Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Apr 1.
Published in final edited form as: Psychon Bull Rev. 2013 Apr;20(2):348–355. doi: 10.3758/s13423-012-0354-3

The Benefit of Forgetting

Melonie Williams 1, Sang W Hong 2, Min-Suk Kang 1,3, Nancy B Carlisle 4, Geoffrey F Woodman 1
PMCID: PMC3593955  NIHMSID: NIHMS426276  PMID: 23208769

Abstract

Recent research using change-detection tasks has shown that a directed-forgetting cue, indicating that a subset of the information stored in memory can be forgotten, significantly benefits the other information stored in visual working memory. How do these directed-forgetting cues aid the memory representations that are retained? We addressed this question in the present study by using a recall paradigm to measure the nature of the retained memory representations. Our results demonstrate that a directed-forgetting cue leads to higher fidelity representations of the remaining items and a lower probability of dropping these representations from memory. Next, we show that this is possible because the to-be-forgotten item is expelled from visual working memory following the cue allowing maintenance mechanisms to be focused on only the items that remain in visual working memory. Thus, the present findings show that cues to forget benefit the remaining information in visual working memory by fundamentally improving their quality relative to conditions in which just as many items are encoded but no cue is provided.

Research on directed forgetting in long-term memory has supported the claim that we can volitionally select which representations are retained in memory (Anderson & Green, 2001; Anderson & Levy, 2009; Johnson, 1994; MacLeod, 1999). When participants are instructed to forget items on a list, recall for those words are significantly lower than the other words on the studied list. Williams and Woodman (2012) recently examined how directed-forgetting cues change what is maintained in visual working memory. In that study, participants were always shown six colored squares to remember. On a subset of trials, they were provided a cue during the retention interval indicating that they could forget three of the six squares (i.e., a directed-forgetting cue). These cues resulted in superior performance at detecting changes relative to baseline trials without a cue. Furthermore, evidence from an event-related potential (ERP) experiment indicated that directed-forgetting cues improve change detection for the remaining information in working memory by denying task-irrelevant information access to limited-capacity maintenance processes. This denial of access allows maintenance mechanisms to be focused on the remaining representations in visual working memory. If this explanation is correct, then focusing maintenance on the task-relevant representations in visual working memory could either be 1) increasing their fidelity relative to conditions in which a set of memoranda are shown and no cues about future relevance are shown or 2) preventing the remaining representations from being dropped from visual working memory (Zhang & Luck, 2009). In the present study, we sought to determine the source of the benefits conferred by cues to forget information in visual working memory.

To determine how directed-forgetting cues benefit the representations in visual working memory that remain task relevant, we needed to measure more detailed aspects of the nature of the representations than the accuracy-based measures provided by change-detection tasks. Recently researchers have developed techniques for measuring the precision and number of representations held in visual working memory (Bays & Husain, 2008; Zhang & Luck, 2008). For example, Zhang and Luck (2008) showed how mixture modeling could be used in conjunction with a recall paradigm to simultaneously measure the number and resolution of representations that are stored. Using this approach they turned to the question of what happens to items stored in visual working memory across time; do they gradually decay or are the representations suddenly lost (i.e., sudden death, Zhang & Luck, 2009)? Their findings showed that information is lost from visual working memory because items are dropped or suddenly die rather than gradually decaying. In the present study, we used the same methodological approach (i.e., a recall task and mixture modeling) to determine whether a cue instructing participants to forget some information benefits the remaining representations. This could come about by either increasing the resolution and fidelity of the remaining items, because maintenance mechanisms can be focused on them relative to conditions in which this cognitive resource is spread across more items, or by preventing their sudden death and total loss. Finally, it is possible that focusing maintenance relative to spreading it more thinly across more items might have both of these effects.

Experiment 1

The goal of Experiment 1 was to measure the nature of memory representations following a cue to forget other items. We used a cued recall procedure similar to that of Zhang and Luck (2008). Observers were asked to remember one or two colored squares. As illustrated in Figure 1, stimuli were briefly presented, followed by a short retention interval, and then a color wheel with all possible colors and a box around one location marking the item that needed to be reported. Three trial types occurred during this experiment. On 1-object trials we presented only one colored square in the memory array. On 2-object trials, two colored squares were presented during the memory array. Finally, on 2-cued trials, two colored squares were presented in the memory-array and a cue to forget one square appeared during the retention interval. During test, only one square was probed on all three types of trials and observers made a response by clicking on the color wheel.

Figure 1.

Figure 1

Open in a new tab

Example stimuli and three trial types in Experiment 1. The top panel shows a 1-object trial, the middle panel a 2-object trial, and the bottom panel a 2-cued trial.

If focusing maintenance following a cue to forget some information benefits the remaining representations by improving their fidelity, then we should observe better precision for the tested representation during 2-cued trials compared to 2-object trials. This would be observed as a smaller standard deviation (σ) of the distribution of responses around the actual color of the tested object. However, if cues to forget prevent the sudden death of the remaining memory representations that receive the full benefit of focused maintenance, then the probability of having retained the tested item on the 2-cue trials relative to the 2-object baseline should be higher. This would be observed as a higher Pm (probability of memory) due to the fewer pure guesses distant in color space from the color actually shown in the memory array. Finally, it is possible that a cue to forget benefits the remaining representations in both these ways, improving their fidelity and probability of being retained in visual working memory across the entire retention interval.

Method

Participants

Twenty volunteers 18–35 years old reported normal or corrected-normal vision and provided informed consent prior to the experiment.

Stimuli and Procedure

We presented stimuli using MATLAB (Mathworks, MA) and the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) on a calibrated CRT display (Sony Trinitron Multiscan E540) in a near dark room so that the display was the predominant light source. Spectral power distributions of the red, green and blue phosphors were measured using a spectroradiometer (Ocean Optics, Model USB4000). The relative light level of each gun at every digital value (256; 28 levels) was measured with a Minolta colorimeter (model CA-100). The effective viewing distance was 90 cm.

The stimuli were either one or two colored squares (each 0.64° × 0.64° of visual angle) presented on a gray background (37.7 cd/m2). Colors of the stimuli were randomly chosen from 180 colors, defined within CIE-Lab color space. Stimulus locations were randomly selected from 12 equally spaced positions on a virtual circle, whose radius was 2.5° from the central fixation. For 2-object trials, both locations were selected randomly with at least a 90° angle between them. The test stimulus was a color wheel based on Zhang and Luck (2008). The color wheel was composed of 180 segments with invisible boundaries, each of which contained a color from CIE-Lab space. The colors of the wheel were centered at (L = 60, a = 0, b=20) with a radius of 65. The thickness of the wheel was 1.78° with a 5.22° inner radius. The color wheel randomly rotated across trials to prevent predictability of specific color locations during the experiment. Participants performed a concurrent articulatory suppression task to minimize verbal recoding of the object colors. The stimuli were the letter strings “a,b,c,d”, “w,x,y,z”, “1,2,3,4”, and “6,7,8,9” centered at fixation. The forget cue stimuli were black arrows (approximately 0.64° × 0.32°) presented at the center of the display, briefly replacing the fixation point.

Each 1-object and 2-object trial began with a memory array presented for 500 ms followed by a 3,000 ms blank retention interval. On 2 blank retention interval. On 2-cue trials, the forget cue appeared for a duration of 500 ms, 1,000 ms after the offset of the memory items, followed by the rest of the retention interval lasting 1,500 ms. At test, a color wheel appeared along with a outlined square centered in the location of a previously presented square indicating that participants should report the color of that square. Participants were instructed to use the mouse to click the best-matched color on the wheel. To stress accuracy, responses were not timed. Feedback regarding color choice was provided immediately after response using an outlined circle on the color wheel marking the actual color value.

All trial types (1-object, 2-object, and 2-cued) were randomly intermixed, with each type occurring 100 times across six blocks. At the start of a block, participants were given the articulatory suppression string and told to repeat the letters nonstop. The letter sequence changed at the start of each block. After every 50 trials, observers were allowed to take a short break before pressing the spacebar to continue.

Data Analysis

Due to the circular nature of the stimulus space we analyzed participants' responses using a quantitative model that fitted a von Mises distribution. Using these parameters, we were able to calculate Kappa (κ) and Pm. Variability was measured as the width under the Gaussian component of the model and reflects the precision of the memory trace. Kappa (calculated using the following equation) was then translated to σ with 1/κ being equal to σ2.

f(x∣μ,κ)=eκcos(χ−μ)2πI0(κ)

One minus the probability of failure, measured as the height of the uniform distribution component of the mixture model, was calculated to derive Pm which reflects the probability that the representation was in memory at the time of test.

Results

Figure 2A shows the histograms of the responses across the three trial types and the mixture model fits. The Pm and σ values of the mixture modeling analyses are shown in Figure 2B and 2C. The effect of cuing participants to forget one of the two items on the 2-cue trials increased Pm and decreased σ to levels approaching those of when only one item was encoded and maintained on the 1-object trials. Differences in Pm and σ were analyzed using analyses of variance (ANOVAs) with the within-subjects factor of trial type (1-object, 2-object, 2-cued). We found significant main effects of trial type on both Pm, F(2, 38) = 13.80, p < 0.001, and σ, F(2, 38) = 29.77, p < 0.001.

Figure 2.

Figure 2

Open in a new tab

The results from Experiment 1. A) Histogram of all responses (in degrees from the actual color value in color space) separated by trial type. Each line represents the mixture distribution's best fit to the data. The inset box shows the effect of trial type on the tails. B) Pm values (+/− 2 SEM) and C) σ (+/− 2 SEM) values derived from the mixture model analyses.

Planned comparisons of the 2-object trials and 2-cued trials showed significant differences of both Pm, t(19) = 3.46, p < 0.05 and σ, t(19) = 6.63, p < 0.001. When comparing 1-object to 2-object trials, both Pm, t(19) = 5.19, p < 0.001 and σ, t(19) = 6.88, p < 0.001, were significantly different. The comparisons of 1-object compared to 2-cue trials only found a significant difference in σ, t(19) = 2.77, p < 0.05. The values of Pm for these trial types were not significantly different, t(19) = 1.49, p = 0.15, showing that the cues to forget one items can make memory for the remaining item similar to the response metrics in the situation in which only one item was ever stored.

Discussion

The findings of Experiment 1 support the hypothesis that focusing maintenance by cuing an observer to forget an item benefits the remaining representation in visual working memory in two ways. It can increase the fidelity of the remaining representation and prevent its loss. The σ findings evidence a boost in precision for the item that people were not cued to forget. The Pm findings showed that these cues also prevented the other item from being lost from visual working memory across the remaining 1.5-second retention interval. The difference in standard deviation between the 2-cued trials and 1-object trials suggests that some amount of information loss did occur during the second prior to cue presentation, before maintenance can be focused on just the task-relevant item. What is still unclear is what happened to the item people were cued to forget. Was that item discarded from working memory or was it left to simply passively decay or die from neglect in the visual working memory store? Experiment 2 was designed to distinguish between these possibilities.

Experiment 2

We had two goals in Experiment 2. The first was to see if the findings from Experiment 1 would replicate with a new and larger group of observers. The second goal was to observe what happens to the representation of the item made task-irrelevant by a cue to forget. It is possible that the benefits observed in Experiment 1 were due to subjects actively expelling the cued item from visual working memory. Or they might have simply failed to actively maintain the item the cued indicated should be forgotten. To distinguish between these hypotheses we needed to probe memory for the item we cued people to forget. This can be difficult because once we begin testing memory for items the cues indicate will not be tested, participants might begin ignoring these cues (see Williams & Woodman, 2012). Therefore, we wanted to probe memory infrequently for these forgettable items and focus on the first such memory probes when people would not have become sensitized to the possible task-relevance of these memories. This meant that we needed to collect data from a large number of participants to have enough observations to feed the mixture modeling analyses.

The design of Experiment 2 was a shorter version of Experiment 1 with the addition of invalid trials. Unbeknownst to the participants, on five of the 2-cue trials the cue was invalid because the to-be-forgotten item was probed at test. If the forget cues are responded to by simply not actively maintaining the cued item, then we should see evidence that it is allow to simply passively decay over time. This would mean that when participants' memories are probed for the to-be-forgotten object, we would observe precision and Pm values similar in form to the 2-object trials, simply further reduced due to the absence of active maintenance of this visual working memory representation. Note that a probabilistic sudden-death hypothesis (Zhang & Luck, 2009) in which the likelihood of death increases as time passes would make similar predictions. However, if after a cue to forget an item, it is expelled from working memory, then we should see that responses to these surprise memory probes result in random guessing about what the color of this item. That is, we should observe low Pm for those items with no Gaussian peak in the distribution around the color initially presented (i.e., a flat line).

Method

Participants

We collected data from 100 new volunteers from the same pool after informed consent was obtained.

Stimuli and Procedure

The stimuli and procedures were similar to Experiment 1 with the addition of the infrequent invalid trials. Because we were primarily interested in participants' response following the first invalid cue, we shortened the experiment to 50 trials in total; fifteen 1-object trials, fifteen 2-object trials, fifteen 2-cued trials, and five invalid trials following directed-forgetting cues. Participants were given the same instructions as Experiment 1 and were not warned about the misleading, invalid cues. On trials where an invalid cue appeared, participants were directed to ignore that item but then probed on that item during recall.

Results

Figure 3A and 3B shows that we replicated the pattern of results from Experiment 1 on the trial types that were the same in Experiment 2. Performance was slightly higher overall in Experiment 2 compared to Experiment 1. However, a cue to forget one of the two items in memory resulted in higher Pm and lower σ compared to 2-object trials without a directed-forgetting cue during the retention interval. In contrast to the high Pm and low σ for 1-object, 2-object, and 2-cued trials in Experiment 1 and 2, the invalid trials of Experiment 2 yielded little evidence that the item the cued indicated could be forgotten was in memory, with striking variability of the responses and little or no clustering of responses around the actual color of the item participants were cued to forget.

Figure 3.

Figure 3

Open in a new tab

The results from Experiment 2. A) Pm values (+/− 2 SEM) and B) σ (+/− 2 SEM) values from the mixture modeling analysis across all trial types and memory probes. C) Histogram of all responses (in degrees in color space from the actual color of the probed item) separated by whether it was the first trial with an invalid directed-forgetting cue (light blue) or the average of all five invalid trials across the 100 participants (purple). The lines shows the mixture model fit.

The within-subjects ANOVA supported the observation that the original conditions used in Experiment 1 exhibited the same pattern we saw in Experiment 2. We found significant main effects of trial type (1-object, 2-object, and 2-cued trials) on Pm, F(2, 198) = 4.42, p < 0.05 and σ, F(2, 198) = 26.20, p < 0.001. Like Experiment 1, our planned comparisons showed that across the 2-object trials and 2-cued trials, the difference between Pm approached significance, t(99) = 1.85, p = 0.07 and we found a significant difference on σ, t(99) = 2.8, p < 0.001. When comparing 1-object to 2-object trials both Pm, t(99) = 2.72, p < 0.01 and σ, t(99) = 6.95, p < 0.001, were significantly different. The comparisons of 1-object to 2-cue trials only found that σ, t(99) = 4.56, p < 0.01 differed significantly across these trial types, as Pm for these trial types were not statistically distinguishable, t(99) = 1.06, p = 0.29. These analyses mirror the essential findings reported in Experiment 1. Statistical support for this conclusion comes from entering Pm and σ data into 2×3 ANOVAs with the between-subjects factor of experiment (1 versus 2) and within-subjects factor of trial type (1-object, 2-object, and 2-cue). Neither ANOVA yielded a significant interaction of experiment x trial type, Fs < 1.0, due to the similar patterns across the dependent variables in both experiments. Both yielded significant effects of trial type (Pm, F(2, 236) = 9.62, p < 0.001, and σ, F(2, 236) = 19.12, p < 0.001) and the effect of experiment was significant in the Pm ANOVA, F(1, 118) = 27.00, p < 0.001, due to higher probabilities of remembering across all trial types in Experiment 2.

As shown in light blue in Figure 3C, the distribution of the responses on the first invalid trial yielded an essentially flat line indicating that participants guessed at the color of the item they were cued to forget during the retention interval. The modeling analysis confirmed that the probability that the to-be forgotten representation was still in memory was approximately 1% (Pm = 0.01). We also ran the modeling analysis on the combination of all five invalid trials (see Figure 3C, purple). Again, the mixture model values indicated that representations of the items participants were cued to forget were virtually absent from memory when probed (Pm = 0.07). When we compared the 2-cue trials (trials with valid directed-forgetting cues) against invalid directed-forgetting cued trials, we found that there was a significant difference in Pm, t(99) = 121.76, p < 0.001 and σ, t(99) = 13.18, p < 0.001. In addition to these analyses, we also used a bootstrapping technique to estimate the confidence intervals for Pm (Fougnie & Alvarez, 2011) in which we resampled all the available invalid cue trials 1000 times. Using this method, yielded confidence intervals similar to those from the ANOVA (Pm lower bound = .0523, upper bound = .1029) putting the upper bound of the invalid responses almost an order of magnitude lower than the Pm of the 2-object trials when either item could be tested.

Discussion

The findings of Experiment 2 replicated those of Experiment 1 in showing improvements of precision and the probability of storage following the presentation of directed-forgetting cues during the retention interval. Moreover, the trials with invalid cues showed that the participants' memory for the item they were cued to forget was highly inaccurate, as predicted if people were discarding that representation from visual working memory. The findings suggest that the participants guessed at the correct color when presented with these surprise memory probes. Averaging across all five of the invalid trials and the one-hundred participants, showed that nearly all of the responses were guess, with the mixture modeling only providing a hint of a bump in the distribution at the remembered location. During debriefing, several participants reported that after noticing the possibility of being tested on the cued item they tried to continue remembering this color during the remaining trials. This likely contributes to the slight increase in the rate of responding around the presented color. These results provide support for the hypothesis that a directed-forgetting cue results in participants completely discarding the representation from visual working memory provided nearly all memory cues are valid (see Williams & Woodman, 2012). Although the present findings are consistent with the idea that the to-be-forgotten information is discarded from visual working memory, it is not possible to completely rule out that this information is simply allowed to decay or die in visual working memory. For example, the decay or death hypothesis could account for the findings if the rate of decay or death from visual working memory is similar to that of iconic memory (Sperling, 1960). Future research will be needed within improbable invalid cues to determine whether the rate of information loss from visual working memory in the absence of active maintenace can account for the present findings.

General Discussion

Our use of mixture modeling during a directed-forgetting procedure revealed how these cues benefit the remaining representations in visual working memory. In Experiment 1, we found that a directed-forget cue resulted in a higher probability of retaining the remaining item, as well as greater precision of that representation compared to when two items were encoded and held in memory throughout the 3-second retention interval. Experiment 2 demonstrated that a cue not only helps to preserve a representation, but it also results in the representation that the cue indicates can be forgotten is probably expelled from visual working memory. This shows that maintenance mechanisms can be focused on task-relevant representations during post cuing procedures because the other information is simply removed from the limited-capacity visual working memory store.

An alternative explanation of the present findings in which maintenance of certain memory representations does not change regardless of the cues, is that the cues we presented during the retention intervals reduce interference from memory probe displays (Makovski & Jiang, 2007; Makovski, Sussman, & Jiang, 2008; Sligte, Scholte, & Lamme, 2008). This interpretation appears unlikely to account for the body of evidence for three reasons. First, the recall probe used in the present experiments was a single outlined box in all conditions. The minimalistic interference caused by this probe would be similar in all conditions and does not predict the complete absence of information when participants were probed to report the item that they were cued to forget. Second, in an unpublished pilot experiment, we found that cues presented earlier in the retention interval show greater benefits than cues prevented later in the retention interval, thus, closer to the memory probes. This finding is expected if the cues allow a participant to protect certain representations in memory by focusing maintenance mechanisms on them (Griffin & Nobre, 2003; Matsukura, Luck, & Vecera, 2007). In contrast, it is not clear how an account in which the cues prevent interference from the memory probes could account for this pattern. Third, we have previous found that cues to forget or remember a subset of the items in visual working memory change the nature of event-related potential components measured during memory retention intervals (Williams & Woodman, 2012). Observing these effects a second before any memory test array makes the retrieval-interference explanation untenable.

The findings of the present study have several implications. First, models of memory which explain serial position effects often model the loss of information from working memory as due to displacement from new information (Raaijmakers & Shiffrin, 1981). Indeed, this has an intuitive appeal as the contents of working memory might often be displaced by our encountering new objects as we move through our environment. However, the present results demonstrate that this is not the only way information can leave temporary memory. We show that we can expel information from visual working memory in a top-down manner and that this then benefits the remaining information in visual working memory. This expulsion also has benefits for processing given that studies suggest that the maintenance of information in visual working memory might trigger an attentional bias to similar items in the environment (Carlisle & Woodman, 2011a; Olivers, Meijer, & Theeuwes, 2006; Soto, Heinke, Humphreys, & Blanco, 2005) and these memory representations can even warp our perception of low-level features like the motion of stimuli (Kang, Hong, Blake, & Woodman, 2011). Though it appears possible to override such attentional biases with high-level goals (e.g., Carlisle & Woodman, 2011b; Woodman & Luck, 2007), if we want to avoid unwanted deployments of attention to items in our visual field that are not currently task relevant, then it is most effective to keep representations of task-irrelevant objects out of visual working memory all together.

The next implication of the present findings is for the physiological economy of the brain. It is clear that task relevant information is maintained in visual working memory by the sustained firing of neurons (Chelazzi, Miller, Duncan, & Desimone, 1993; Rainer, Asaad, & Miller, 1998) and potentially the correlated firing between these neurons (Vogel, Woodman, & Luck, 2001). Given this, it is metabolically economical to discard information from visual working memory, if possible, instead of having displacement remove representations from being actively maintained. The former would allow the brain to conserve neural activity, whereas the latter would require the brain to fire action potentials constantly without regard to metabolic costs. The behavioral findings we have shown here explain previous electrophysiological findings (Williams & Woodman, 2012) which are consistent with the idea that cues presented during retention intervals are used to discard information from visual working memory and conserve the neural maintenance of information for the most relevant representations.

Acknowledgements

This research was supported by the National Eye Institute of the National Institutes of Health (RO1-EY019882) and the National Science Foundation (BCS 09-57072, and a graduate research fellowship to M.W.).

We thank Kristen Quinn and Kingsley Njoku who helped with data collection. We also thank Julianna Ianni and Adrienne DiRaddo for their useful feedback and suggestions on this project.

References

  1. Anderson MC, Green C. Suppressing unwanted memories by executive control. Nature. 2001;410:366–369. doi: 10.1038/35066572. [DOI] [PubMed] [Google Scholar]
  2. Anderson MC, Levy BJ. Suppressing Unwanted Memories. Current Directions in Psychological Science. 2009;18(4):189–194. doi: 10.1177/0963721417689881. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bays PM, Husain M. Dynamic shifts of limited working memory resources in human vision. Science. 2008;321(8):851–854. doi: 10.1126/science.1158023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Carlisle NB, Woodman GF. Automatic and strategic effects in the guidance of attention by working memory representations. Acta Psychologica. 2011a;137:217–225. doi: 10.1016/j.actpsy.2010.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Carlisle NB, Woodman GF. When memory is not enough: Electrophysiological evidence for goal-dependent use of working memory representations in guiding visual attention. Journal of Cognitive Neuroscience. 2011b;23:2650–2664. doi: 10.1162/jocn.2011.21602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chelazzi L, Miller EK, Duncan J, Desimone R. A neural basis for visual search in inferior temporal cortex. Nature. 1993;363:345–347. doi: 10.1038/363345a0. [DOI] [PubMed] [Google Scholar]
  7. Fougnie D, Alvarez GA. Object features fail independently in visual working memory: Evidence for a probabilistic feature-store model. Journal of Vision. 2011;11(12):3, 1–12. doi: 10.1167/11.12.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Griffin IC, Nobre AC. Orienting Attention to Locations in Internal Representations. Journal of Cognitive Neuroscience. 2003;15(8):1176–1194. doi: 10.1162/089892903322598139. [DOI] [PubMed] [Google Scholar]
  9. Johnson HM. Processes of Successful Intentional Forgetting. Psychological Bulletin. 1994;116(2):274–292. [Google Scholar]
  10. Kang M-K, Hong SW, Blake R, Woodman GF. Visual working memory contaminates perception. Psychonomic Bulletin & Review. 2011;18:860–869. doi: 10.3758/s13423-011-0126-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. MacLeod CM. The item and list methods of directed forgetting: Test differences and the role of demand characteristics. Psychonomic Bulletin & Review. 1999;6(1):123–129. doi: 10.3758/bf03210819. [DOI] [PubMed] [Google Scholar]
  12. Makovski T, Jiang YV. Distributing versus focusing attention in visual short-term memory. Psychonomic Bulletin Review. 2007;14(6):1072–1078. doi: 10.3758/bf03193093. [DOI] [PubMed] [Google Scholar]
  13. Makovski T, Sussman R, Jiang YV. Orienting attention in visual working memory reduces interference from memory probes. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2008;34(2):369–380. doi: 10.1037/0278-7393.34.2.369. [DOI] [PubMed] [Google Scholar]
  14. Matsukura M, Luck SJ, Vecera SP. Attention effects during visual short-term memory maintenance: Protection or prioritization. Perception and Psychophysics. 2007;69(8):1422–1434. doi: 10.3758/bf03192957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Olivers CNL, Meijer F, Theeuwes J. Feature-based memory-driven attentional capture: Visual working memory contents affects visual attention. Journal of Experimental Psychology: Human Perception and Performance. 2006;32:1243–1265. doi: 10.1037/0096-1523.32.5.1243. [DOI] [PubMed] [Google Scholar]
  16. Raaijmakers JGW, Shiffrin RM. Search of associative memory. Psychological Review. 1981;88:93–134. [Google Scholar]
  17. Rainer G, Asaad WF, Miller EK. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature. 1998;393:577–579. doi: 10.1038/31235. [DOI] [PubMed] [Google Scholar]
  18. Sligte IG, Scholte HS, Lamme VA. Are there multiple visual short-term memory stores? PLos ONE. 2008 doi: 10.1371/journal.pone.0001699. doi:3:e1699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Soto D, Heinke D, Humphreys GW, Blanco MJ. Early, involuntary top-down guidance of attention from working memory. Journal of Experimental Psychology: Human Perception and Performance. 2005;31(2):248–261. doi: 10.1037/0096-1523.31.2.248. [DOI] [PubMed] [Google Scholar]
  20. Sperling G. The information available in brief visual presentations. Psychological Monographs. 1960;74 (Whole No. 498) [Google Scholar]
  21. Vogel EK, Woodman GF, Luck SJ. Storage of features, conjunctions, and objects in visual working memory. Journal of Experimental Psychology: Human Perception and Performance. 2001;27:92–114. doi: 10.1037//0096-1523.27.1.92. [DOI] [PubMed] [Google Scholar]
  22. Williams M, Woodman GF. Directed Forgetting and Directed Remembering in Visual Working Memory. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2012;38(5):1206–1220. doi: 10.1037/a0027389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Woodman GF, Luck SJ. Do the contents of visual working memory automatically influence attentional selection during visual search? Journal of Experimental Psychology: Human Perception and Performance. 2007;33(2):363–377. doi: 10.1037/0096-1523.33.2.363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Zhang W, Luck SJ. Discrete fixed-resolution representations in visual working memory. Nature. 2008;453:233–235. doi: 10.1038/nature06860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Zhang W, Luck SJ. Sudden Death and Gradual Decay in Visual Working Memory. Psychological Science. 2009;20(4):423–428. doi: 10.1111/j.1467-9280.2009.02322.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES