordinates. In a second experiment, we show backward masks to be effective in retinotopic as well as spatiotopic coordinates, demonstrating that FM was indeed remapped to world-centered coordinates. Together this provides conclusive evidence that trans-saccadic spatial remapping is not limited to higher-level WM processes but also occurs for sensory memory representations.

## Introduction

To only process visual information while it is available to the eyes would be a fatal disadvantage for dynamic agents in a dynamic world. A memory buffer allows us to access visual information after termination of its retinal input (visuospatial short-term memory; VSTM). Aristotle already mentioned the phenomenon of visible persistence in 3rd century B.C.1, and the scientific study of VSTM goes back to at least Fechner2 and Helmholtz3. It continues to captivate researchers to this day4. The discrepancy between the rich subjective experience of the world and the scientific evidence for the limits of perception is especially puzzling5.

## Methods

### Participants

All participants reported normal or corrected to normal visual acuity and gave informed consent. 8 Utrecht University students (aged 20–26, 6 female) participated in Experiment 1 and 21 students (aged 19–41, 15 female) participated in Experiment 2 for monetary reward. The study was approved by the Ethics Committee of the Faculty of Social and Behavioral Sciences of Utrecht University and has been carried out in accordance with the Declaration of Helsinki.

### Materials

Stimuli were displayed in a dark room on an ASUS PG278q 27′′ LCD monitor with a display area of 60 × 34 cm (49.6 × 29.3 dva; degrees visual angle) and a resolution of 2560 × 1440 px at a refresh rate of 100 Hz and response time of 1ms. Monocular eye movements were recorded by an Eyelink1000 eye tracker (SR Research Ltd, Canada) at a temporal resolution of 1000 Hz and a maximal spatial resolution of 0.01 dva. Participants were seated on an adjustable chair and placed their head on a chinrest 65 cm in front of the screen. The experiment was designed in Matlab 2015a and Psychtoolbox 329,30.

### Stimuli and procedure

#### Experiment 1

Following verbal and on-screen instructions, participants completed the task with short breaks, approximately every 10–15 minutes. The eye tracker was re-calibrated at the beginning of the session and after each break. To ensure that the stimuli elicited no retinal afterimages we calibrated the gray value of the screen background to be perceptually isoluminant to the red (2.71 cd/m2, x = 0.646, y = 0.338) stimuli for every participant. This was done by means of heterochromatic flicker photometry31.

Each trial began with three dots (0.22 dva) that always remained visible. The central fixation dot was flanked by two dark gray dots at a horizontal distance of 6.2 dva and remained blue until participants pressed the space bar to begin a trial, upon which the central dot turned red. The trial layout is illustrated in Fig. 2. A memory array consisting of eight red rectangles in one of four orientations (0.25 × 0.9 dva, 2.5 dva from central fixation) was presented for 500 ms (Fig. 2). On half the trials, after a blank interval of 100 ms (only the three fixation dots were visible), the red fixation dot jumped to the left or the right with the other two dots displayed in dark gray. On the other half of the trials the central dot remained red. Participants were required to keep their gaze at the red dot and to move their eyes immediately when it jumped to a new location. When their gaze deviated 2.5 dva from the red dot the trial was aborted and repeated at the end of the block. Participants subsequently pressed the space bar to go to the next trial. On half the trials, after 1000 ms to allow for both saccade execution and remapping, a valid retro-cue appeared as a red line (0.05 dva in width) from central fixation extending 1.44 dva in the direction of one of the memory items. After another blank of 1000 ms the probe array appeared again with either no change or with one of its items rotated 90°. This was always the cued item. Participants were instructed to indicate whether or not they noticed a change in the cued item by pressing the up/down arrow keys. After each trial participants received auditory feedback. The time delay between memory array offset and retro-cue (the delay interval at which FM capacity is probed) was 1100 ms. On the other half of trials, the cue appeared together with the probe array (post-cue). This produced four conditions: post-cue with and without intervening saccade (i.e., probing WM), retro-cue with and without intervening saccade (i.e., probing FM). Crucially, the cue in the retro-cue condition appeared at the same time as the probe array in the post-cue condition, ensuring that WM and FM capacities could be adequately compared. Change/no-change as well as left/right saccade cue were balanced between conditions.

### Data Analysis

Based on Cowan’s k 7 we estimated memory capacity as:

$k=[2×accuracy−1]×memorysetsize$
(1)

It should be noted that the present study is agnostic with regard to working memory resources being allocated according to a discrete slot32 or continuous resource33 model, a subject of ongoing debate. Cowan’s k here can be seen as a continuous measure of capacity that is independent of set size. The term items in the present article therefore simply refers to a point on the Cowan’s k scale. The essential metric is the difference in capacity between conditions within participants, rather than an absolute estimate of capacity limits in terms of number of discrete items.

#### Experiment 1

Sessions lasted 1.5 h in which participants performed 470 experimental trials on average, about 118 per condition. Participants practiced the memory task without the saccade condition for 1.5 h on the previous day.

Previous studies investigating FM have found that about one quarter of participants require excessive training or are unable to learn the task at all. It should be noted that this does not necessarily reflect individual differences in FM but more likely a failure to use the retro-cue, which requires participants to translate the exogenous retro-cue into an endogenous shift of attention. Since the focus of the present study was not within-fixation FM, two participants who did not supersede the pre-determined accuracy threshold of 85% in the within-fixation FM condition at the end of the training session were excluded from further participating in the experiment. This ensured reliable data for addressing the research question at hand: trans-saccadic FM.

#### Experiment 2

Sessions lasted 2 h, during which participants performed 530 experimental trials on average, about 88 per condition. Participants practiced the task without masks for 2 h on the previous day. In line with Experiment 1, 7 participants were excluded during the training session if they did not reach 85% accuracy in the within-fixation FM condition after three blocks.

### Statistical analysis

Bayesian analysis allows the use of a stopping rule34. Data was collected until a preliminary analysis via a directional Bayesian t-test (JASP Team, 2017; JASP Version 0.8.3.1) comparing the relevant conditions (tWM, tFM) reached BF+0 > 6 or BF+0 < 1/6 (Experiment 1), or after two months of data collection (Experiment 2). A one-sided test rather than a two-sided test was used since there was a strong expectation that tFM is larger than tWM. Subsequent analyses were performed in Stan35 using R36 and the R package brms37. The model was a multilevel logistic regression using random intercepts to control for individual baseline differences with model equation

$logit ( r e s p o n s e i j ) = b 0 + u i + ∑ c o n = 1 n c o n b c o n x c o n , j$
(2)

where $respons e i j$ is the predicted binary response (success) of participant $i$ to trial $j$, $b 0$ is an intercept, which coincides with the mean log odds for the reference category, $u i$ is the random intercept for participant $i$, $n c o n$ is the total number of non-reference conditions, $b c o n$ is a difference parameter for condition con and $x c o n , j$ is a dummy variable indicating whether trial $j$ belongs to condition con.

Priors were set to be weakly informative for the first experiment. From previous research it is clear that participants will almost certainly do better than chance and will almost certainly not achieve perfect scores, which is reflected in the priors. The second experiment used the same random intercept model with different conditions. For this experiment, more informative priors were set based on the knowledge obtained in the first experiment. Priors are described in detail in the supplementary material.

We employed the Bayesian hypothesis testing framework using Bayes factors38. For all hypothesis tests we first assessed the equality of two parameters, and subsequently tested for the direction of the effect39,40. The equality hypothesis tests (H0: conditions are equal; H1: conditions are different) were computed in brms by the Savage-Dickey method41. The directed hypothesis tests are based on the ratio between the proportion of the posterior that is in agreement with a hypothesis and its complement. In other words, the ratio between the probability density mass of a difference between two conditions above and below zero42,43,44. Posterior model probabilities were computed, for which prior odds for the hypothesis pairs under comparison were set to 1 to reflect equal weights of the hypotheses a priori. Analysis scripts and raw data are available via the Open Science Framework at https://osf.io/ye9ya/.

## Results

### Experiment 1

In the first experiment, we demonstrate a retro-cue benefit irrespective of whether observers made a saccade. This provides strong evidence that FM items were remapped across saccades.

#### Higher capacity in the retro-cue condition

Accuracy scores for tFM were higher than tWM scores by 9.5 percentage points on average (0.48 ± 0.1 (mean ± standard deviation) on the log-odds scale). This corresponds to an increase in memory capacity by k = 1.5 items. The Bayes Factor in favor of unequal tFM and tWM was 5530 with a posterior model probability of ≈1. The directed hypothesis test indicated that tFM capacity is almost certainly larger than tWM capacity (posterior model probability ≈1).

Accuracy scores in the FM condition (within fixation) were 10.8 percentage points higher than WM scores (0.69 ± 0.12 on the log-odds scale). This corresponds to an increase in memory capacity by k = 1.7 items. The Bayes Factor in favor of unequal FM and WM was 26982 with a posterior model probability of $≈1$. The directed hypothesis test indicated that FM capacity is almost certainly larger than WM capacity (posterior model probability ≈1). Comparing tFM with FM capacity suggests that on average 1.5 of 1.7 sensory memory items were remapped.

#### Summary

We provide strong evidence that in addition to stable memory items some, if not all, fragile memory items have also been remapped. Individual data is shown in Fig. 3 (left panel). The posterior probability density distributions are shown in Fig. 3 (right panel). These distributions represent our knowledge about possible values of the average proportion correct in each condition. Plots displaying the hypothesis tests graphically are provided in the supplementary material.

### Experiment 2

In the second experiment, we selectively masked locations to disrupt FM representations in order to assess where in space and time FM exists across saccades. The informative priors employed in this section are discussed in the supplementary materials. The results show that masks at the retinotopic location as well as at the spatiotopic location reduced the retro-cue benefit. This confirms that FM items have been remapped to task-relevant, world-centered coordinates.

#### Higher capacity in the retro-cue condition

First, we replicated the main finding of Experiment 1. On average, tFM accuracy scores were 10.1 percentage points higher than tWM scores (0.51 ± 0.07 on the log-odds scale). The Bayes Factor in favor of unequal tFM and tWM was 28408, with a posterior model probability of ≈1. The directed hypothesis test again indicated that tFM capacity is almost certainly larger than tWM capacity with a Bayes Factor approaching infinity and posterior model probability ≈1.

#### No difference between early and late masks

For retinotopic masks, the Bayes Factor was 4.3 in favor of no difference between early and late mask presentation, with a posterior model probability of 0.811. Retinotopic early masks were 1.5 percentage points more effective than retinotopic late masks. For spatiotopic masks, the Bayes Factor was 7.5 in favor of no difference between early and late mask presentation, with a posterior model probability of 0.883. Spatiotopic late masks were 0.3 percentage points more effective than spatiotopic early masks. Following this evidence for no difference, early and late mask data were pooled for retinotopic and spatiotopic mask conditions respectively.

#### Retinotopic and spatiotopic masks are effective

On average, accuracy scores in the retinotopic mask condition were 4.8 percentage points lower than tFM scores (0.25 ± 0.06 on the log-odds scale). The Bayes Factor in favor of unequal tFM and retinotopic mask conditions was 600, with a posterior model probability of 0.998. The directed hypothesis test indicated that retinotopic masks almost certainly were effective with a Bayes Factor approaching infinity and posterior model probability ≈1.

On average, accuracy scores in the spatiotopic mask condition were 3.6 percentage points lower than tFM scores (0.19 ± 0.06 on the log-odds scale). The Bayes Factor in favor of unequal tFM and spatiotopic mask conditions was 16.84, with a posterior model probability of 0.944. The directed hypothesis test indicated that spatiotopic masks almost certainly were effective with a Bayes Factor of 1999 and a posterior model probability ≈1.

#### Summary

We confirm that (1) tFM capacity is larger than tWM capacity and (2) FM is remapped to spatiotopic coordinates. Individual data is shown in Fig. 4 (left panel). The posterior probability densities, which represent our knowledge about possible values for the parameters and display the effects found graphically, are shown in Fig. 4 (right panel). Plots displaying the hypothesis tests graphically are provided in the supplementary material.

## Discussion

We investigated trans-saccadic visuospatial short-term memory (tVSTM) in a retro-cue change-detection paradigm and observed remapping of both robust and fragile memory traces. In addition to items that received dedicated attentional resources (robust WM), some items in unstable, pre-attentive FM were also remapped. Our results challenge the strongly held beliefs that (1) remapping always requires attentional resources, and (2) that tVSTM contains only WM items and thus is synonymous with tWM.

### The capacity of trans-saccadic VSTM

We observed a trans-saccadic sensory memory capacity well above that of tWM. This is a clear indication that some, if not all, FM items have been remapped. Due to individual differences in WM capacity it is crucial to compare capacities within-subject and within-task, that is, WM after a saccade with FM after a saccade, rather than to establish global capacity estimates such as the “magic number four”. With an appropriate baseline condition (tWM) any capacity increase observed for tFM must reflect items that were stored as FM but remained accessible in task-relevant coordinates after the saccade (see also Fig. 1). If only stable items survived saccades, then no retro-cue benefit should have been observed because there would not have been any fragile memory traces left for the retro-cue to rescue.

In our view, previous studies23,24 have failed to observe remapping of sensory memory because the employed paradigms were not sensitive enough to pick up these fragile memory traces across saccades. Irwin23 used a post-cue delayed-recall procedure and showed stimuli only once, requiring participants to begin recalling the test item from the set of memorized stimuli immediately after the post-cue. The intrusion-errors Irwin reported in Table 4 are much more pronounced for the saccade condition, which indicates difficulties to localize the test item in memory and explains the reduced performance after saccades. While Prime et al.24 used a change-detection task, they did not include a retro-cue. Sensory memory can’t be directly reported but must first be transferred to stable WM. The retro-cue change-detection task employed in the present study enabled participants to first stabilize a remapped fragile memory trace and to then compare it with the visible test item at the same spatiotopic location.

### Pre-attentive spatial remapping and the functional role of sensory memory

Traditionally, trans-saccadic memory was believed to be comprised of WM items only since the spatial remapping process is thought to strongly depend on attention. Attention here refers to the allocation of a limited cognitive resource to a small set of objects. Items in WM have been prioritized by attentional mechanisms, while items in FM may have received no attention at all or sufficiently fewer resources to remain in a sensory memory state, which is easily masked by visual interference16,17.

Especially the failure to detect a role for sensory memory in trans-saccadic perception has led this memory form to be dismissed as an artefact of visual processing48. By demonstrating remapping of sensory memory items that have not received dedicated cognitive resources, we confirm that sensory memory is not merely an artefact, but an object-based form of memory that is maintained and updated across eye movements. This suggests potential functional roles for sensory memory. One possibility is that trans-saccadic perception relies on a highly detailed pre-categorical representation of the world. It is conceivable that remapping an entire scene across eye movements provides a reference frame for trans-saccadic integration and a computationally efficient way to localize the most relevant objects in memory (WM) within this frame. In that case, sensory memory would support trans-saccadic visual stability, one of the great puzzles of visual neuroscience49.

An important question concerns whether FM items (defined as items that can only be reported in a retro-cue paradigm) depend on qualitatively different resources (i.e., a different memory store) or on quantitatively different resources (i.e., receiving less attention) than WM items. We interpret our results within a framework that assumes the former possibility (FM and WM rely on a separate cognitive resource). Two aspects would be necessary to substantiate this framework: (1) a differential effect of withdrawal of attention on WM and FM. (2) Separate neural substrates of the two memory forms.

(2) Sligte, Wokke, Tesselaar, Scholte and Lamme50 discovered that TMS pulses to the right DLPFC strongly affected measures of WM but not FM. Sligte et al. were asking the question whether FM is a weak form of WM (items that received less attention); or whether FM is independent of the attentional boosting that WM relies on. Their results support the hypothesis that FM does not depend on the wide-spread network activity associated with the attentional boosting observed in WM, but relies at least partially on a different neurological architecture.

Taken together these results strongly suggests that FM does not depend on attention in the way WM does and that FM items received little to no attention during encoding. Irrespective of whether or not the FM interpretation of the retro-cue benefit effect holds, the present results at the very least demonstrate that items that exist in a lower priority state (fewer cognitive resources, less attentional prioritization, high maskability) are remapped in addition to those items that were prioritized and received most resources.

## Conclusion

In summary, the present study provides compelling evidence for spatial remapping of items in pre-attentive, high-capacity, fragile sensory memory across eye movements. This contradicts previous studies that confine remapping to robust, capacity-limited WM and has implications for the role of attention in spatial remapping.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## References

1. 1.

Allen, F. The persistence of vision. Am. J. Physiol. Opt (1926).

2. 2.

Fechner, G. T. Ueber die subjectiven Nachbilder und Nebenbilder. Ann. Phys. 126(6), 193–221 (1840).

3. 3.

Helmholtz, H. V. Concerning the perceptions in general. Treatise Physiol. Opt. 3 (1866).

4. 4.

Luck, S. J. & Vogel, E. K. Visual working memory capacity: From psychophysics and neurobiology to individual differences. Trends Cogn. Sci. 17, 391–400 (2013).

5. 5.

Cohen, M. A., Dennett, D. C. & Kanwisher, N. What is the Bandwidth of Perceptual Experience? Trends Cogn. Sci. 20, 324–335 (2016).

6. 6.

Pinto, Y., Sligte, I. G., Shapiro, K. L. & Lamme, V. a. Fragile visual short-term memory is an object-based and location-specific store. Psychon. Bull. Rev. 20, 732–739 (2013).

7. 7.

Cowan, N. The magical number 4 in short term memory. A reconsideration of storage capacity. Behav. Brain Sci. 24, 87–186 (2001).

8. 8.

LaBar, K. S., Gitelman, D. R., Parrish, T. B. & Mesulam, M. Neuroanatomic overlap of working memory and spatial attention networks: a functional MRI comparison within subjects. Neuroimage 10, 695–704 (1999).

9. 9.

Awh, E., Jonides, J. & Awh, E. Overlapping mechanisms of attention and spatial working memory 5, 119–126 (2001).

10. 10.

Sperling, G. The information available in brief visual presentations. Psychol. Monogr. 74, 1–29 (1960).

11. 11.

Landman, R., Spekreijse, H. & Lamme, V. A. F. Large capacity storage of integrated objects before change blindness. Vision Res. 43, 149–164 (2003).

12. 12.

Griffin, I. C. & Nobre, A. C. Orienting attention to locations in internal representations. J. Cogn. Neurosci. 15, 1176–1194 (2003).

13. 13.

Neisser, U. Cognitive Psychology. (Appleton-Century-Crofts., 1967).

14. 14.

Souza, A. S. & Oberauer, K. In search of the focus of attention in working memory: 13 years of the retro-cue effect. Attention, Perception, Psychophys. 1–22, https://doi.org/10.3758/s13414-016-1108-5 (2016).

15. 15.

Sligte, I. G., Scholte, H. S. & Lamme, V. A. F. Are there multiple visual short-term memory stores? PLoS One 3, 2–10 (2008).

16. 16.

Vandenbroucke, A. R. E., Sligte, I. G. & Lamme, V. A. F. Manipulations of attention dissociate fragile visual short-term memory from visual working memory. Neuropsychologia 49, 1559–1568 (2011).

17. 17.

Pinto, Y. et al. Conscious Visual Memory With Minimal Attention. 146, 214–226 (2016).

18. 18.

Matsukura, M. & Hollingworth, A. Does visual short-term memory have a high-capacity stage? Psychon. Bull. Rev. 18, 1098–1104 (2011).

19. 19.

Bays, P. M. & Husain, M. Spatial remapping of the visual world across saccades. Neuroreport 18, 1207–1213 (2007).

20. 20.

Irwin, D. E. & Gordon, R. D. Eye Movements, Attention and Trans-saccadic Memory. Vis. cogn. 5, 127–155 (1998).

21. 21.

Cavanagh, P., Hunt, A. R., Afraz, A. & Rolfs, M. Visual stability based on remapping of attention pointers. Trends Cogn. Sci. 14, 147–53 (2010).

22. 22.

Jonikaitis, D., Szinte, M., Rolfs, M. & Cavanagh, P. Allocation of attention across saccades. J. Neurophysiol. 109, 1425–34 (2013).

23. 23.

Irwin, D. E. Memory for position and identity across eye movements. Journal Of Experimental Psychology-Learning Memory And Cognition 18, 307–317 (1992).

24. 24.

Prime, S. L., Tsotsos, L., Keith, G. P. & Crawford, J. D. Visual memory capacity in transsaccadic integration. Exp. Brain Res. 180, 609–28 (2007).

25. 25.

Deubel, H., Schneider, W. X. & Bridgeman, B. Postsaccadic target blanking prevents saccadic suppression of image displacement. Vision Res. 36, 985–996 (1996).

26. 26.

McRae, K., Butler, B. E. & Popiel, S. J. Spatiotopic and retinotopic components of iconic memory. Psychol. Res. 49, 221–227 (1987).

27. 27.

Germeys, F., De Graef, P., Van Eccelpoel, C. & Verfaillie, K. The visual analog: evidence for a preattentive representation across saccades. J. Vis. 10, 9 (2010).

28. 28.

Schut, M. J., Van der Stoep, N., Postma, A. & Van der Stigchel, S. The cost of making an eye movement: A direct link between visual working memory and saccade execution. J. Vis. 17, 15 (2017).

29. 29.

Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).

30. 30.

Kleiner, M. et al. What’s new in Psychtoolbox-3? Perception 36, S14 (2007).

31. 31.

Kaiser, P. K. & Comerford, J. P. Flicker photometry of equally bright lights. Vision Res. 15, 1399–1402 (1975).

32. 32.

Zhang, W. & Luck, S. J. Discrete fixed-resolution representations in visual working memory. Nature 453, 233–U13 (2008).

33. 33.

Bays, P. M. & Husain, M. Dynamic shifts of limited working memory resources in human vision. Science 321, 851–4 (2008).

34. 34.

Rouder, J. N. Optional stopping: No problem for Bayesians. 301–308, https://doi.org/10.3758/s13423-014-0595-4 (2014).

35. 35.

Carpenter, B. et al. Journal of Statistical Software Stan: A Probabilistic Programming Language. J. Stat. Softw. VV (2016).

36. 36.

Ihaka, R. & Gentleman, R. Interface Foundation of America R: A Language for Data Analysis and Graphics R: A Language for Data Analysis and Graphics. Source J. Comput. Graph. Stat. 5, 299–314 (1996).

37. 37.

Bürkner, P.-C. brms: An R Package for Bayesian Generalized Linear Mixed Models using Stan. Journal of Statistical Software, 80(1), 1–28, https://doi.org/10.18637/jss.v080.i01 (2015).

38. 38.

Kass, R. E. & Raftery, A. E. Bayes Factors. J. Am. Stat. Assoc. 90, 773–795 (1995).

39. 39.

Marsman, M. & Wagenmakers, E. Three Insights from a Bayesian Interpretation of the One-Sided P Value. 1–11, https://doi.org/10.1177/0013164416669201 (2016)

40. 40.

Johnson, D. The insignificance of statistical significance testing. J. Wildl. Manage. 63, 763–772 (1999).

41. 41.

Wagenmakers, E. J., Lodewyckx, T., Kuriyal, H. & Grasman, R. Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cogn. Psychol 60, 158–189 (2010).

42. 42.

Klugkist, I., Laudy, O. & Hoijtink, H. Inequality constrained analysis of variance: a Bayesian approach. Psychol. Methods 10, 477–93 (2005).

43. 43.

Klugkist, I., Kato, B. & Hoijtink, H. Bayesian model selection using encompassing priors. Stat. Neerl. 59, 57–69 (2005).

44. 44.

Wetzels, R., Grasman, R. P. & Wagenmakers, E. J. An encompassing prior generalization of the Savage-Dickey density ratio. Comput. Stat. Data Anal. 54, 2094–2102 (2010).

45. 45.

Golomb, J. D., Chun, M. M. & Mazer, J. A. The Native Coordinate System of Spatial Attention Is Retinotopic. J. Neurosci. 28, 10654–62 (2008).

46. 46.

Zimmermann, E., Morrone, M. C., Fink, G. R. & Burr, D. Spatiotopic neural representations develop slowly across saccades. Curr. Biol. 23, R193–R194 (2013).

47. 47.

Fabius, J. H. & Fracasso, A. & Van der Stigchel, S. Spatiotopic updating facilitates perception immediately after saccades. Sci. Rep. 6, 34488 (2016).

48. 48.

Haber, R. N. The impending demise of the icon: A critique of the concept of iconic storage in visual information processing. Behav. Brain Sci. 6, 1 (1983).

49. 49.

Melcher, D. & Colby, C. L. Trans-saccadic perception. Trends Cogn. Sci. 12, 466–73 (2008).

50. 50.

Sligte, I. G., Wokke, M. E., Tesselaar, J. P., Steven Scholte, H. & Lamme, V. A. F. Magnetic stimulation of the dorsolateral prefrontal cortex dissociates fragile visual short-term memory from visual working memory. Neuropsychologia 49, 1578–1588 (2011).

## Acknowledgements

This research was funded by a VIDI Grant 45213008 from the Netherlands Organization for Scientific research to Stefan Van der Stigchel.

## Author information

### Affiliations

1. #### Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands

• Paul Zerr
• , Surya Gayet
•  & Stefan Van der Stigchel
2. #### Methodology and Statistics, Utrecht University, Utrecht, The Netherlands

• Kees Mulder
3. #### Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands

• Yaïr Pinto
•  & Ilja Sligte

### Contributions

P. Zerr developed the study concept, programmed the experiments, collected and analyzed the data and wrote the manuscript. All authors provided critical revisions. S. Van der Stigchel, I. Sligte, Y. Pinto and S. Gayet provided key insights and very helpful discussions regarding the study design and contributed to the development of the paradigm. K. Mulder provided key insights into Bayesian analysis and crucial contributions to the results section. S. Van der Stigchel also provided project funding. All authors approved the final version of the manuscript for submission.

### Competing Interests

The authors declare that they have no competing interests.

### Corresponding author

Correspondence to Paul Zerr.

## Electronic supplementary material

### DOI

https://doi.org/10.1038/s41598-017-16156-0