visual working memory - awh/vogel lab · memory,1–3 a system that allows us to temporarily keep...

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Visual working memoryIrida Mance and Edward K. Vogel∗

Visual working memory (VWM), the system of storing, manipulating, and utilizing,visual information is fundamental to many cognitive acts. Exploring the limitationsof this system is essential to understand the characteristics of higher-ordercognition, since at a basic level, VWM is the interface through which we interactwith our environment. Given its important function, this system has become a veryactive area of research in the recent years. Here, we examine current models ofVWM, along with the proposed reasons for what limits its capacity. This is followedby a short description of recent neural findings that have helped constrain modelsof VWM. In closing, we focus on work exploring individual differences in workingmemory capacity, and what these findings reveal about the intimate relationshipbetween VWM and attention. © 2013 John Wiley & Sons, Ltd.

How to cite this article:WIREs Cogn Sci 2013, 4:179–190. doi: 10.1002/wcs.1219

INTRODUCTION

Our ability to acquire, maintain, and use infor-mation from our environment relies on working

memory,1–3 a system that allows us to temporarilykeep information ‘in mind’ while we manipulate or actupon it. Most theories of working memory proposethat this system is limited in its storage capacity,and relies on separable verbal and visual storagesubsystems.4 The visual component, known as visualworking memory (VWM),a supports the maintenanceof a small amount of visual information over a shortperiod of time. Considering the important role thatVWM plays in both perception5 and higher-levelcognition,6 understanding the limits of its processinghas been the topic of sizable research efforts in thepast several decades.

The most notable characteristic of workingmemory is its limited capacity. Both behavioral andneural studies have suggested that VWM capacityis limited to about three to four items. The firstindications of this highly limited capacity wereprovided by Sperling’s7 classic experiments on iconicmemory. In these studies, participants were shownbrief displays of alphanumeric characters, and wereinstructed to immediately report as many items asthey could remember. When asked to report all of theitems in the display, Sperling found that participants

∗Correspondence to: [email protected]

Department of Psychology, University of Oregon, Eugene, OR, USA

could report only about four or five items on average.However, there are a few potential limitations of thisestimate of VWM. First, participants were requiredto either vocalize or write down their responses, andthis reporting process may have underestimated howmany items were stored. Second, the memoranda wereletters and numbers, which may have encouragedparticipants to use a verbal code to store the items,and thus performance may have also relied on verbalworking memory mechanisms.

The work of Philips8 overcame some of thelimitations of the Sperling studies. Instead of whole orpartial report, Philipps showed subjects two hard-to-verbalize checkerboard patterns that were separatedby a varying interstimulus interval and asked thesubject to report whether the two checkerboardswere the same or different. This change detectiontask revealed that performance was near perfect forvery short intervals (e.g., <250 ms), then declinedprecipitously over the following several hundredmilliseconds. He manipulated the complexity ofthe memoranda by increasing the number of cellsin the checkerboard, and found that the morecomplex arrays showed more substantial declinesin performance. While these results also revealthe capacity-limited nature of VWM, the use ofcomplex arrays made it difficult to establish an easilyquantifiable estimate of VWM capacity.

Using a similar approach, Luck and Vogel9 useda variant of Phillips’8 change detection paradigm10

with displays that contained simple and highly

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Participants were shown a memory array for 100 ms, followed by a900 ms retention period, and test array. The test array was eitheridentical to the original memory array, or differed by one item.(b) Average accuracy as a function of set size. Performance was veryhigh for set sizes 1–3, and began to systematically decrease as the setsize increased beyond three items.

discriminable colored objects. In this procedure (seeFigure 1), participants are briefly shown arrays ofcolored squares, which disappear for about 1 second(retention interval). Following the retention interval,a test array is presented that either perfectly matchesthe colors of the initial memory array on half ofthe trials, or contains one item that changed coloron the other half of the trials. By systematicallyvarying the set size of the memory array tocontain from 1 to 12 items, Luck and Vogel,like Sperling,7 found capacity to be between threeand four items. That is, participants were nearperfect at detecting changes for arrays containingone, two, or three items, but then systematicallydeclined in performance for larger array sizes.Importantly, this estimate was unaffected by a verbalmemory load9,11 indicating that verbal storage wasnot contributing to performance. Although there iscontinuing debate regarding the precise nature ofthese capacity limits (as will be discussed below),these experiments did demonstrate that the storage

capacity of VWM is limited to a very small amount ofvisual information.

Given the importance of working memory forperforming many cognitive tasks, there are numerousactive research areas exploring a wide range of topicsrelated to VWM. For the present review, however, ourmore feasible goal is to concentrate on three core issueswithin current work on VWM. First, we will discussthe ongoing debate regarding the nature of VWMcapacity limits. Second, we focus on new researchestablishing neural measures of VWM capacity, andhow these findings relate to models of VWM. Finally,we describe new findings that are highlighting theimportant relationship between VWM and selectiveattention, and what these two concepts reveal aboutindividual differences in VWM capacity.

CAPACITY LIMITS: NUMBEROR RESOLUTION?

There has been an ongoing debate over how to bestcharacterize the limits of VWM. Luck and Vogel9

interpreted their initial findings as evidence thatcapacityb is determined by a maximum number ofitems that individuals could store simultaneously. Theview that a maximal number of items stored is whatlimits capacity is often termed the ‘slot’ or discreteresource model of VWM capacity. According tostrong versions of this theory, the capacity of workingmemory is determined by the number of ‘slots’available, and the resolution of the representationsthat occupy each slot is relatively stable.9,12 Incontrast, pure flexible resource models propose thatVWM resources are distributed in a continuousmanner, and may be flexibly shared between as fewor many representations as necessary, with a tradeoffin the precision of representations. Consequently, thelimiting factor for performance in these models is nothow many items are stored, but rather how muchresource is devoted to each representation. Thus, thereason that performance suffers as more items arestored is because each representation is allotted asmaller portion of a finite resource.13

To test whether the fidelity of representationschanges as a function of the number of items stored,Wilken and Ma14 applied a signal detection approachto a change detection task. According to their view,VWM representations are inherently noisy, and thegreater the number of representations that must bemaintained, the greater the amount of noise in thesystem. Following this logic, Wilken and Ma suggestedthat the loss of accuracy observed at higher set sizesis a consequence of the increase in the magnitude ofnoise, rather than a constant capacity limit. To test

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FIGURE 2 | (a) Recall procedure used by Zhang and Luck.12 Participants were shown a memory array, and following a 900 ms retention period,reported the color of a probed item by clicking on a color wheel. (b) Predicted results of Zhang and Luck’s mixture model. The model combines aGaussian distribution of responses for stored items (dashed blue line), with a uniform distribution of responses for items that were not stored (dashedred line). On average, performance will reflect responses to both stored and not stored items, resulting in a mixture of the two distributions (solidblack line). (c) Results for set size 3 and 6; though the probability of reporting a color that was not stored increased with set size (reflected bydifference between the tail ends of the distributions), the standard deviation of the distributions did not, indicating no change in resolution.

this hypothesis, they employed a continuous reportprocedure in which participants were given a memoryarray, much like the one used by Luck and Vogel,9

but were required to report the exact color of acued item by adjusting a color wheel. The results oftheir experiments indicated that the accuracy of theparticipants’ responses decreased as the number ofmemory items increased, indicating that the fidelity ofVWM representations decreased as set size increased.

However, one potential problem with theWilken and Ma findings is that they did not test thepossibility of subjects storing a small number of fixed-resolution representations, and randomly guessingwhen the tested item was not in memory. Using thesame continuous report procedure, Zhang and Luck12

used a mathematical technique to isolate participants’responses as either stored representations or randomguesses. By analyzing the distribution of responseerrors, they were able to determine whether an itemhad been stored in VWM, and also the resolution ofthe stored representations. The results indicated thatwhen the memory array contained one to three items,guessing rate was fairly low. However, as set sizeincreased, so did the proportion of random guesses,suggesting that participants were indeed able to store

no more than three items in VWM. Though thenumber of stored items did not vary by set size,the resolution of representations, as indexed by thestandard deviation of responses, did. Zhang and Luckcould account for this last finding by incorporatingan averaging process to their model. According tothis ‘slots + averaging’ theory, working memory iscomprised of three discrete slots, and if the numberof to-be-remembered items is below capacity, eachslot can store an independent representation of eachitem. These independent copies can then be averagedtogether to yield more precise representations ofmemory items (Figure 2).

This leads us to an important assumption ofthe model: the precision of VWM representationscan never exceed that of a single slot. The reasonthat precision is higher for fewer than three storeditems is that multiple slots, each with an independentsource of error, are averaged together to yield amore precise representation. When individuals aremaintaining about three items, the precision of eachrepresentation will reflect the noise level of each slot,and thus, the standard deviation measure of precisionshould plateau when memory capacity is reached.This is exactly what Zhang and Luck observed.



However, others have suggested that this plateauin precision is an artificial phenomenon, resultingfrom individuals accidentally reporting the colorfeature of a non-target item.13 Though more recentexperiments have suggested that a likely reason forwhy a clear resolution cutoff may not always befound is because of substantial individual differencesin working memory capacity. Importantly, Andersonet al.15 demonstrated that when these individualdifferences are accounted for, the precision of VWMrepresentations reaches a stable asymptote once anindividuals’ capacity has been reached.

Besides these two viewpoints, a third class ofmodels propose that VWM capacity is limited bothby a maximal number of items, as well as a limitedamount of resolution resources that must be sharedamongst the actively represented objects. For example,Alvarez and Cavanagh16 found that capacity estimatesare negatively related to stimulus complexity. In thisset of experiments participants were shown items thatcould range from simple colored squares, to verycomplex objects such as Chinese characters, or shadedcubes. They operationalized the information load ofeach stimulus using a visual search paradigm, withsearch rate being used as a metric of informationload. The results indicated that information loadwas inversely related to memory capacity for eachclass of items, such that individuals could rememberabout four or five simple squares, and approximatelyone complex shaded cube. Alvarez and Cavanaghinterpreted these findings as evidence for a hybridbetween the discrete- and flexible-resource models.Meaning that the amount of resource allocated toeach representation will limit capacity, though thereis also an upper bound to how many items maybe represented, even when information load is verylow. A potential caveat to these findings, however,is that as the complexity of the stimuli increased,so did the similarity between the memorandum andtest. That is, for more complex items the perceptualdifference between the item in memory and thechanged item presented at test is much smaller thanthat for simple items. Awh et al.17 showed that whenthis similarity confound is controlled for, memorycapacity did not vary as a function of stimuluscategory. In these experiments, when the similaritybetween the memory and test array was high, suchas when a Chinese character changes into a differentChinese character, individuals were pretty inaccurateat detecting that a change has occurred. However,when similarity between the two arrays decreased,such as when a Chinese character was changedinto a colored cube, change detection performanceincreased, and memory capacity was again estimated

to be about four items. These results suggest thatthe number of represented items does not decreasewith object complexity, but rather that the resolutionneeded for an accurate judgment during a fine-grainedcomparison increases.18

Clearly there are limits to the amount ofinformation that may be simultaneously representedin VWM. Unfortunately, there is still no definitiveagreement in the literature on whether these capacitylimits arise due to a fixed item-limit, or an exhaustedcontinuous resource. Both general classes of models doa fairly good job of explaining the extant behavioraldata. Consequently, we suggest that research onthe neural bases of item limits may provide fruitfuland informative new constraints on extant theories,yielding neurophysiologically plausible models ofVWM processes.

NEURAL MEASURES OF VWM

While long-term memory representations are sus-tained through relatively permanent changes at thesynaptic level, VWM representations are maintainedthrough the sustained firing of neurons.19 This distinc-tion between long-term memory and VWM is oftendifficult to determine using behavioral tasks becauseperformance could depend on a range of processes thatextend well beyond the ‘online’ maintenance demandson VWM. As a result, neural measures of VWM pro-cessing are critical to understanding the mechanismsinvolved in the active maintenance of information.

Over the past few decades, primate studieshave been considerably successful in revealingwhich brain areas facilitate working memory. Forexample, single-unit recordings have shown that whenmonkeys perform a delayed-match-to-sample task, inwhich they have to maintain one representation inmind and match it to a subsequent test stimulus,neurons that initially fired to a to-be-rememberedstimulus will continue to fire above their baselinefiring rate throughout the retention period.20,21

This phenomenon, referred to as delay activity,has been observed in a wide range of corticalareas, and is particularly prevalent in three keyareas: the prefrontal cortex (PFC), the posteriorparietal cortex, and inferior temporal cortex.20,22,23

Importantly, the activity observed in each of theseareas is often specific to the type of informationthat is currently being maintained. For example,some evidence suggests that delay activity in theposterior parietal cortex reflects the spatial locationof encoded information,24–26 while activity in theinferior temporal cortex is more important forrepresenting the identity of representations.21,27



The prefrontal areas, however, seem to play avital role in integrating the information associatedwith VWM representations,22 likely through theintrinsic connectivity within the PFC.28 Though theprecise nature of interactions between posterior andprefrontal areas is far from being well understood,substantial evidence suggests that feedforward andfeedback communication between these areas is vitalfor the maintenance of VWM representations.25,29

In addition to online maintenance, prefrontalregions have also been shown to be important for theretrieval of representations from long-term memory.30

For example, Rainer et al.22 showed that activityin the PFC toward the end of a retention periodbegins to reflect an anticipated stimulus (previouslyassociated with the shown stimulus), rather thanthe initially presented stimulus. Experiments usinghuman observers have yielded results that areremarkably similar to these findings. In a fMRIstudy, Ranganath et al.31 observed that activity incategory-selective regions of the inferior temporalcortex begins to increase when participants performedeither a working memory or delayed paired associatestask. Furthermore, activity in the hippocampus andanterior prefrontal cortes was especially enhancedduring the retrieval stage of the paired associatestask. Overall, both experimental evidence,20,23,30,32–35

and biophysical models of working memory36,37

support the view that sensory information is mainlypreserved in posterior regions, while the PFC exertstop–down control over these more posterior areas,possibly providing a mnemonic code for relevantrepresentations or task rules.38,39

Along with revealing the biological underpin-nings of VWM processes, single-unit studies arebeginning to provide information on why behavioraldata can often support multiple models of work-ing memory. Using a change detection task whilesimultaneously recording from the parietal and frontalcortex, Buschman et al.40 obtained results consistentwith a hybrid of both discrete- and flexible-resourcemodels of working memory. In these experiments,monkeys were presented with visual displays of itemsthat were either bilaterally or unilaterally distributed.When more items were presented on the same sideas a subsequently tested item, performance began todecline, indicating that resources were shared amongitems presented in one visual hemifield. However, ifmore items were added to the side that was contralat-eral to the subsequent target item, performance wasnot affected, suggesting a discrete, slot-like capacitylimit between the left and right hemifields. In all, theamount of information that the monkeys were ableto maintain for each visual hemifield increased until

about two items, resulting in a capacity of abouttwo for each hemifield, and a capacity limit of aboutthree to four items in total across both hemispheres.Although human subjects do not appear to show thesame performance deficits based on the lateraliza-tion of stimuli,41,42 neuroimaging experiments havealso provided support for aspects of both discrete-and flexible-resource models of VWM. More specif-ically, the findings of Xu and Chun43,44 suggest thatneural activity in the inferior intra-parietal sulcus (IPS)reflects the number of items stored in memory, regard-less of item complexity. After a four-item limit hasbeen reached, activity in this area begins to plateau,indicating a fixed capacity on the number of itemsthat may be represented. In contrast, activity in thesuperior IPS and lateral occipital complex fluctuatedwith the number and complexity of objects that indi-viduals were able to hold in memory, suggesting amore flexible allocation of resources.

One weakness of neuroimaging experiments,however, is they have a limited temporal resolution,which makes it difficult to assess whether theobserved activity reflects the active maintenance ofVWM representations throughout the trial period, orsome kind of retrieval process during the responsephase. Though some experimenters have attempted toaddress this issue by increasing the retention intervalduring the scanning period,43,45 recent findings suggestthat with longer delays performance may begin todepend on contributions from long-term memory.46

For this reason, electrophysiological measures ofneural activity are especially well suited for assessingexactly how many representations individuals areactively maintaining in an ‘online’ state. For example,Vogel and Machizawa47 found that event-relatedpotentials (ERPs) time-locked to the onset of abilateral display of items, show a negative waveover posterior electrode sites contralateral to theposition of the remembered item (see Figure 3). Thisnegativity, known as the contralateral delay activity(CDA), persisted throughout the retention intervaluntil the test was presented. The amplitude of theCDA increased as a function of the number ofto-be-remembered items, but reached an asymptoteat approximately three items. Moreover, the pointat which the CDA reached an asymptote wasstrongly related to each individual’s working memorycapacity, such that individuals with lower VWMcapacity reached a plateau at lower set sizes thanhigher capacity individuals. These and follow-upexperiments48 have found that the CDA is unchangedby the number of locations monitored, task difficulty,or the size and spacing of memory items, indicatingthat the CDA is a pure measure of the number of



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FIGURE 3 | (a) The contralateral delay activity (CDA) as shown by Vogel and Machizawa.47 In this task the contralateral negativity reflects theattended hemifield for a bilateral array of items. ERPs were time-locked to the onset of the memory array; posterior electrode sites ipsilateral andcontralateral to the attended locations were averaged together. (b) Mean amplitude of the CDA as a function of memory array size. Note thatamplitude plateaus at three items. (c) Correlation between an individual’s memory capacity and their CDA asymptote.

items that individuals are able to actively maintain inVWM.

INDIVIDUAL DIFFERENCES INCAPACITY AND ATTENTION

Individuals show stable and systematic variability inVWM performance. These individual differences inVWM capacity are an important aspect of cognitiveability because they predict differences in intellectualability, such as fluid intelligence and scholasticachievement.6 Accordingly, a significant question fora VWM researcher is why these individual differencesexist in the first place, and how best to characterizethe difference between a high- and low-capacityindividual.

A dominant and intuitive model of individualdifferences in VWM is that high-capacity individualssimply have more ‘slots’ in memory than low-capacityindividuals. However, growing evidence has begun tosupport an alternative explanation for these capacitydifferences. That is, rather than differing in termsof available storage space, high- and low- capacityindividuals may instead differ primarily in their abilityto use attention to control what is stored in workingmemory. In fact, a number of reports on verbalworking memory have found that high and lowWM capacity individuals differ in their performanceon an anti-saccade task, a task thought to reflectexecutive attention ability.49–51 In regards to VWM,this suggest that high-capacity individuals may bebetter at restricting the inputs to VWM to include onlythe most task-relevant items within the environment,whereas low-capacity individuals may be poorer at

filtering out irrelevant items from being unnecessarilystored in VWM.52–55

Further support for this controlled attentiontheory comes from reports showing that many ofthe cortical regions implicated in the formationand maintenance of VWM representations arealso important for controlling the allocation ofattention.56–60 Indeed, some have argued that thelimiting factor in short-term memory tasks is the scopeof attention.61,62 Given this tight coupling betweenattention and VWM, an important question concernshow the ability to control attention is related tomemory capacity.

Both electrophysiological and neuroimagingstudies have provided compelling demonstrationsof individual differences in the ability to filterirrelevant information. In one such study, Vogelet al.55 instructed participants to restrict which itemsthey were to hold in memory by asking individualsto select and store only the red items in memoryarrays that contained both red and blue items(see Figure 4). Using the CDA amplitude as ameasure of filtering efficiency, Vogel et al. foundthat higher capacity individuals were much betterat keeping irrelevant objects from being stored thanlow-capacity individuals. For example, when low-capacity individuals were presented with displays thatcontained two red and two blue items, the amplitudeof the CDA more closely resembled that of fourrelevant items presented on the screen; while for high-capacity individuals, CDA amplitude was more similarto that for two items, suggesting much more efficientfiltering. Importantly, these results show that undermany circumstances, low-capacity individuals actuallyhold more information in VWM than high-capacity



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FIGURE 4 | (a) Filtering task used by Vogel et al.55 Each trial began with an arrow cue indicating which side of the memory array was to beattended and compared to a test array. The memory array could contain two or four relevant items (e.g., two or four red items), or two relevant andtwo irrelevant items (e.g., two red and two blue, as shown). (b) CDA time-locked to the memory array, split between high- and low-capacityindividuals. For high-capacity individuals, the amplitude of the CDA for displays containing distractors resembled that for displays containing only tworelevant items, while for low-capacity individuals, activity was similar to that of four relevant items. (c) Correlation of filtering efficiency (as measuredby the ability to exclude irrelevant items) and each individual’s memory capacity.

individuals, but it is simply the wrong information forthe task.

McNab and Klingberg found similar results in arecent neuroimaging experiment.54 Here, individualswere given cues that indicated whether an upcomingmemory array would contain irrelevant distractors ornot. When filtering was required, activity in both thePFC and basal ganglia increased, and this increase inactivity was positively related to individual VWMcapacity. Furthermore, the researchers examined

activity in the posterior parietal cortex, an area thathas been shown to be sensitive to the number of itemsstored in memory.44,45 When comparing the storage ofdisplays that contained only target items, and displaysof targets and distractors, similar to Vogel et al.,55

the authors found that lower capacity individualswere more likely to store irrelevant distractoritems.

A number of unresolved questions emergefrom the above findings. One issue concerns why



low-capacity individuals fail at remembering displayswithout distracters. One possibility is that workingmemory tasks rely on the ability to maintain distinctrepresentations throughout a retention period, andlater compare the memorandum with a test array oritem. If one concedes that VWM capacity is indeedlimited, then it’s possible that the items in a displaythat exceed an individuals’ capacity are tantamount tobeing distractors. These extraneous items, which alsocompete for representational space, may hinder theability select and compare only the relevant items tothose in the test array; much in the same way that task-rule designated distractors can interfere with the abil-ity to filter and selectively store only the task-relevantitems. Though further work needs to explore this pos-sibility, the present data suggest that the ability to filterirrelevant information is tightly related to the amountof relevant information that can be stored in VWM.

A second issue concerns why low-capacity indi-viduals fail to filter out irrelevant information in thefirst place. Recent experiments by Fukuda and Vogel63

have shown that differences in filtering abilities likelystem from how efficiently attention can be allocatedto the task-relevant items, and disengaged from theirrelevant distractors. The authors explored this issueusing an attention capture paradigm. In this taskobservers are told to attend to a particular target item,and are instructed to report on a certain property ofthis item. For example, individuals may be asked toreport the location of the gap on a red-C target imbed-ded in a display of distractors. On a minority of trials,an irrelevant item that matches one of the definingfeatures of the target, such as its color, will be flashedbriefly somewhere around the periphery of the display.The results typically show that performance suffersthe most when this irrelevant distractor matches thetarget by one of its defining properties (e.g., samecolor), and marginally influences performance if itdoes not. By using this task to investigate differencesis attention allocation between high- and low-capacityindividuals, Fukuda and Vogel demonstrated that low-capacity individuals are much more likely to have theirattention captured by the irrelevant flanking items.Subsequent electrophysiological experiments demon-strated that the irrelevant flankers also captured theattention of the high-capacity individuals.64 However,these individuals were much faster at disengaging theirattention from the irrelevant flankers and returningto the target item. The low-capacity individuals, onthe other hand, were much more likely to be cap-tured by irrelevant items, and have their attentionlinger at those locations. What this indicates is thatboth high- and low-capacity individuals were capturedby irrelevant information. Though, the high-capacity

individuals were much quicker at disengaging theirattention from irrelevant items, and reengaging theirattention to the task-relevant items.

Overall, the results of both filtering and atten-tion capture experiments suggest that individual

BOX 1

FORGETTING FROM VWM

Though it is often noted that individuals canremember less items with longer retentionintervals, the mechanisms underlying thisoccurrence remain relatively unknown. Onenoteworthy assessment by Zhang and Luck65

found information can be held in VWM forat least 4 seconds, with little loss in either thenumber or precision of representations. Whilethe probability of losing items did increasefollowing this interval, the items maintainedexhibited little change in either their strength orprecision. Thus suggesting that a primary reasonof why information is forgotten is due to anabrupt termination of representations, insteadof a gradual decay over time.

A related question concerns how we failto maintain the feature information of VWMrepresentations. Recent findings by Fougnie andAlvarez66 suggest that the features of objects failindependently of one another. Here, participantswere shown colored oriented triangles, andfollowing a delay, reported both the orientationand color of a probed triangle. The resultsshowed that when individuals failed to representeither the color or orientation of the testitem, they were not any more likely to losethe color or orientation of that particularitem, suggesting that the features werelost independently. Subsequent experimentsshowed that this loss was contingent on thedegree of overlap between feature dimensions.When participants stored height and widthinformation, two dimensions that likely draw oncommon neural populations, the two featureswere lost together. However, though the loss oftwo non-overlapping feature dimensions arguesagainst the view that perfectly integrated objectsare the units of VWM, it is possible that themethod of report may have encouraged aloose binding of representations. Although thenature of binding is still a highly debated topic,investigations on the mechanisms that integrateVWM representations together promise to beinteresting area of research.



differences in VWM tasks likely reflect how effectivelyindividuals can control their working memory stor-age, rather than consistent differences in the numberof items stored. More specifically, individuals seem todiffer in their ability to selectively attend to and fillmemory with items that are task relevant, as opposedto salient distractors53,55 (Box 1). A somewhat coun-terintuitive conclusion from these findings is thatlower capacity individuals are storing more infor-mation than high-capacity individuals, though suchinformation is irrelevant to the goal at hand (Box 2).

BOX 2

UNITS OF VWM

One central question in determining VWMcapacity concerns whether representations arestored as integrated objects,9,11 or whether theyare stored as fragmented features.67,68 Luck andVogel9 explicitly tested this issue by requiringparticipants to remember either the color, theorientation, or both color and orientation of barstimuli. If the number of features limit capacity,then individuals should have been about twiceas accurate in the single feature conditions thanin the dual feature condition. However, this wasnot what Luck and Vogel found. Instead, partic-ipants were just as accurate when rememberingboth the color and orientation of items, as whenremembering either feature alone.

These conjunction results are also consis-tent with an independent feature store modelof VWM capacity, in which each feature type(e.g., color, orientation, etc) has its own avail-able capacity.69 To test this alternative, Luckand Vogel ran a separate experiment in whichthe memory array was made up of dual-coloredobjects. If the two features compete for thesame VWM resources, then accuracy should havebeen worse in the color-color conjunction con-dition than in the single color condition. Yet,participants were again just as accurate in thetwo color condition. Though this dual-color, pureobject benefit has not been replicated by otherlabs,67,68,70,71 we have yet to find complete cost,the independent feature model would predict.That is, the dual color conjunction cost is almostnever equivalent to that of the same number ofseparately presented objects. Indeed, numerousrecent behavioral and neurophysiological exper-iments have found at least some amount ofobject-based advantage for VWM representa-tions even with two attributes from the same

feature space.71–73 Thus, while there may be acost to representing multiple features within asingle dimension, to our knowledge, there iscurrently little evidence showing that capacity isdetermined solely by the number of featureswith no object benefit. Indeed, while theindependent feature store model appears to befairly compelling, direct evidence supporting thismodel has never been reported. Specifically, ifeach feature dimension has its own separatecapacity, one would expect greatly improvedVWM performance for heterogeneous arrays ofitems containing different critical features (e.g.,four colors, four oriented bars, four shapes,etc.). To our knowledge, no one has ever foundsuch a separate feature benefit. So, which is it?Objects or features? To us, the current weight ofevidence favors at least a ‘weak’ object benefit inwhich there is some, but not complete benefitsof objecthood on VWM performance.

CONCLUSION

Most work on VWM suggests that this multi-component system is capacity limited. While thereis considerable debate over the exact number or res-olution limits, most researchers will agree that thereare substantial constraints to the amount of informa-tion that may be retained in VWM. Here, we focusedon the major theories of VWM, and the proposedfactors that limit its processing. Though there arestill many aspects of VWM models that are contro-versial, neural measures are laying the groundworkfor agreement over physiologically plausible theoriesof VWM capacity. Neural components that provide‘online’ measures of the amount of information thatindividuals are concurrently representing appear to beespecially promising ways of measuring the limits ofmemory capacity. Components such as the CDA seemto be particularly powerful in this regard, since theycan account for both overall estimates of VWM andindividual differences in working memory capacity.

An important and often neglected aspect ofVWM capacity is its relation to cognitive process-ing. Though this relationship has been thoroughlyexplored in the verbal domain, research on visualmemory and intelligence has lagged behind. The fewrecent studies that have begun to explore this rela-tionship have indicated that the intimate connectionbetween working memory and attention seems toplay a key role in this relationship. Distinguishingbetween where these two processes converge anddiverge will be an important part of understanding



higher-order cognition, since, at a fundamental level,these capacity limitations mediate our ability to inter-act with the world.

NOTESaHere we use the term VWM to refer to the temporaryretention of visual information that just perceived orretrieved from long-term memory. However, the term

visual short-term memory would have been equallyappropriate. These two terms are often used inter-changeably, and often refer to the same memoryprocess.

bIn the present article, we define capacity as theamount of visual information that can be temporar-ily stored for use in a cognitive or sensory-guidedtask.

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