Frontoparietal and Cingulo-opercular Networks Play ...

Frontoparietal and Cingulo-opercular Networks PlayDissociable Roles in Control of Working Memory

George Wallis, Mark Stokes, Helena Cousijn, Mark Woolrich, and Anna Christina Nobre

Abstract

■ We used magnetoencephalography to characterize the spatio-temporal dynamics of cortical activity during top–down control ofworking memory (WM). fMRI studies have previously implicatedboth the frontoparietal and cingulo-opercular networks in controlover WM, but their respective contributions are unclear. In ourtask, spatial cues indicating the relevant item in a WM array oc-curred either before the memory array or during the maintenanceperiod, providing a direct comparison between prospective andretrospective control of WM. We found that in both cases a fron-toparietal network activated following the cue, but following retro-

cues this activation was transient and was succeeded by a cingulo-opercular network activation. We also characterized the timecourse of top–down modulation of alpha activity in visual/parietalcortex. This modulation was transient following retrocues, occur-ring in parallel with the frontoparietal network activation. We sug-gest that the frontoparietal network is responsible for top–downmodulation of activity in sensory cortex during both preparatoryattention and orienting within memory. In contrast, the cingulo-opercular network plays a more downstream role in cognitivecontrol, perhaps associated with output gating of memory. ■

INTRODUCTION

Performance in working memory (WM) tasks is stronglymodulated by selection cues that allow people to priori-tize the most task-relevant item. Sperling (1960) demon-strated that cues picking out the task-relevant items givenbefore or immediately following the presentation of aWM array (when items were still available in the iconicbuffer) improved WM performance. This effect has a readyinterpretation in terms of gating of encoding to a capacity-limited WM store, reducing memory load to one item.However, more surprisingly, cueing the task-relevant itemduring the memory retention interval, termed “retrocue-ing” (Sligte, Scholte, & Lamme, 2008; Nobre et al., 2004;Griffin & Nobre, 2003; Landman, Spekreijse, & Lamme,2003), improves response accuracy almost as much. Thisfinding challenges the assumption that WM performanceis only limited by storage capacity. Retrieval mechanismsor “output gating” (Chatham, Frank, & Badre, 2014) maybe just as important a determinant of performance as “in-put gating” of WM. Although precues are thought to permitinput gating of WM, retrocues may facilitate output gating.Our study investigated the different patterns of involve-ment of control networks in these different forms of top–down control of WM.One hypothesis holds that a shared top–down atten-

tional mechanism acts both on perceptual input and onWM representations (Gazzaley & Nobre, 2012) to medi-ate both precue and retrocue benefits. fMRI experiments

suggest that whether control is prospective and selectsfrom perceptual input or retrospective, selecting fromwithin WM results in the following: (1) substantially over-lapping frontoparietal control regions are recruited (Nee& Jonides, 2009; Nobre et al., 2004) and (2) activity insensory cortex is modulated retinotopically (Kuo, Stokes,Murray, &Nobre, 2014; Munneke, Belopolsky, & Theeuwes,2012; Sligte, Scholte, & Lamme, 2009;Mangun,Hopfinger, &Buonocore, 2000). This fits well with a model of WM inwhich persistent activity in perceptual cortex is responsiblefor maintaining WM representations (Harrison & Tong,2009; Pasternak & Greenlee, 2005): Top–down influencesbias perceptual activity during WM retention in the sameway as it is biased by perceptual attention. However, thismodel may be too simple. First, recent work suggests thatnot all items in WM are associated with a persistent activitystate in sensory cortex (LaRocque, Lewis-Peacock, & Postle,2014). Second, retrocues recruit additional sites in frontalcortex besides the frontoparietal network. In particular,Higo, Mars, Boorman, Buch, and Rushworth (2011) and,more recently, Nelissen, Stokes, Nobre, and Rushworth(2013) have suggested that the dorsomedial pFC and theanterior insula or frontal operculum [fO] (hereafter weuse the term frontal operculum for clarity) may be the crit-ical sites for top–down control over WM representations—as opposed to frontoparietal sites. These areas are nodesof a second “cingulo-opercular” control network, which isdistinct from the frontoparietal network classically associ-ated with top–down control (Petersen & Posner, 2012;Dosenbach et al., 2007). The additional recruitment ofthe cingulo-opercular network by retrocues, as comparedUniversity of Oxford

© 2015 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 27:10, pp. 2019–2034doi:10.1162/jocn_a_00838

to precues, is an intriguing clue to how its functionalrole may differ from and complement the frontoparietalnetwork.

In this study, we used a precision/capacity (Zhang &Luck, 2008) visual WM task giving precues (Murray,Nobre, & Stokes, 2011; Sperling, 1960) and retrocues(Murray, Nobre, Clark, Cravo, & Stokes, 2013; Landmanet al., 2003) on different trials to compare prospectiveand retrospective control of WM. We first reviewed priorfMRI work using a meta-analysis technique to confirm thedifferent spatial patterns of cortical recruitment betweenprecues and retrocues and obtain a priori spatial ROIs.We then used magnetoencephalography (MEG), an imag-ing modality with high temporal resolution, to character-ize the pattern of cortical activity in these areas after bothprecues and retrocues. Our time-resolved data reveal thetemporal relationship between activation in these twonetworks and top–down modulation of activity in sensorycortex. They show that both precues and retrocues sim-ilarly recruit the frontoparietal network, but retrocues ad-ditionally recruit the cingulo-opercular network at a latertime point.

METHODS

Participants and Behavioral Task

Fifty volunteers were recruited (26 women, 24 men;mean age = 24 years, range = 19–34). All participantswere healthy, had normal or corrected-to-normal vision,and were right-handed. Ethical approval was obtainedfrom the National Health Service South Central Berkshireethics committee (11/SC/0053). One participant per-formed at chance in the behavioral task, and one was un-able to complete the MEG session. Forty-eight full datasets were therefore available for behavioral analysis. Fur-ther participants were excluded from the MEG analysis.Four made eye movements larger than 2° horizontal dis-placement during the WM retention intervals (as mea-sured on the basis of the eye-tracker signal), potentiallycontaminating the MEG signal. Six participants were re-jected because their MEG data were of poor quality:For two participants, the spatial coregistration of theMEG forward model with the signal space failed, andfor four participants, the signal was heavily contaminatedwith electronic artifacts. This left us with MEG data from38 participants. The behavioral task was programmed inMatlab and PsychToolbox (Pelli, 1997). Behavioral andMEG analyses were performed using custom-writtenMatlab software, SPM8, FSL, Fieldtrip, and the in-houseOHBA Software Library.

We used a four-item WM task with predictive cues(precues) and retrodictive cues (retrocues). The behav-ioral task was run in two separate sessions: The trainingsession contained 6 × 36 trial blocks (216 trials), and theMEG session contained 9 blocks (324 trials).

Figure 1A is a task schematic. Four memory items werepresented. Following a delay period of 3310 msec, a sin-gle probe item appeared. The probe was identical to oneof the items from the memory array but rotated aboutthe circular end body, through 5°, 15°, or 45° clockwiseor anticlockwise (equal probability). Participants re-sponded with a right-hand button press; the left button(index finger) indicated an anticlockwise rotation, theright button (middle finger) clockwise. On one-third oftrials, a spatial precue 1540 msec before array onset indi-cated the relevant memory item. On a separate one-third of trials, there was a spatial retrocue 1540 msec intothe retention interval. On remaining trials (“neutral-cuetrials”), no information was given about which itemwould be probed. Trial types were randomly interleaved.Cues were always valid. The cue consisted of a white anda black arrow formed by the sides of a square (Figure 1A).For half of the participants, the black arrow indicated thecued quadrant, and for half of participants, the whitearrow indicated the cued quadrant, controlling for anyeffects driven by the physical properties of the cue.Participants were asked to maintain central fixation,

and eye movements were monitored using an infraredbinocular eyetracker (Eyelink 1000, SR Research, OttowaCanada). During the training session, stimuli were pre-sented on an LCD screen (Samsung 2233Rz, Samsung,Seoul) at a viewing distance of 80 cm. During the MEGsession, stimuli were back-projected (Panasonic PTD7700E, Panasonic, Osaka Japan) onto a screen at a view-ing distance of 85 cm. In both sessions, the WM stimuliwere presented at an eccentricity 6° visual angle from thefixation cross, and each stimulus subtended 1.2°.To reduce noise from trials in which the participants

were settling into the task, the first 50 trials were dis-carded from the training block, as well as the first 10 trialsfrom the MEG session (including these trials does notsubstantively alter any aspect of the results). Any trialsin which the RT was shorter than 0.1 sec or longer than10 sec (the median RT was 1.1 sec) were discarded fromthe analysis, as they likely represented anticipation re-sponses or a lapse in task engagement, respectively.For most participants (40 of 48), five or fewer trials wereexcluded on the basis of RT. The maximum number oftrials rejected was 20.A mixture model was fitted for the accuracy data

(Zhang & Luck, 2008) in which responses across trialsare assumed to come from one of two distributions: auniform ‘guess’ distribution, representing trials in whichparticipants had no information about the probed itemand responded at random, and a Von Mises distribution(circular analogue of a Gaussian) that represents thefidelity of the WM representation on trials when partici-pants were not guessing. We checked for response bias to-ward “clockwise” or “anticlockwise” responses bytaking the difference of net clockwise and anticlockwise re-sponses across all trials for each cue condition. For neutraltrials, there was a tendency for participants to respond

2020 Journal of Cognitive Neuroscience Volume 27, Number 10

“anticlockwise” ( p(clockwise) − p(anticlockwise) =−0.12, p = .001). This was also significant for precuetrials (−0.077, p = .042) and present as a trend for retro-cue trials (−0.066, p = .08). This suggested that, whenguessing, participants were slightly more likely to pressthe index-finger than middle-finger button. Clockwise/an-

ticlockwise orientation changes were collapsed togetherfor modeling, factoring out the response bias. The prob-ability that a participant made the correct response on agiven trial depends onk, the precision of the VonMises dis-tribution (higher values indicate a tighter distribution);pGuess, the probability that participants are guessing on

Figure 1. Task schematic andbehavioral data. (A) Precue/retrocue WM task. The threetrial types are randomlyinterleaved, each making upone-third of trials overall.In this case, the white arrowindicated the cued direction.For half of participants,this was reversed, and theblack arrow indicated the cueddirection. (B) Accuracy data.The proportion of clockwiseresponses is plotted for eachorientation change of the probeitem (x-axis, clockwise positive).Both precues (blue) andretrocues (red) improveperformance at all orientationchange magnitudes. (C) RTdistributions for each trial type,binned into quintiles. Precuesand retrocues both speedresponses. (D, E) Mixturemodel analysis. Precues andretrocues substantially reducethe proportion of trials onwhich participants are guessing(D). Precues substantiallyincrease the precision withwhich items are represented inmemory but retrocues haveonly a modest effect onprecision (E). (F) Misgatinganalysis. If there were no effectof nontarget items onresponses, this plot would showa flat line. Nontarget itemsinfluenced responding moststrongly in the neutral-cuecondition. Error bars represent±1 SEM.

Wallis et al. 2021

any particular trial; and θ, the size of the orientation change.This is captured in Equation 1.

p correctð Þ ¼ pGuess� 0:5ð Þ þ 1−pGuessð Þ� vonmisescdf k; θð Þ (1)

vonmisescdf is the cumulative density function of theVon Mises distribution, which can be evaluated numerically.Themodel was fitted for each participant and each conditionseparately using a maximum likelihood approach to getvalues of pGuess and k (Figure 1D, E).

The mixture model analysis assumes that nontargetitems have no effect on responding, but previous workusing similar multi-item WM tasks suggests that nontargetitems can sometimes influence behavior (Bays, Gorgoraptis,Wee, Marshall, & Husain, 2011; Bays, Catalao, & Husain,2009). In the current task, the orientation of each mem-ory item was assigned randomly and independently ofthe orientation of the other items. The effect of non-target items could therefore be evaluated by running asimilar analysis to that for the target item, examiningthe probability of responding “clockwise” or “anticlock-wise,” depending on whether the probe item was rotatedclockwise or anticlockwise of each nontarget item. Be-cause the target and nontarget orientations were ran-domly and independently assigned, the orientationdifference between the probe item and a given nontargetitem spanned the full circle between −180° and 180°. Thisrange was divided into eight bins: clockwise/anticlockwise0°–45°, 45°–90°, 90°–135°, and 135°–180°, and for each bin

the probability of responding clockwisewas calculated. Notethat, for any given bin, the probability of the probe itemhaving been clockwise or anticlockwise of the target itemwas always the same (.5), so if there were no influence ofnontarget items, the probability of responding “clockwise”should be equal across all bins.

Meta-analysis of fMRI Data/Derivation of MEG ROIs

We performed meta-analyses of spatial precueing and ret-rocueing fMRI studies to guide analysis of MEG datausing the freely available GingerAle software package(Eickhoff et al., 2009).Separate meta-analyses were conducted for precue and

retrocue experiments. Imaging studies were searchedusing Pubmed and Google Scholar. Retrocue studieswere included if they involved a late cue (>1 sec afterthe memory array) to focus on an item already in mem-ory, and we used data from contrasts that isolated thebrain response to this cue. Precue studies were restrictedto studies cueing spatial attention to isolate activationsassociated with spatial orienting. We included only thosestudies that could differentiate responses to the cue fromsubsequent responses to the cued targets. Seventeenstudies were included in the retrocue meta-analysis, andeight studies were included in the precue meta-analysis.These studies are listed in Tables 1 and 2. Local maximawere extracted from the resulting meta-analysis activationmaps and are listed in Tables 3 and 4.

Table 1. Studies Included in the Retrocue Meta-analysis

Study Study No. No. of Participants

Rowe, Toni, Josephs, Frackowiak, and Passingham (2000) 1 6

Rowe and Passingham (2001) 2 6

Raye, Johnson, Mitchell, Reeder, and Greene (2002) 3 12

Johnson, Raye, Mitchell, Greene, and Anderson (2003) 4 14

Nobre et al. (2004) 5 10

Johnson, Mitchell, Raye, and Greene (2004) 6 14

Lepsien, Griffin, Devlin, and Nobre (2005) 7 10

Johnson et al. (2005) 8 14

Lepsien and Nobre (2006) 9 14

Yeh, Kuo, and Liu (2007) 10 10

Johnson, Mitchell, Raye, D'Esposito, and Johnson (2007) 11 15

Yi, Turk-Browne, Chun, and Johnson (2008) 12 8

Raye, Mitchell, Reeder, Greene, and Johnson (2008) 13 29

Johnson and Johnson (2009) 14 14

Nee and Jonides (2009) 15 18

Roth, Johnson, Raye, and Constable (2009) 16 22

Higo et al. (2011) 17 21


To simplify this spatial pattern into a single set of ROIsfor MEG analysis (Table 5), with a spatial sampling appro-priate to the comparatively coarse spatial resolution ofthe method (as compared with fMRI), we convertedthese local maxima into a left/right symmetric spatiallysparse set of brain locations by averaging local maximafor each cluster within each meta-analysis map. We then

averaged activations common to the precue and retrocuemeta-analyses (using a distance threshold of 16 mm).This set of combined coordinates was converted into asymmetric set of ROIs by averaging corresponding leftand right hemisphere ROI locations and mirroring theactivations that occurred in the left hemisphere only (mid-dle temporal gyrus [MTG], TPJ) in the right hemisphere.

Table 2. Studies Included in the Precue Meta-analysis

Study Study No. No. of Participants

Mangun et al. (2000) 1 6

Corbetta, Kincade, Ollinger, McAvoy, and Shulman (2000) 2 13

Giesbrecht, Woldorff, Song, and Mangun (2003) 3 10

Nobre et al. (2004) 4 10

Woldorff et al. (2004) 5 20

Wilson, Woldorff, and Mangun (2005) 6 16

de Haan, Morgan, and Rorden (2008) 7 12

Egner et al. (2008) 8 14

Table 3. Activation Clusters from the Retrocue Meta-analysis

Label Cluster No.

Local Maxima (MNI Coordinates)

Cluster Volume (mm3)x y z

Right anterior MFG 1 44 46 24 1696

34 50 16

Left anterior MFG 2 −40 36 28 944

Left precentral / Left anterior MFG 3 −50 −2 40 5040

−50 22 28

−54 12 34

Right anterior insula (fO) 4 48 12 −4 2056

Left anterior insula (fO) 5 −40 14 −4 1304

dACC/pre-SMA 6 0 18 48 3272

−2 30 36

Right precentral 7 48 6 42 440

Left MTG 8 −58 −38 0 824

−66 −42 0

Left TPJ 9 −56 −38 32 480

−48 −38 32

Left IPS 10 −38 −48 44 488

Right SPL 11 12 −70 52 1400

18 −62 54

Right IPS 12 34 −58 42 1048

38 −44 44

30 −64 38

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MRI Scan

A structural MRI was acquired for each participant using aSiemens 3T scanner (OCMR, Oxford, UK). A 32-channel

head coil was used to obtain a T1-weighted anatomicalimage with 224 (1-mm) slices. This anatomical imagewas used to define the single-shell MEG forward model(Nolte, 2003). SPM’s spm_eeg_inv_mesh was used tocompute the transformation that mapped a set of canon-ical meshes for the cortical surface, skull, and scalp toeach participant’s individual anatomical MRI, and thistransformation was used to define a MEG forward modeltailored to each participant’s head shape, computedusing Fieldtrip’s forward toolbox (Donders Institute,Nijmegen, The Netherlands; shared with SPM). Spatialcoregistration between the forward model and MEGspace was by aligning the spaces based on anatomicallandmarks (nasion and left/right preauricular points) fora first estimate and then refining this fit using pointsrecorded from the scalp surface (see MEG scan), whichwere matched to the scalp surface mesh using an itera-tive closest point algorithm as implemented in SPM.

MEG Scan

MEG data were acquired using an Elekta Neuromag 306-channel system (Elekta, Stockholm, Sweden; 204 planargradiometers, 102 magnetometers). The MEG suite ispassively shielded. ECG and vertical/horizontal EOG

Table 4. Activation Clusters from the Precue Meta-analysis

Label Cluster #

Local Maxima (MNI Coordinates)

Cluster Volume (mm3)x y z

Left anterior MFG 1 −40 30 24 472

Right FEF 2 32 0 50 1560

Left precentral/ left FEF 3 −36 −4 42 464

−42 −4 52

Left FEF 4 −22 −2 50 512

−22 −8 58

Left IPS 5 −40 −48 36 544

Right SPL 6 26 −56 58 1112

Left IPS 7 −22 −66 54 3960

−18 −58 54

−22 −68 40

Right SPL 8 22 −72 50 768

28 −68 44

Right IPS0/V7 9 34 −78 26 584

34 −74 26

Left IPS0/V7 10 −28 −78 30 400

Left occipital 11 −46 −70 −10 800

Right occipital 12 34 −80 14 800

34 −84 14

Table 5. ROIs Derived from the fMRI Meta-analysis

MNI Coordinates

x y z

IPS0 ±34 −76 26

Mid-IPS ±30 −68 40

Anterior IPS ±39 −48 40

SPL ±12 −68 60

FEF ±27 −3 52

Precentral cortex (or iFEF) ±46 1 43

Anterior MFG ±40 39 23

TPJ ±52 −38 32

MTG ±62 −40 0

fO ±44 13 −4

Pre-SMA 0 24 42


were recorded. Head position was continuously moni-tored using emitting coils affixed to the participant’sscalp and a Polhemus 3D tracking system (PolhemusEastTrach 3D, Polhemus, Vermont, Unites States). Ana-tomical landmarks (nasion and left/right preauricularpoints) were recorded, as well as ∼100 points spreadout over the scalp surface. MEG data were recorded inthree blocks of ∼15 min each.

MEG Preprocessing

MEG data were initially inspected to remove any chan-nels severely corrupted by noise and then de-noisedand corrected for head movements using Elekta’s MaxfilterSignal Space Separation algorithm (Taulu, Kajola, &Simola, 2004). Data were epoched and visually inspectedagain using Fieldtrip’s visual artifact rejection tool for stan-dard artifacts: Contaminated trials and channels weretagged on the basis of abnormal variance, kurtosis, andmaxima/minima in the time domain data. Eyeblinks weredetected from the EOG, and eye-tracker data using a semi-automatic algorithm and data from 200 msec before anduntil 300 msec after each blink were excluded from allanalyses, including estimation of the beamformer weights.

MEG Analysis

Data were cut into three epochs for analysis: precue ep-och, array epoch, and retrocue epoch (see Figure 1A).Key experimental contrasts compared (1) activity in trialsin which there was a precue or retrocue with neutral-cuetrials (“cue effects”) and (2) activity in leftward-cued trialswith activity in rightward-cued trials (“cue laterality”—tomeasure alpha-power lateralization in visual cortex).

Sensor Space Analysis of AlphaPower Lateralization

The time domain sensor space signal was transformed tothe frequency domain in 50-msec steps, using a Hanningtaper/FFT algorithm with a taper spanning four cycles ofthe filtered frequency, for frequencies between 3 and30 Hz in 1-Hz steps. The resulting power spectra were av-eraged over trials within each cue condition. The powertime series in the planar gradiometer pairs were thencombined (Cartesian sum), giving a 102-channel com-bined planar gradiometer map of sensor space power.The cue laterality subtractions [precue left minus precueright] and [retrocue left minus retrocue right] were com-puted per participant for the precue and retrocueepochs, respectively. Sensor space cluster permutationstatistics (Maris & Oostenveld, 2007) were computedfor these topographies by permuting cue left/cue rightcondition labels (using Fieldtrip’s ft_freqstatistics). Clus-ters were formed in space (sensor proximity) and time,averaging over the alpha (8–12 Hz) band.

Sensor Space Classification Analysis ofAlpha Lateralization

We used a simple correlation-based pattern classificationapproach to test whether the pattern of decrease in alphapower to an attentional cue resembled the pattern ofevent-related alpha desynchronization (ERD) in responseto a physical stimulus. We averaged the induced re-sponses to the probe stimulus between 300 and 500 msec(the time window for which within-epoch alpha-bandquadrant classification for the probe item was highest),yielding four sensor space patterns for trials in whicheach of the four visual quadrants was probed. Theseprobe ERD topographies were then correlated againstthe trial-wise cue-induced topographies from the precueand retrocue epochs. The trials were classified to a quad-rant based on whichever probe stimulus quadrant pat-tern was most correlated with the pattern of activity onthat trial. A leave-one-out approach was used to preventcross-temporal correlations within trials from confound-ing the analysis: The to-be-classified trial was always ex-cluded from the averages of probe stimulus activity. Theclassification results were averaged over time–frequencybins in the alpha band (8–12 Hz) and the interval 0.4–0.8 sec after the cue for plotting (Figure 2C, D).

Cross-temporal Correlation Analysis

To establish whether the patterns of brain activity weobserved were stable or transient, we correlated cue-induced brain states across time, as indexed by theinduced response topographies. We randomly subdi-vided all of the experimental trials into two groups ofequal size (discarding trials if more trials had survivedpreprocessing in one condition than another). Withineach of the two halves, data were averaged within condi-tions, and the cue effects contrast was computed. Thisyielded two independent estimates of the cue effectstopography.

We performed this analysis for the theta (3–7 Hz), al-pha (8–12 Hz), and beta (18–30 Hz) bands separately. Asin previous analyses (Stokes et al., 2013), one half of thedata was designated the training data, and the other halfthe test data. The topographies were extracted from thetraining data for each time point in the epoch. This to-pography was correlated with the topography at everytime point in the test data, building up a cross-temporalcorrelation matrix (King & Dehaene, 2014). If a state istransient, then it gives rise to high correlation valuesmainly on the diagonal of this matrix. By contrast, tempo-rally stable states will give rise to high correlation valuesconfined to the diagonal of this matrix. The data weresplit randomly, so each time this analysis is run, a slightlydifferent result will be obtained. We therefore performedthis analysis 20 times and averaged the results; this boot-strapping procedure stabilizes the estimate of the cor-relation structure. We statistically evaluated the strength

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of the correlations at the group level by forming clustersin the time/time correlation space and then tested theseagainst a permutation distribution of cluster size.

Source Space Analyses

To characterize the time course of activation in each ROI,a virtual electrode was created for each ROI coordinateusing a linearly constrained minimum variance beamfor-mer (Woolrich, Hunt, Groves, & Barnes, 2011). Thetime–frequency representation of the data was then com-puted at each virtual electrode, between 3 and 30 Hz. Thetime–frequency data were averaged across task conditionswithin participants. The condition averages (cue effectsand cue laterality) were then subtracted within partici-pants. These contrasts were averaged at the group levelto create time–frequency maps of cue-related activity foreach ROI. Significance testing was performed by formingclusters in the time/frequency space for each ROI, for pos-itive and negative deviations in power, and then testingagainst a permutation distribution of cluster size.

To visualize spatial patterns of activation over thewhole brain and verify that these were appropriately sam-pled by the ROIs derived from the meta-analysis, we ran

whole brain-induced response analyses for the thetaband (3–7 Hz), alpha band (8–12 Hz), and beta band(18–30 Hz). The 4-D spatiotemporal map for each analy-sis epoch (precue, retrocue) was averaged over succes-sive 300-msec windows. Cluster permutation statisticsfor the 3-D maps were computed for the informativecue versus neutral cue contrasts with a cluster-formingthreshold of 3 (t statistic).

RESULTS

Behavioral Data

Group level accuracy data are shown in Figure 1B: Both pre-cues and retrocues improved response accuracy, increasingthe proportion of correct responses across all magnitudesof orientation change. Group level parameters for the mix-ture model analysis described in Methods are plotted inFigure 1D, E. Precues and retrocues both reduced guessrate, but only precues substantially increased precision. Arepeated-measures ANOVA with factor Cue condition(3 levels: neutral, precue, retrocue) found evidence for amain effect of Cue condition upon guess rate (F(1.74,82.3) = 203.1, p < .0005). Paired sample t tests against theneutral condition confirmed that guess rate was significantly

Figure 2. Modulation of alpha-band (8–12 Hz) power in visualcortex. (A, B) Sensor spacetopography of alpha power forthe contrast [cue left minus cueright], at 0.6 sec following theprecue (200 msec FWHM) (A)and retrocue (B). Sensorsbelonging to significant clusters(see Results) are circled (black,positive clusters; white,negative clusters). (C, D)Classifying the direction ofattention by correlating the cue-induced topography with thetopography of the inducedresponse to the probe item.Classification percentages areshown relative to the cuedquadrant, averaged over the 8–12 Hz band, from 0.4 to 0.8 secpostcue. E, F show an alphalateralization index calculatedfor the IPS0 virtual electrode.Alpha lateralization is persistentfollowing precues, untilpresentation of the memoryarray. Lateralization subsides formost of the maintenanceinterval but ramps up justbefore presentation of theprobe item. Followingretrocues, alpha lateralization istransient, returning to near-baseline level by 1 sec postcue.


reduced in the precue condition (t(47) = 16.95, p< .0005)and retrocue condition (t(47) = 14.55, p < .0005).For the precision parameter, there was also a main ef-

fect of Cue condition (F(2, 94) = 17.2, p< .0005). Paired-sample t tests indicated that the precision in the precuecondition was significantly higher than in the neutral con-dition (t(47) = 5.63, p < .0005), but the increase in pre-cision in the retrocue condition was only marginallysignificant (t(47) = 2.04, p = .047). Precision in the pre-cue condition was significantly higher than precision inthe retrocue condition (t(47) = 3.56, p = .001).We also tested whether nontarget items affected be-

havior. This has previously been termed “misbinding”(Bays et al., 2011) but could also be characterized as “mis-gating,” if as argued here, the effect is attributable to se-lecting the wrong item in memory to guide behavior. Weperformed a repeated-measures ANOVA with factors Ori-entation bin (8 levels corresponding to the orientationbins described in Methods) and Cue condition (3 levels:neutral, precue and retrocue). There was a main effect ofOrientation bin (F(7, 329) = 13.65, p < .0005) confirm-ing that the nontarget items affected responding, andthere was an interaction between Orientation bin andCue condition (F(14, 658) = 4.11, p < .0005) indicatingthe degree of misgating differed between the cue condi-tions. These data are shown in Figure 1F.To quantify the differences between cue conditions

contributing to this interaction, we ran repeated-measuresANOVAs comparing pairs of conditions (i.e., as describedabove, but with cue condition levels (1) precue, retrocue;(2) precue, neutral cue; (3) retrocue, neutral cue). Therewas no evidence for a Cue condition × Orientation bininteraction for (1) precue versus retrocue (F(7, 329) =0.598, p = .757), but there was a significant Cue condi-tion × Orientation bin interaction for (2) precue versusneutral cue (F(7, 329) = 5.71, p < .0005) and for (3) ret-rocue and neutral cue (F(7, 329) = 5.87, p < .0005).Therefore, both precues and retrocues significantly re-duced the propensity to respond on the basis of a non-target item.Relative to the neutral condition, precues and retro-

cues also reduced RTs. In Figure 1C, RT distributionsare expressed in terms of quintile means: Precues andretrocues were associated with a strikingly similar RT dis-tribution, with a reduction in RT relative to the neutralcondition that was present across all quintiles (mean re-duction in RT for precues, 422 msec, SEM = 34 msec;retrocues, 432 msec, SEM = 36 msec). We compared me-dian RTs between the cue conditions using a repeated-measures ANOVA with factor Cue condition (3 levels).There was a significant main effect of Cue condition (F(2,94) = 136.56, p < .0005). We then compared the condi-tions using paired-sample t tests. There was a significantdifference between median RT for neutral and precue tri-als ( p = 5.7 × 10−7) and neutral and retrocue trials ( p =2.9 × 10−7), but no significant difference between precueand retrocue trials ( p = .90).

Behavioral analyses performed over the subset of 38participants for whom we were able to analyze theMEG data were qualitatively the same as for the set of48 participants who successfully completed the behav-ioral task, except that the marginally significant effect ofretrocues upon precision (kappa) did not reach signifi-cance (t(37) = −1.58, p = .12).

Alpha Power Modulation in Perceptual and ParietalCortex Indexes the Allocation of Attention

Alpha power in perceptual and parietal cortex is robustlymodulated by preparatory attention (van Ede, Köster, &Maris, 2012; Haegens, Händel, & Jensen, 2011; Siegel,Donner, Oostenveld, Fries, & Engel, 2008; Worden, Foxe,Wang, & Simpson, 2000). Typically, when attention is di-rected to one side of space, there is a relative increase inalpha power in the ipsilateral cortex and decrease in alphapower in the contralateral cortex, compared with whenattention is directed to the other hemifield. We hypoth-esized that the pattern of alpha activity in visual and pa-rietal cortex would be similarly modulated by precuesand retrocues, as both cue types may recruit a commontop–down mechanism. We first performed a sensor spaceanalysis subtracting the pattern of activation in trials inwhich a quadrant in the right hemifield was cued fromthe pattern of activation when a quadrant in the left hemi-field was cued. The results (for a representative time point0.6 sec postcue) are plotted in Figure 2A (precues) and B(retrocues). Bothprecues and retrocues robustly lateralizedalpha activity.

Cluster permutation tests revealed a significant clusterof sensors with increased power over the left occipito-parietal sensors for the contrast precue left minus precueright ( p = .0005). The cluster of sensors with decreasedpower over the right hemisphere sensors did not reachsignificance ( p = .10). For the retrocue left minus retro-cue right contrast, there was both a significant cluster ofsensors with increased power centered over the lefthemisphere occipito-parietal sensors ( p = .0065) and asignificant cluster of sensors with decreased power overthe right hemisphere sensors ( p = .001).

The source space time course for the laterality contrastwas extracted at occipito-parietal ROIs and converted in-to a lateralization index by flipping the sign of the de-crease in alpha power in the right hemisphere andadding it to the increase in alpha power in the left. Later-alization was strongest in the IPS0 ROI, for which precueand retrocue time courses are shown in Figure 2E and F.Precues gave rise to a more sustained alpha lateralizationlasting from ∼0.5 sec postcue until the presentation ofthe memory array, whereas retrocues gave rise to a moretransient lateralization of alpha power between 0.5 and1 sec postcue that returned to baseline before the probestimulus appeared. Note the very similar time course ofdecrease in alpha power in IPS for the cue effects con-trast (Figure 5A).

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To establish whether the alpha-power changes werequadrant-specific and resembled an ERD to a physicalstimulus, we classified the cue-induced topographiesbased on the ERD to the probe item. The results areshown in Figure 2C and D, in which the classification pat-tern is shown averaged over time–frequency bins in thealpha band (8–12 Hz) between 0.4 and 0.8 sec followingprecues and retrocues, coded relative to the cued quad-rant. We tested the quadrant specificity of this effect sta-tistically by separately comparing up–down classificationand left–right classification and testing resulting time–frequency clusters of above-chance classification againsta permutation distribution of cluster sizes under the nullhypothesis. Quadrant classification was significant forboth precues (up/down cluster, p = .029; left/right clus-ter, p = .0002) and retrocues (up/down cluster, p <.0002, left/right cluster, p < .0002).

We also tested for an induced-response analogue tocontralateral delay activity (Vogel & Machizawa, 2004)in which there is a sustained ERP lateralization duringthe retention interval following lateralized encoding byclassifying cued quadrant in the WM retention intervalin precue trials. For comparison, Sauseng et al. (2009)reported alpha lateralization in the delay interval of atask similar to that used by Vogel and colleagues. Wefound a transient effect in the alpha band (8–12 Hz)soon after the memory array, significant only for theleft/right decoding ( p = .0062) between 0.5 and 0.7 secfollowing the array onset (compare with the timing ofpeak lateralization following retrocues). We also founda stronger effect (Figure 2E) that emerged in the run-up(<1 sec before) to the probe stimulus (up/down decod-ing, p = .0022; left/right decoding, p = .0002). Our re-sults therefore partly replicate those of Sauseng et al.(2009), but the longer retention interval in our experi-ment (3000 msec as compared to 900 msec in the pre-vious study) revealed that alpha lateralization in theretention interval manifested over short intervals whenattention was most likely to be lateralized, consistentwith the transient lateralization observed followingretrocues.

Cross-temporal Classification Analysis

To picture the overall temporal dynamics of brain activityin response to a precue or retrocue without first spatiallyselecting the data, we randomly split the trials in eachcondition into two halves and correlated the induced re-sponse topographies of contrasts performed on each halfseparately, across time (i.e., correlating the topography attime 1 with the topography at time 2—for all combina-tions of t1 and t2). As illustrated in Figure 3A, on-diagonalcorrelations reflect the reproducibility of topographiesacross the independent data sets, and the off-diagonalcorrelations capture the temporal persistence of thesecue-induced brain states (see King & Dehaene, 2014,for an in-depth discussion). The analysis was performed

separately for the theta (3–7 Hz), alpha (8–12 Hz), andbeta (18–30 Hz) bands (Figure 3B).The cross-temporal correlation matrix shows that, after

precues, a stable state emerges from ∼0.6 sec postcueuntil the presentation of the memory array (Figure 3B,“square” in upper right for precues). Retrocues gave riseto a different pattern. Cue-induced activity following aretrocue continues to evolve throughout the analysis ep-och. This overview of the temporal structure of the cue-induced responses can be compared with the sourcespace analysis of induced responses shown in Figure 5.

fMRI Meta-analysis

The results of the meta-analyses are given in Tables 3 and 4,and the ROIs derived from the meta-analysis results areshown in Figure 4. There was overlap between precue-and retrocue-associated activations in the anterior middlefrontal gyrus (MFG; or dlPFC) and intraparietal sulcus (IPS).Both cue types also activated regions in the precentral gy-rus. Comparing themeta-analysis results, there was a disso-ciation between cue types, in that precue studies reportedactivity in the FEF whereas retrocue studies reportedactivity in a more inferior region we call iFEF (adoptingDerrfuss’ terminology; Derrfuss, Vogt, Fiebach, vonCramon, & Tittgemeyer, 2012). This is consistent withthe previous study by Nee and Jonides (2009), which iden-tified this dissociation. iFEF is in proximity to IFJ, and theretrocue meta-analysis activation cluster encompassedthe coordinates of both regions. Kastner and colleagues(2007) have also identified these two dissociable precen-tral regions.Retrocues additionally activated the bilateral fO and

the pre-SMA. The left posterior MTG and the left inferiorparietal lobule were also activated following retrocues.The meta-analysis results were consistent with previ-

ously described frontoparietal and cingulo-opercular con-trol networks (Petersen & Posner, 2012; Dosenbachet al., 2007). Precues and retrocues both activated thefrontoparietal network, and retrocues additionally acti-vated the cingulo-opercular network.

Induced Responses in Control Networks

The ROIs derived from the fMRI meta-analysis were used toextract source space-induced responses from frontal andparietal control regions. The contrast [informative cue mi-nus neutral cue] between 3 and 30 Hz is shown for fronto-parietal and cingulo-opercular ROIs in Figure 5A. Theremaining parietal ROIs (IPS, SPL) (not shown) showed asimilar pattern of responses to the mid-IPS.The spatial pattern of induced responses in the control

region ROIs replicated the spatial pattern of BOLD activa-tions captured in themeta-analysis. Both precues and retro-cues activated the anterior MFG and mid-IPS, with precuesadditionally activating FEF and retrocues activating iFEF. Inmid-IPS, an increase in theta power was accompanied by a


decrease in alpha/beta power that was sustained for pre-cues and transient for retrocues. This decrease was stron-gest in the left IPS. These frontoparietal network powerincreases occurred at a similar latency after the cue fol-lowing both precues and retrocues.

Only retrocues coactivated the fO and pre-SMA (cingulo-opercular network), again replicating the pattern observedin the fMRI meta-analysis. In contrast to the frontoparietalnetwork, these regions coactivated late in the retrocueepoch, after the decrease in power inmid-IPS had returnedto baseline∼1 sec postcue. The fO power increases were inthe theta/alpha band, and the pre-SMA in the beta band.The pre-SMA also responded in the theta band immedi-ately following both precues and retrocues. These powerincreases were not present in the [precue minus neutralcue] analysis for the same retrocue epoch time periodand are therefore unlikely to correspond to preparatoryactivity for the probe stimulus.

We verified that the ROIs appropriately sampled the pat-tern of activity in source space by computing whole-brainmaps of activity over successive 300-msec windows. Thewhole brain-induced response maps reproduced the pat-terns expected on the basis of the fMRI meta-analysis. Thisis illustrated in Figure 5B, which shows the time period300–600 msec postcue, and in Figure 5C, which showsthe time period 1200–1500 msec postcue.

Figure 3. Temporal dynamics ofcue-induced brain states. (A) Theexpected correlation patterns forunstable and stable brain states.A continually changing brainstate will not be correlated whent1 ≠ t2, that is, on the off-diagonal. By contrast a stablestate will lead to off-diagonalcorrelations. (B) The correlationstructure for the [precue –neutral] and [retrocue – neutral]topographies, that is, the stabilityof cue-induced brain states asindexed using sensor spaceinduced responses in the theta(3–7 Hz), alpha (8–12 Hz), andbeta (18–30 Hz) bands, over allsensors. Group level t statisticsare shown. The correlationstructure was tested using apermutation distribution ofcluster size and is thresholdedat t = 2. Significant correlationsare in full color saturation;nonsignificant correlations areunsaturated. The response toprecues remains significantlycorrelated between 0.4 sec andthe end of the precue epoch(presentation of the memoryarray) indicating a persistent,stable state. By contrast, theresponse to retrocues is lessstable, evolving throughoutthe analysis epoch.

Figure 4. Meta-analysis of prospective and retrospective cueing tasks.MEG ROIs derived from meta-analysis activation results. The ROIs arecolored by network membership (following Dosenbach et al., 2007).As ROIs were symmetric across hemispheres, we show the lefthemisphere only.

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Figure 5. Cue effects contrasts.(A) Induced responses infrontoparietal and cingulo-opercular ROIs reveals biphasictime course of control networkactivation. The time–frequency(TF) plots are for the contrast:informative cue minus neutralcue. TF data are averaged overleft and right hemisphere ROIs,as most effects were bilateral,with the exception of the twoplots marked with asterisks(anterior MFG and mid IPSfollowing precues) for whicheffects were left-lateralized.Significant activation clustersare shown in full colorsaturation. Precues give riseto a sustained alpha/betadesynchronization in the leftmid-IPS ( p < .0002) lastinguntil the presentation of thememory array, matching thetime course of alphalateralization in occipital cortex,consistent with a role for left IPSas a proximal control region forthis attentional effect. Rightanterior MFG ( p = .0004, earlycluster; p = .029, late cluster)and bilateral FEF ( p = .0016)are activated in the theta bandearly following the cue. Cingulo-opercular nodes are notactivated with the exception ofa short-lived activation in thepre-SMA immediately followingthe cue ( p = .006). Retrocuesgave rise to an alpha/betadesynchronization in the mid-IPS ( p < .0002), which lasteduntil ∼1 sec postcue, matchingthe time course of alphalateralization. Activations in thetheta and alpha/beta band inthe anterior MFG ( p = .0054,p = .0004) preceded theparieto-occipital effects.Consistent with the pattern infMRI, retrocues did not activatethe FEF but did give rise to abilateral activation in the moreventral precentral ROI ( p =.0036). Retrocues also gave riseto activations in the cingulo-opercular nodes. The pre-SMA was activated immediately following the retrocue in the theta band ( p = .0006) and alsolater in the epoch in the beta-band (∼1.2 sec postcue; p = .006). At this later time point, there was also a bilateral activation in the anterior insula/fOin the theta/alpha band ( p < .0002). (B) Whole-brain activation maps for the contrast [informative cue – neutral cue] projected onto the CARETcortical surface, averaged over the 300–600 msec time period following both precues and retrocues. Only activation belonging to a statisticallysignificant activation cluster is shown. (C) Whole-brain activation maps for the contrast [informative cue – neutral cue] projected onto the CARETcortical surface, averaged over the 1200–1500 msec time period following both precues and retrocues. Only activation belonging to a statisticallysignificant activation cluster is shown.


DISCUSSION

Selection mechanisms are effective in improving WM accu-racy whether cues are prospective, selecting what “getsinto” WM, or retrospective, selecting from within WM.Although much of the previous literature has suggestedthat both effects may be mediated by a common top–down mechanism modulating activity in sensory cortex,mediated by a frontoparietal network (Gazzaley & Nobre,2012), other investigators have suggested that a separatecingulo-opercular network is specifically involved in exert-ing control over the contents of WM (Nelissen et al., 2013;Higo et al., 2011). We addressed this question by record-ing MEG data while participants processed either a precueor a retrocue. The spatial resolution of the source spaceMEG recordings was sufficient to replicate the spatial pat-tern of network activations seen in a meta-analysis of pre-vious fMRI studies, and the high temporal resolution ofthe method allowed us to dissociate the activation timecourse of the two networks.The spatiotemporal pattern of induced responses to

precues and retrocues implicated the frontoparietal andcingulo-opercular networks in different aspects of cogni-tive control. A cross-temporal correlation analysis of thesensor data showed that the brain response to precueshad a sustained component, whereas retrocues gave riseto a response that continued to evolve over the analysisepoch. A source space analysis showed that the fronto-parietal sites associated with precueing were activatedin the early phase of the response following retrocues,but that the cingulo-opercular sites activated later. Com-paring these network dynamics with the time course ofalpha modulation in perceptual cortex, they are consis-tent with the suggestion that the frontoparietal networkis responsible for top–down control over sensory cortex(as reviewed in Gazzaley & Nobre, 2012). We proposethat, consistent with prior hypotheses (Gazzaley & Nobre,2012; Nobre et al., 2004), a common frontoparietal net-work is responsible for endogenously modulating activityin sensory cortex, whether this is to bias sensory process-ing during preparatory attention or to reactivate a sensoryrepresentation during memory retrieval. However, westress that, because we did not use a connectivity analysis,we did not obtain direct evidence that the frontoparietalnetwork was responsible for top–down control.Following retrocues, the cingulo-opercular power in-

creases occurred later in the trial, once the frontoparietaland sensory power changes had subsided. This disparityin activation timing is inconsistent with the hypothesisthat these cingulo-opercular sites directly modulate activ-ity in sensory cortex during top–down control (Nelissenet al., 2013; Higo et al., 2011) but suggests that they areimportant for a separate operation specifically associatedwith retrocueing, as discussed below.The “dual network” account proposed by Dosenbach,

Fair, Cohen, Schlaggar, and Petersen (2008) dissociatesthe frontoparietal and cingulo-opercular networks on the

basis of temporal scale of control operations: Althoughthe frontoparietal network is involved in moment-by-moment adjustment of top–down control based on evolv-ing task requirements, the cingulo-opercular network is aparallel system maintaining task set over longer periods.However, the finding that the cingulo-opercular networkis transiently recruited following retrocues suggests a moredynamic role in ongoing cognitive control. Our results canbe compared with an fMRI study by Ploran and colleagues(2007), in which images were slowly revealed in noise, untilparticipants were able to make a discrimination response.Activity in the frontoparietal network slowly increased as ev-idencewas accumulated, but the cingulo-opercular networkwas activated only at the moment participants made theirresponse. These data suggested that the cingulo-opercularnetwork has a more “downstream” role, acting on evidenceintegrated by the frontoparietal network in an interactionwith sensory cortex. Analogously, we suggest that in our taskthe frontoparietal sites act to retrieve perceptual informa-tion about the cued item, and the cingulo-opercular net-work underpins a downstream stage. This is broadlyconsistent with a “cascade” account of executive function(Banich, 2009), but further work is needed to characterizethe nature of this secondary role for the cingulo-opercularnetwork. Plausible functions include prioritization of theretrieved information to drive the response to the probeitem or inhibition of interfering information (uncuedmemoranda). The latter hypothesis might explain whywe did not observe a similar power increase in the cingu-lo-opercular network before the presentation of the probeitem in the precue condition: If there is only one item inmemory, there is are no other items that might interferewith the cognitive operation performed on the relevantitem (in this case, orientation comparison with the probestimulus).

Our results are also consistent with a conservative viewof the role of attention in memory maintenance. Modula-tions of alpha power in perceptual and parietal cortex area reliable marker for preparatory attention (van Ede, deLange, Jensen, & Maris, 2011; Rihs, Michel, & Thut, 2007).We found both the expected quadrant-specific modulationof alpha power following precues, but also a very similarpattern following retrocues, consistent with recent reportsby Poch, Campo, and Barnes (2014) and Myers, Walther,Wallis, Stokes, and Nobre (2015). Alpha lateralization wassustained following precues, until the presentation of thememory array, but transient following retrocues, peakingbetween 0.5 and 1 sec postcue and then returning to base-line. We also observed alpha lateralization in the retentioninterval as previously reported (Sauseng et al., 2009), butinterestingly this was also not sustained, as might havebeen expected if memory maintenance consisted in sus-tained top–down activation of representations in parietalor sensory cortex (Kiyonaga & Egner, 2012; Awh, Vogel,& Oh, 2006). Instead, over the relatively long retention in-terval in our experiment, there were two periods of signif-icant alpha lateralization: one close after the presentation

Wallis et al. 2031

of the memory array (significant between 0.5 and 0.7sec postcue in the classification analysis) and thenanother that ramped up toward the presentation of thememory probe (see Figure 2E). Intriguingly, alpha later-alization did not also ramp up in readiness for the probeitem following retrocues. We speculate that this may bebecause, in the 1.5 sec between retrocue and probeitem, participants are occupied retrieving and preparingto use the retrocued item, and there is not enough timeto make a preparatory switch in attention back towardthe probe item.

Taken together, these results are consistent with theidea that alpha lateralization in parietal/perceptual cortexduring memory maintenance tracks top–down activation,but that this may be dissociable from maintenance pro-cesses (LaRocque et al., 2014; Lewis-Peacock, Drysdale,Oberauer, & Postle, 2012). Rather than interpreting theretinotopic reactivation representing the cued item fol-lowing a retrocue as biasing of ongoing maintenance ac-tivity, an alternative interpretation is that this reactivationreflects a brief “access” event in which the sensory prop-erties of the cued item are reactivated in order that it canbe prioritized in memory. Various contemporary modelsof WM have adopted a multilevel framework (Oberauer &Hein, 2012; Olivers, Peters, Houtkamp, & Roelfsema,2011), in which, of the set of items in memory, a singleitem can be elevated to a special prioritized state. Sen-sory reactivation following a retrocue may be involvedin moving items into this prioritized state.

Our behavioral data also support this interpretation, asthey were more consistent with retrocues modulating theretrievability of an item in memory (“output gating”) thanwith retrocues changing whether (or with what fidelity)the item was maintained in memory. Both precues andretrocues reduced guess rate and the rate of respondingabout uncued items and also caused a leftward shift inthe entire RT distribution compared with neutral-cue trials,consistent with faster retrieval of cued items. However,precues had a substantial effect on the precision withwhich the cued items were represented, whereas precisionwas only marginally modulated by retrocues. This is notconsistent with the proposal that retrocues act primarilyby protecting cued items from gradual decay (Pertzov,Bays, Joseph, & Husain, 2012), as were this the case wewould expect to have seen a more substantial precision ad-vantage following retrocues. An alternative explanation interms of maintenance processes is that retrocues protectitems from “sudden death” during the retention interval,which might explain the difference in guess rate. However,in Murray et al. (2013) retrocues boosted performanceeven if compared against a condition in which the probeitem was presented early, at the same time as the retrocue,implying that the retrocue benefit could not depend onlyon protecting items from forgetting during the remainderof the retention interval. Finally, in the current study, retro-cues almost completely abolished the effect of nontargetitems on behavior, implying that retrocues can prevent re-

trieval errors in which the wrong item is selected to guidebehavior—that is, they mitigate against errors in outputgating. These aspects of the behavioral data are all consis-tent with retrocues facilitating output gating, as opposed tooptimizing memory maintenance.In summary, although selection from WM following a

retrocue involves a similar top–down modulation of sen-sory and parietal cortex and a similar pattern of frontopa-rietal network activation as does preparatory attention, ourdata suggest that control over WM is not identical with top–down attention acting to bias memory maintenance activityin sensory and parietal cortex. Sensory reactivation wastransient, and there was no evidence for sustained biasingof maintenance activity following cues. Instead we wouldsuggest that the frontoparietal network mediates top–down control over sensory cortex, which can be recruitedeither to bias perception (attention) or to retrieve per-ceptual content associated with WM. We found, in linewith previous studies (Nelissen et al., 2013; Higo et al.,2011), that the second cingulo-opercular network wasspecifically recruited by retrocues, but not precues.However, the activation timing suggested it was not di-rectly involved in control over sensory representations,as prior studies have suggested. The precise role of thecingulo-opercular network in cognitive control remainsto be elucidated. Broadly, precues facilitate input gatingwhereas retrocues may facilitate output gating of mem-ory (Hazy, Frank, & O’Reilly, 2007). The frontoparietalnetwork has a role in both input and output gating, butthe cingulo-opercular network may be specifically asso-ciated with output gating.

Acknowledgments

This work was funded by Wellcome Trust studentships to G. W.and H. C., MRC fellowship MR/J009024/1 to M. S., a WellcomeTrust Equipment Grant to A. C. N. to support OHBA, the Na-tional Institute for Health Research (NIHR) Oxford BiomedicalResearch Centre based at Oxford University Hospitals TrustOxford University, and an MRC UK MEG Partnership grant(MR/K005464/1). The authors would like to thank Nils Kolling,Nicholas Myers, and Franz Neubert for helpful discussions andSven Braeutigam, Henry Luckhoo, Diego Viduarre, and AdamBaker for their assistance with the MEG analysis.

Reprint requests should be sent to George Wallis, Oxford Cen-tre for Human Brain Activity, Warneford Hospital, Oxford, OX37JX, UK, or via e-mail: [email protected].

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