Working memory is a fundamental cognitive function that enables us to hold onto past sensory information in service of upcoming future behaviour (D’Esposito and Postle, 2015; van Ede and Nobre, 2023; Rainer et al., 1999). Accordingly, by its very nature, working memory is concerned with two components: the past and the future.

In conventional laboratory tasks, past and future components are often conflated such as when sensory information in working memory is tested at the same location as where it was encoded. By contrast, in the dynamic situations we face every day, sensory information often disappears at one specific location (where it enters visual working memory) but becomes relevant at another location (as also in Brincat et al., 2021; Doherty et al., 2005; Woodman et al., 2012; Kahneman et al., 1992; Zaksas et al., 2001). Imagine trying to capture a photograph of a precious bird species that you just saw disappear behind a building. Your working memory of the bird is likely to consider not only where you last saw the bird, but also where you expect it to re-appear to capture it on camera. Such situations raise an interesting, underexplored question: when past and future locations of memory contents are not the same, does the brain code internal representations with regard to past, future, or both?

To address this question, we developed a task in which we dissociated the encoding (past) and to-be-tested (future) locations associated with visual representations in working memory. This enabled us to experimentally isolate past and future memory attributes and to track their respective utilisation through spatial biases in gaze behaviour in healthy human volunteers.

Twenty-five human volunteers performed a working-memory task in which visual memory items were encoded and tested at different locations (Figure 1a). Participants memorised two coloured gratings with different orientations presented either vertically or horizontally. The crucial manipulation was that we always tested memory content in the orthogonal axis and at a predictable location (depending on the future rule that we varied across sessions; see Figure 1—figure supplement 1 for the four possible rules).

Figure 1 with 4 supplements see all

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After a delay period, we cued the relevant memory item by changing the colour of the central fixation dot. At this stage, participants were required to select the colour-matching grating from working memory in order to compare it to the upcoming test stimulus (clockwise/counter-clockwise judgement). Crucially, we always presented two stimuli at the test-phase of which only one was relevant, as determined by the future rule. For example, in Figure 1a, after the green memory item is cued, the relevant test stimulus will be the right stimulus, given the future rule (top item tested on right) in this session. Note how the colour cue only ever informed the to-be-tested memory content but never directly informed the to-be-tested future location. The to-be-tested location was an attribute of the cued memory content but not of the cue itself.

Participants were able to perform this dynamic visual working-memory task, with an average accuracy of 70±2percent correct (mean ± SEM) and an average reaction time of 1218±125 ms.

Individual saccades reveal truly joint consideration of past and future memory attributes

In principle, the observed joint activation of the past and future locations associated with the cued memory content in the trial-averaged and participant-averaged data could result from two alternative scenarios with different interpretations. First, either the past or the future alone may be considered in different trials and/or participants, without past and future memory attributes ever being considered together. Alternatively, participants may truly consider both past and future memory attributes jointly at the single-trial, single-saccade level.

While it is notoriously hard to disentangle the single-trial interpretation of averaged data (Stokes and Spaak, 2016), we were here able to do so by interrogating the individual saccade characteristics for which the two alternative scenarios make different predictions. In the first scenario, saccades should be biased to either the past or the future location, but there should be no dependency between them (i.e., a saccade may be biased to the past location regardless of the future location, and vice versa). In contrast, in the second scenario, with truly joint consideration of the past and future, there should be a clear dependency: it should be those saccades that are biased to the past that are also biased to the future. In other words, the future-biased saccades should predominantly be driven by the past-biased saccades, and vice versa.

To disentangle these alternatives at the single-trial level of individual saccades, we focused on the first saccades after the cue, in the 200–600 ms window. We previously identified these windows as the relevant window for microsaccade biases by internal selective attention (Liu et al., 2022a), and this is also where we observed the overlapping past and future biases in the average here (see Figure 1d). To facilitate visualisation and quantification, we rotated all detected saccades to match a consistent coordinate frame, in which the past location is represented horizontally (right = towards, left = away), and the future location vertically (top = toward, bottom = away; see Figure 2a).

Figure 2 with 1 supplement see all

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As shown in Figure 2a, the majority of first-detected microsaccades in our window of interest were made toward the past (right >left) and toward the future (top >bottom), replicating our prior analyses. Critically, this visualisation and quantification enabled us to disentangle the two alternatives sketched above in which past and future saccades at the single-trial (individual-saccade) level were either independent (past bias regardless of future and future bias regardless of past) or dependent (joint past and future bias). Our data supported the latter.

Figure 2b shows the bias toward the past as a function of saccade direction with regard to the future. Likewise, Figure 2c, shows the future bias as a function of whether saccades were also biased to the past. In both cases, we see a clear dependency: the past bias is particularly pronounced for saccades that also have a future bias (Figure 2b) and, vice versa, the future bias is most pronounced for saccades that also have a past bias (Figure 2c). This is perhaps best appreciated by the binarised quantification of these same data in Figure 2d: showing the percentage of identified first saccades toward or away from the future location, as a function of whether the same saccades were also biased toward or away from the past location. We found a clear interaction (F(1, 24)=18.1, p<0.001, partial η²=0.43), whereby the bias toward the future location was exclusively observed for those saccades that were also biased toward the past location. Indeed, follow-up t-tests revealed no difference in future bias for saccades that were away from the past (t(24) = 1.13, P_Bonferroni=1, d=0.23), but a clear future effect for saccades that were toward the past (t(24)=4.65, P_Bonferroni<0.001, d=0.93). This would not be expected if single saccades cared exclusively about either the past or the future location. Instead, this provides single-trial-level support with the truly joint consideration of past and future memory attributes.

Here, we brought the study of visual working memory into a dynamic context by experimentally dissociating (orthogonalising) the past (last-seen location) and future (to-be-tested location) attributes of visual memory contents, and independently tracking the utilisation of these two attributes through the gaze. Doing so, we unveil a novel, fundamental property of working memory – the joint availability and utilisation (i.e. selection) of past and future memory attributes. As such, our data provide key support for the proposal that memory is fundamentally future-oriented (van Ede and Nobre, 2023; Rainer et al., 1999; Nobre and Stokes, 2019; Schacter et al., 2007), while also reminding us that past memory attributes are not forgotten, even when these do not become relevant again.

Our finding of joint utilisation of past and future memory attributes emerged from at least two alternative scenarios of how the brain may deal with dynamic everyday working memory demands in which memory content is encoded at one location but needed at another. First, memory contents could have directly been remapped (Brincat et al., 2021; de Vries and van Ede, 2024; Hayhoe and Ballard, 2005; Pelz and Canosa, 2001) to their future-relevant location. However, in this case, one may have expected to exclusively find a future-directed gaze bias, unlike what we observed. Moreover, using a complementary analysis of saccade directions along the axis of the future rule (de Vries and van Ede, 2024), we found no direct evidence for remapping in the period between encoding and cue (Figure 2—figure supplement 1). Second, when dealing with multiple memory contents, contents could be stored at the past location at first and the future location could be considered only after relevant memory content has been selected (in which case the past bias should have preceded the future bias). In contrast, our data suggest that the brain simultaneously retains the copy of both past and future-relevant locations in working memory, and (re)activates each during mnemonic selection. Thus, while it is not surprising that the future location is considered (Hayhoe and Ballard, 2005; Pelz and Canosa, 2001; Mennie et al., 2007), it is far less trivial that both past and future attributes would be retained and (re)activated together. This is our central contribution.

By capitalising on the discrete nature of saccades, we were able to demonstrate the truly joint consideration of past and future attributes, at the single-trial level. As such we were able to bypass a fundamental challenge of disentangling multiple potential single-trial interpretations when only having trial-average data available (for related discussions, see Stokes and Spaak, 2016; Jones, 2016; Nobre and van Ede, 2020). For example, when only considering the temporally overlapping past and future signals at the trial-average level, it was impossible to tell whether these joint effects resulted from a mix of trials and/or participants that relied on either the past or the future. By considering the discrete events – the individual saccades at the single-trial level – we could demonstrate how biases to the past and the future co-existed at the single-saccade level, supporting a truly joint consideration of past and future.

In the current study, we tracked spatial attention using microsaccades (Engbert and Kliegl, 2003; Hafed and Clark, 2002; Rolfs, 2009; Laubrock et al., 2010; Yuval-Greenberg et al., 2014; Shelchkova and Poletti, 2020; Fernández et al., 2023; Lowet et al., 2018; Liu et al., 2022a; van Ede et al., 2021; van Ede et al., 2019; van Ede et al., 2020). Compared to the commonly used online indicator of spatial attention, such as electrophysiological measures, microsaccades have important features that make them a promising complementary tool for uncovering the mechanisms of spatial attention (Rolfs, 2009; Engbert, 2006), including when directed internally as we have shown here. First, as we have discussed above, microsaccades are discrete events, allowing to track spatial attention at the single-trial level. Second, microsaccades are not limited to tracking spatial attention toward the left or right visual field, as electrophysiology indicators often are. These two aspects were paramount to our demonstration of joint single-trial consideration of past and future memory attributes, and are likely to open additional doors for future investigations.

While the past gaze bias that we report here replicates our own prior studies (Liu et al., 2022a; van Ede et al., 2021; van Ede et al., 2019; van Ede et al., 2020), here we for the first time demonstrate a similar bias to the future-relevant memory location that is similarly driven by microsaccades. This signal may reflect either of two situations: the selection of a future copy of the cued memory content or anticipatory attention to the anticipated location of its associated test-stimulus. Either way, by the nature of our experimental design, this future signal should be considered a content-specific memory attribute for two reasons. First, the two memory contents were always associated with opposite testing locations, hence the observed bias to the relevant future location must be attributed specifically to the cued memory content. Second, we cued which memory item would become tested based on its colour, but the to-be-tested location was dependent on the item’s encoding location, regardless of its colour. Hence, consideration of the item’s future-relevant location must have been mediated by selecting the memory item itself, as it could not have proceeded via cue colour directly. Accordingly, our data reveal how this future feature can be accessed from memory together with the specific memory content that it is associated with, implying joint storage and utilisation of past and future memory attributes.

Building on the above, at face value, our task may appear like a study that simply combines two established tasks: tasks using retro-cues to study attention in working memory (e.g. van Ede and Nobre, 2023; Griffin and Nobre, 2003; Panichello and Buschman, 2021; Souza and Oberauer, 2016) and tasks using pre-cues to study the orienting of spatial attention to an upcoming external stimulus (e.g. Griffin and Nobre, 2003; Panichello and Buschman, 2021; Posner, 1980; Worden et al., 2000; Schmidt et al., 2002). A critical difference with common pre-cue studies, however, is that the cue in our task never directly informed the relevant future location. Rather, as also stressed above, the future location was a feature of the cued memory item (according to the future rule), and not of the cue itself. Note how this type of scenario may not be uncommon in everyday life, such as in our opening example of a bird flying behind a building. Here too, the future relevant location is determined by the bird – i.e., the memory content – itself.

In our study, the past location of the memory items was technically irrelevant for the task and could thus, in principle, be dropped after encoding. One possibility is that participants remapped the two memory items to their future locations soon after encoding, and had started – but not finished – dropping the past location by the time the cue arrived. In such a scenario, the past signal is merely a residual trace of the memory items that serves no purpose but still pulls the gaze. Alternatively, however, the past locations may be utilised by the brain to help individuate/separate the two memory items. Moreover, by storing items with regard to multiple spatial frames (Draschkow et al., 2022) – here with regard to both past and future visual locations – it is conceivable that memories may become more robust to decay and/or interference. Also, while in our task past locations were never probed, in everyday life it may be useful to remember where you last saw something before it disappeared behind an occluder. In future work, it will prove interesting to systematically vary the delay between encoding and cue to assess whether the reliance on the past location gradually dissipates with time (consistent with dropping an irrelevant feature), or whether the past trace remains preserved despite longer delays (consistent with preserving utility for working memory).

Our data complement other recent studies investigating visual working memory in more dynamic contexts (Brincat et al., 2021; van Ede et al., 2021; Draschkow et al., 2022; Chung et al., 2022; de Vries et al., 2020; Xie et al., 2022), and showcase the rich nature of working memory representations. For example, akin to our finding of joint consideration of past and future locations in working memory, recent studies have uncovered the joint consideration of allocentric and egocentric spatial frames for working memory (Draschkow et al., 2022; Fiehler et al., 2014). While storing memory content at a single location (or with reference to a single frame) would appear more intuitive and efficient, our data reveal an intriguing alternative. We speculate that the joint retention of multiple spatial attributes may make memories more robust, as well as more flexible for serving continuously evolving demands during everyday behaviour.

Data and code availability. The corresponding data and code can be found at: https://osf.io/xukzs/ at OSF (https://doi.org/10.17605/OSF.IO/XUKZS).

1. 1. Liu B
   2. Alexopoulou Z
   3. van Ede F

   (2024) Open Science Framework

   Jointly looking to the past and the future in visual working memory.

   https://doi.org/10.17605/OSF.IO/XUKZS

1. 1. Brincat SL
   2. Donoghue JA
   3. Mahnke MK
   4. Kornblith S
   5. Lundqvist M
   6. Miller EK

   (2021) Interhemispheric transfer of working memories

   Neuron 109:1055–1066.

   https://doi.org/10.1016/j.neuron.2021.01.016

   • PubMed
   • Google Scholar
2. Preprint

   1. Chung YH
   2. Schurgin M
   3. Brady TF

   (2022) The role of motion in visual working memory for dynamic stimuli: More lagged but more precise representations of moving objects

   PsyArXiv.

   https://doi.org/10.31234/osf.io/cu3zg

   • Google Scholar
3. 1. D’Esposito M
   2. Postle BR

   (2015) The cognitive neuroscience of working memory

   Annual Review of Psychology 66:115–142.

   https://doi.org/10.1146/annurev-psych-010814-015031

   • PubMed
   • Google Scholar
4. 1. de Vries IEJ
   2. Slagter HA
   3. Olivers CNL

   (2020) Oscillatory control over representational states in working memory

   Trends in Cognitive Sciences 24:150–162.

   https://doi.org/10.1016/j.tics.2019.11.006

   • PubMed
   • Google Scholar
5. 1. de Vries E
   2. van Ede F

   (2024) Microsaccades track location-based object rehearsal in visual working memory

   eNeuro 11:ENEURO.0276-23.2023.

   https://doi.org/10.1523/ENEURO.0276-23.2023

   • PubMed
   • Google Scholar
6. 1. Doherty JR
   2. Rao A
   3. Mesulam MM
   4. Nobre AC

   (2005) Synergistic effect of combined temporal and spatial expectations on visual attention

   The Journal of Neuroscience 25:8259–8266.

   https://doi.org/10.1523/JNEUROSCI.1821-05.2005

   • PubMed
   • Google Scholar
7. 1. Draschkow D
   2. Nobre AC
   3. van Ede F

   (2022) Multiple spatial frames for immersive working memory

   Nature Human Behaviour 6:536–544.

   https://doi.org/10.1038/s41562-021-01245-y

   • PubMed
   • Google Scholar
8. 1. Engbert R
   2. Kliegl R

   (2003) Microsaccades uncover the orientation of covert attention

   Vision Research 43:1035–1045.

   https://doi.org/10.1016/s0042-6989(03)00084-1

   • PubMed
   • Google Scholar
9. 1. Engbert R

   (2006) Microsaccades: a microcosm for research on oculomotor control, attention, and visual perception

   Progress in Brain Research 154:177–192.

   https://doi.org/10.1016/S0079-6123(06)54009-9

   • PubMed
   • Google Scholar
10. 1. Fernández A
    2. Hanning NM
    3. Carrasco M

    (2023) Transcranial magnetic stimulation to frontal but not occipital cortex disrupts endogenous attention

    PNAS 120:e2219635120.

    https://doi.org/10.1073/pnas.2219635120

    • PubMed
    • Google Scholar
11. 1. Fiehler K
    2. Wolf C
    3. Klinghammer M
    4. Blohm G

    (2014) Integration of egocentric and allocentric information during memory-guided reaching to images of a natural environment

    Frontiers in Human Neuroscience 8:636.

    https://doi.org/10.3389/fnhum.2014.00636

    • PubMed
    • Google Scholar
12. 1. Griffin IC
    2. Nobre AC

    (2003) Orienting attention to locations in internal representations

    Journal of Cognitive Neuroscience 15:1176–1194.

    https://doi.org/10.1162/089892903322598139

    • PubMed
    • Google Scholar
13. 1. Hafed ZM
    2. Clark JJ

    (2002) Microsaccades as an overt measure of covert attention shifts

    Vision Research 42:2533–2545.

    https://doi.org/10.1016/s0042-6989(02)00263-8

    • PubMed
    • Google Scholar
14. 1. Hayhoe M
    2. Ballard D

    (2005) Eye movements in natural behavior

    Trends in Cognitive Sciences 9:188–194.

    https://doi.org/10.1016/j.tics.2005.02.009

    • PubMed
    • Google Scholar
15. 1. Jones SR

    (2016) When brain rhythms aren’t “rhythmic”: implication for their mechanisms and meaning

    Current Opinion in Neurobiology 40:72–80.

    https://doi.org/10.1016/j.conb.2016.06.010

    • PubMed
    • Google Scholar
16. 1. Kahneman D
    2. Treisman A
    3. Gibbs BJ

    (1992) The reviewing of object files: object-specific integration of information

    Cognitive Psychology 24:175–219.

    https://doi.org/10.1016/0010-0285(92)90007-o

    • PubMed
    • Google Scholar
17. 1. Laubrock J
    2. Kliegl R
    3. Rolfs M
    4. Engbert R

    (2010) When do microsaccades follow spatial attention?

    Attention, Perception, & Psychophysics 72:683–694.

    https://doi.org/10.3758/APP.72.3.683

    • Google Scholar
18. 1. Liu B
    2. Nobre AC
    3. van Ede F

    (2022a) Functional but not obligatory link between microsaccades and neural modulation by covert spatial attention

    Nature Communications 13:3503.

    https://doi.org/10.1038/s41467-022-31217-3

    • PubMed
    • Google Scholar
19. Preprint

    1. Liu B
    2. Nobre AC
    3. van Ede F

    (2022b) Microsaccades Transiently Lateralise EEG Alpha Activity

    bioRxiv.

    https://doi.org/10.1101/2022.09.02.506318

    • Google Scholar
20. 1. Lowet E
    2. Gomes B
    3. Srinivasan K
    4. Zhou H
    5. Schafer RJ
    6. Desimone R

    (2018) Enhanced neural processing by covert attention only during microsaccades directed toward the attended stimulus

    Neuron 99:207–214.

    https://doi.org/10.1016/j.neuron.2018.05.041

    • PubMed
    • Google Scholar
21. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
22. 1. Mennie N
    2. Hayhoe M
    3. Sullivan B

    (2007) Look-ahead fixations: anticipatory eye movements in natural tasks

    Experimental Brain Research 179:427–442.

    https://doi.org/10.1007/s00221-006-0804-0

    • PubMed
    • Google Scholar
23. 1. Nobre AC
    2. Stokes MG

    (2019) Premembering experience: a hierarchy of time-scales for proactive attention

    Neuron 104:132–146.

    https://doi.org/10.1016/j.neuron.2019.08.030

    • PubMed
    • Google Scholar
24. 1. Nobre AC
    2. van Ede F

    (2020) Under the mind’s hood: what we have learned by watching the brain at work

    The Journal of Neuroscience 40:89–100.

    https://doi.org/10.1523/JNEUROSCI.0742-19.2019

    • PubMed
    • Google Scholar
25. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) Fieldtrip: open source software for advanced analysis of meg, eeg, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:156869.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
26. 1. Panichello MF
    2. Buschman TJ

    (2021) Shared mechanisms underlie the control of working memory and attention

    Nature 592:601–605.

    https://doi.org/10.1038/s41586-021-03390-w

    • PubMed
    • Google Scholar
27. 1. Pelz JB
    2. Canosa R

    (2001) Oculomotor behavior and perceptual strategies in complex tasks

    Vision Research 41:3587–3596.

    https://doi.org/10.1016/s0042-6989(01)00245-0

    • PubMed
    • Google Scholar
28. 1. Posner MI

    (1980) Orienting of attention

    The Quarterly Journal of Experimental Psychology 32:3–25.

    https://doi.org/10.1080/00335558008248231

    • PubMed
    • Google Scholar
29. 1. Rainer G
    2. Rao SC
    3. Miller EK

    (1999) Prospective coding for objects in primate prefrontal cortex

    The Journal of Neuroscience 19:5493–5505.

    https://doi.org/10.1523/JNEUROSCI.19-13-05493.1999

    • PubMed
    • Google Scholar
30. 1. Rolfs M

    (2009) Microsaccades: small steps on a long way

    Vision Research 49:2415–2441.

    https://doi.org/10.1016/j.visres.2009.08.010

    • PubMed
    • Google Scholar
31. 1. Schacter DL
    2. Addis DR
    3. Buckner RL

    (2007) Remembering the past to imagine the future: the prospective brain

    Nature Reviews. Neuroscience 8:657–661.

    https://doi.org/10.1038/nrn2213

    • PubMed
    • Google Scholar
32. 1. Schmidt BK
    2. Vogel EK
    3. Woodman GF
    4. Luck SJ

    (2002) Voluntazy and automatic attentional control of visual working memory

    Perception & Psychophysics 64:754–763.

    https://doi.org/10.3758/bf03194742

    • PubMed
    • Google Scholar
33. 1. Shelchkova N
    2. Poletti M

    (2020) Modulations of foveal vision associated with microsaccade preparation

    PNAS 117:11178–11183.

    https://doi.org/10.1073/pnas.1919832117

    • PubMed
    • Google Scholar
34. 1. Souza AS
    2. Oberauer K

    (2016) In search of the focus of attention in working memory: 13 years of the retro-cue effect

    Attention, Perception & Psychophysics 78:1839–1860.

    https://doi.org/10.3758/s13414-016-1108-5

    • PubMed
    • Google Scholar
35. 1. Stokes M
    2. Spaak E

    (2016) The importance of single-trial analyses in cognitive neuroscience

    Trends in Cognitive Sciences 20:483–486.

    https://doi.org/10.1016/j.tics.2016.05.008

    • PubMed
    • Google Scholar
36. 1. van Ede F
    2. Chekroud SR
    3. Nobre AC

    (2019) Human gaze tracks attentional focusing in memorized visual space

    Nature Human Behaviour 3:462–470.

    https://doi.org/10.1038/s41562-019-0549-y

    • PubMed
    • Google Scholar
37. 1. van Ede F
    2. Board AG
    3. Nobre AC

    (2020) Goal-directed and stimulus-driven selection of internal representations

    PNAS 117:24590–24598.

    https://doi.org/10.1073/pnas.2013432117

    • PubMed
    • Google Scholar
38. 1. van Ede F
    2. Deden J
    3. Nobre AC

    (2021) Looking ahead in working memory to guide sequential behaviour

    Current Biology 31:R779–R780.

    https://doi.org/10.1016/j.cub.2021.04.063

    • PubMed
    • Google Scholar
39. 1. van Ede F
    2. Nobre AC

    (2023) Turning attention inside out: how working memory serves behavior

    Annual Review of Psychology 74:137–165.

    https://doi.org/10.1146/annurev-psych-021422-041757

    • PubMed
    • Google Scholar
40. 1. Woodman GF
    2. Vogel EK
    3. Luck SJ

    (2012) Flexibility in visual working memory: Accurate change detection in the face of irrelevant variations in position

    Visual Cognition 20:1–28.

    https://doi.org/10.1080/13506285.2011.630694

    • PubMed
    • Google Scholar
41. 1. Worden MS
    2. Foxe JJ
    3. Wang N
    4. Simpson GV

    (2000) Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex

    The Journal of Neuroscience 20:RC63.

    https://doi.org/10.1523/JNEUROSCI.20-06-j0002.2000

    • PubMed
    • Google Scholar
42. 1. Xie Y
    2. Hu P
    3. Li J
    4. Chen J
    5. Song W
    6. Wang XJ
    7. Yang T
    8. Dehaene S
    9. Tang S
    10. Min B
    11. Wang L

    (2022) Geometry of sequence working memory in macaque prefrontal cortex

    Science 375:632–639.

    https://doi.org/10.1126/science.abm0204

    • PubMed
    • Google Scholar
43. 1. Yuval-Greenberg S
    2. Merriam EP
    3. Heeger DJ

    (2014) Spontaneous microsaccades reflect shifts in covert attention

    The Journal of Neuroscience 34:13693–13700.

    https://doi.org/10.1523/JNEUROSCI.0582-14.2014

    • PubMed
    • Google Scholar
44. 1. Zaksas D
    2. Bisley JW
    3. Pasternak T

    (2001) Motion information is spatially localized in a visual working-memory task

    Journal of Neurophysiology 86:912–921.

    https://doi.org/10.1152/jn.2001.86.2.912

    • PubMed
    • Google Scholar

Author details

1. Baiwei Liu

   Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Investigation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   b.liu@vu.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7857-4456

2. Zampeta-Sofia Alexopoulou

   Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7304-0834

3. Freek van Ede

   Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Investigation, Visualization, Writing – original draft, Writing – review and editing, Funding acquisition, Methodology

   For correspondence

   freek.van.ede@vu.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7434-1751

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