Working Memory (WM), the ability to maintain information in service to a task, is characterized by its
limited capacity. Several influential models attribute this limitation in a large extent to proactive
interference (Anderson & Neely, 1996; Bunting, 2006; Kane & Engle, 2000), the phenomenon that
previously encoded, now-irrelevant information competes with relevant information (Keppel &
Underwood, 1963). Here, we look back at the adult PI literature, spanning over sixty years, as well as
recent results linking the ability to cope with PI to WM capacity (Endress & Potter, 2014; Kane & Engle,
2000). In early development, WM capacity is even more limited (Kaldy & Leslie, 2005; Simmering, 2012),
yet an accounting for the role of PI has been lacking. Our Focus Article aims to address this through an
integrative account: since PI resolution is mediated by networks involving the frontal cortex (particularly,
the left inferior frontal gyrus) and the posterior parietal cortex (Badre & Wagner, 2005; Jonides & Nee,
2006), and since children have protracted development and less recruitment (Crone et al., 2006) of these
areas, the increase in the ability to cope with PI (Kail, 2002; De Visscher & Noel, 2014) is a major factor
underlying the increase in WM capacity in early development. Given this, we suggest that future research
should focus on mechanistic studies of PI resolution in children. Finally, we note a crucial methodological
implication: typical WM paradigms repeat stimuli from trial-to-trial, facilitating, inadvertently, PI and
reducing performance; we may be fundamentally underestimating children’s WM capacity.
Keywords: working memory, interference, development, capacity, cognitive control
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- Introduction
Proactive interference (PI) occurs when there is a failure to inhibit previously learned, currently
irrelevant information, resulting in memory retrieval difficulties (errors or slower responses). PI can stem
from old memories, like putting last year’s date on a document weeks after New Year’s, or recent ones,
like that moment of doubt when adding that fourth (or did I already add four?) scoop of sugar to the cake
batter. In either case, now-irrelevant information has intruded into working memory (WM), the limited
capacity system in which information is temporarily activated and manipulated in order to complete a task
(Baddeley & Hitch, 1974). This means that while the source of the irrelevant information may vary, the
competition between ‘retrieval candidates’ plays out in WM (and makes the distinction between
paradigms that target longer-term visual memory versus working memory per se less crucial for
understanding the effects of PI in WM).
PI has been studied extensively in adults since the 1960’s (Keppel & Underwood (1962); for an
excellent recent overview, see Kliegl & Bauml (2021)). Results from a number of WM studies have been
re-analyzed to quantify the effects of PI in school-age children, but there is still a major gap in the literature
concerning PI in young children, particularly under the age of 4. In this paper, we will examine five threads
in the literature: 1) PI is a primary factor limiting WM in adults (Endress & Potter, 2014; Oberauer et al.,
2016), 2) The ability to resolve PI is mediated by a network involving the fronto-parietal system and the
medial temporal lobe (Badre & Wagner, 2005; Jonides & Nee, 2006; Oztekin et al., 2009), 3) WM is more
limited in children than in adults (Kaldy & Leslie, 2003, 2005; Kibbe & Leslie, 2013; Ross-Sheehy et al.,
2003; Simmering, 2012), 4) Children are sensitive to the effects of PI (Kail, 2002) and, 5) the network
underlying PI resolution is immature in children (Polspoel et al., 2019). Tying these threads together, we
argue that developmental increases in the ability to cope with PI is a primary driver of developmental
increases in WM capacity. - Interference limits working memory capacity
Two main theories have been put forth to explain WM capacity limitations: interference (discussed
further below) and decay (Towse & Hitch, 1995). One of the stronger arguments for the role of decay in
WM comes from the Time-Based Resource Sharing model (TBRS) of Barrouillet and Camos (2004).
Here, to offset decay, one must constantly refresh to-be-remembered information during any delay prior
to recall. Thus, according to this model, the longer one needs to maintain information, the more successful
one would be at recall because of the additional opportunities to refresh. Barouillet and Camos provided
evidence for this model by filling this delay period with additional tasks of varying lengths, finding that
recall performance decreased as these additional tasks occupied more of the delay (Barrouillet et al.,
2004, 2007). However, here it is difficult to distinguish the reduction in recall performance due to the
shorter unadulterated delay times from the additional demands, in terms of attention and capacity load,
that the added tasks placed on WM. A recent review concluded that decay plays a fairly marginal role in
keeping information in WM, and instead identified interference as the major limitation (Oberauer et al.,
2016).
The interference theory postulates that what causes us to be more or less likely to keep (taskrelevant) items active in WM is our ability to cope with interference from other sources. These sources
can be (1) previously encoded memories (that is, PI), (2) salient perceptual information in the environment
(distraction), or (3) interference between multiple items needed to be kept in WM (similarity-based
competition). Computational models developed to test competing models of limited WM capacity have
reached similar conclusions (Brown et al., 2007; Oberauer et al., 2012). Oberauer and his collaborators
(2012) used a computational model called the “serial-order-in-a-box complex-span” (SOB-CS), which
posits interference as a main cause of forgetting rather than temporal decay. They found that SOB-CS
outperformed the decay-based TBRS model in predicting behavioral data, suggesting that forgetting from
WM can be better understood through interference. Similarly, Brown et al. (2007) found that a model that
assumes that all types of forgetting are due to interference rather than decay predict the findings of classic
PI experiments (Underwood, 1957) very well. Thus, there is substantial evidence from both behavioral
and computational studies that interference is a major constraint on WM in adults, and as we argue below,
must be considered in the developmental trajectory of WM capacity.
Many studies have demonstrated that PI (as opposed to other forms of interference) is one of the
main limitations on WM capacity (Anderson & Neely, 1996; Bunting, 2006; Endress & Potter, 2014; Kane
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& Engle, 2000; Lustig et al., 2001). These effects in memory were first demonstrated with tasks using
verbal stimuli (Keppel & Underwood, 1962; Wickens et al., 1963). Here, participants were given a list (a
series of numbers, letters, or syllables), then asked to count backwards during a brief retention period,
and then finally asked to recall the list. Participants' ability to recall the lists decreased as trials went on.
Importantly, this was not due simply to fatigue, as participants could be “released” from the effects of PI
by changing the type of the stimuli (e.g., from numbers to letters) (Wickens et al., 1963).
Further supporting the role of PI is Kane & Engle’s (2000) seminal study comparing individuals
with low- versus high-WM capacity. Here, participants performed a WM task in parallel with a secondary
task that varied in terms of attentional load. The ‘high load’ condition required participants to tap their
fingers on the table in a complex novel sequence, whereas the ‘no load’ condition was a repetitive pattern
that minimized attentional demands. The WM task was to recall lists of words, and the design was the
classic buildup-and-release-from-PI following Wickens et al. (1963). The words appeared one at a time
on a screen and the participants were to read the words as they appeared. After a retention task, the
participant was to recall the words orally. The first three lists were words drawn from the same semantic
category (i.e. animals), and the fourth list was from a novel category (i.e. names of countries). As
expected, a buildup of PI was seen across the three lists from the same category (fewer number of
correctly recalled words in each subsequent list), and a release from PI was observed with the fourth list
from the novel semantic category. Importantly, in the ‘no load’ condition, low-WM capacity individuals
were more susceptible to PI than high-WM capacity individuals, while in the high-load condition there
was no difference in PI susceptibility. In other words, the low-WM capacity group’s susceptibility to PI did
not change as a function of attentional load but the high-WM capacity group’s did (see Figure 1). This
suggests that, in the low-load condition, high-WM capacity individuals had available attentional resources
they could employ to help resolve PI, whereas low-span individuals did not. When attention was occupied
in the high-load condition, high-WM capacity individuals no longer had available attentional resources,
and therefore their results mirrored the results of the low-WM capacity individuals. This study, along with
similar results from other paradigms (Bunting, 2006; Lustig et al., 2001) provides strong evidence that
WM capacity is highly dependent upon one’s ability to cope with PI.
Figure 1. The results of Kane & Engle (2000) show the difference in words recalled across lists in the
no-load condition and high-load conditions among high-span vs. low-span individuals. The bar graphs
show the PI effect in the two groups of individuals demonstrating that the low-span individuals’ ability to
cope with PI does not differ significantly when attentional load is increased.
5
While most of the research on PI over the past 60 years has used verbal stimuli, recent studies
have shown that PI occurs in visual working memory as well (Endress & Potter, 2014; Hartshorne, 2008;
Makovski & Jiang, 2008). (We turn our focus to visual WM because this is how WM is measured in early
development since testing preverbal infants and young toddlers with lists of words is not possible.) For
example, Makovski and Jiang (2008) presented participants with a classic change detection paradigm
where an array of different colored disks was presented and after a retention interval, they were presented
with another display containing one colored disk (probe). Participants were to determine if the probe was
in the same location and was the same color as what they had seen on the previous array. Participants
were most likely to make an error when the probe matched the color and location of an array from the
previous trial; strong evidence of PI. In addition to this, Hartshorne (2008) demonstrated that in the same
classic change detection paradigm, PI effects from a single item can persist for up to 3-4 trials. Endress
and Potter (2014) presented a more striking demonstration of the power of interference to modulate
effective WM capacity. In this study, participants were presented with a set of pictures presented serially
at the same location, followed by a probe item. The task was to identify if the probe was novel, or a
member of the previously presented set of pictures. Here, they showed that in a condition designed to
avoid PI (pictures were never repeated), estimated memory capacity did not appear to be fixed, but
instead increased as a function of set size, and far exceeded the classic 3-4 item limit (Cowan, 2001),
reaching estimates as high as 30 items (see Figure 2). When instead PI was present (pictures were
repeated, selected from the same super-set, with replacement), capacity was limited to 3-4 items, largely
independent of set size. (This pattern of results was so inconsistent with the conventional understanding
of WM that Endress and Potter (2014) avoided direct attribution to WM, per se, and instead implicated
temporary visual memory: when interference is minimized, temporary visual memory has no definite
capacity, while in the presence of PI, it has the strict limitations typically associated with WM.)
Figure 2. Results of Endress and Potter (2014) showing large visual WM capacity estimates in conditions
with unique relative to repeated stimuli. Strikingly, participants were able to correctly remember 30 out of
100 unique items (Experiment 3).
The size of the PI effect in visual WM remains an area of debate. For example, both Hartshorne
(2008) and Makovski & Jiang (2008) found evidence of PI in visual WM using a classic change detection
task. In this task, participants are presented with an array of objects on a screen, then after a brief
retention period they are shown a probe and have to report whether the object belonged to the initial
array or not. However, in these studies, the effect of PI only decreased performance by about 15%. Lin
and Luck (2012) argued that the effects of PI can actually be eliminated completely in the change
detection task and have therefore questioned its importance in visual WM altogether. A follow-up study
conducted by Makovski (2016), however, provided an explanation for the discrepancy between the
findings of Endress and Potter (2014) and the studies that used change detection. He showed that spatial
6
location is a critical factor in determining the effects of PI in visual WM. Makovski (2016) showed that the
effects of PI are in fact substantial in visual WM, but that they are specific to each item’s spatial location.
As well, beyond spatial location, the magnitude of the PI effect can depend on several other
factors, such as the similarity of items, the length of the retention time, and whether participants are
allowed to use verbal rehearsal (Cyr et al., 2017; Endress & Potter, 2014; Loess, 1967; Wickens et al.,
1963). Additionally, temporal distinctiveness can also affect a participant’s sensitivity to PI, that is, the
longer the interval between trials, the less PI will affect performance (Kincaid & Wickens, 1970; Shipstead
& Engle, 2013). Taken together, while there is a lot to be learned about the impact of PI, it is clear that it
is a significant factor limiting the effective capacity of visual WM and, as such, needs to be accounted for
when modeling the increasing capacity of visual WM over development. - The resolution of PI is mediated by a network of fronto-parietal areas and the MTL
An influential mechanistic explanation of memory retrieval was put forth by Michael Anderson and
his colleagues (for reviews, see (Anderson, 2003; Anderson & Hulbert, 2021; Anderson & Neely, 1996;
Levy & Anderson, 2002). The starting point of this model is that retrieval always involves a decision
between candidates that have been activated based on retrieval cues. The winner is not simply the
candidate that gets the highest activation, but according to Anderson and his colleagues, competing
candidates need to be actively inhibited. Empirical support came from studies such as Anderson and
Green (2001), which showed that actively inhibiting a previously learned association leads to later
retrieval errors (with attention likely required to accomplish this active inhibition (Anderson et al., 2004;
MacDonald et al., 2000)). Anderson and colleagues (2004) showed that the same neural mechanism that
inhibits competing motor responses (i.e. in Go/No-Go tasks) is used during memory retrieval to inhibit
the competing candidates. As well, brain areas such as the anterior cingulate cortex (ACC) and the
dorsolateral prefrontal cortex (dlPFC) are active during both motor tasks that require response override
as well as during memory retrieval, especially in the presence of interference. Beyond the ACC and the
dlPFC, the resolution of PI involves a complex network of brain areas including areas in the prefrontal
cortex (PFC), posterior parietal cortex (PPC), and in the medial temporal lobe (MTL) (see Figure 3). Here
we will review what is known about these networks, in adults, and then in Section 6 turn to the
developmental work to overview the relative maturation of these areas in children.
Figure 3. Brain areas involved in PI resolution: IFG (inferior frontal gyrus, or mid-ventrolateral prefrontal
cortex, vlPFC), pre-SMA (pre-supplementary motor area), dlPFC (dorsolateral prefrontal cortex), PPC
(posterior parietal cortex), areas in the MTL (medial-temporal lobe). MTL is in lighter color to indicate its
medial position (not visible in this lateral view).
7
The first study that identified brain areas underlying PI resolution was done by John Jonides and
his colleagues using PET (Jonides et al., 1998) in a recent probes task. The recent probes task is a
classic paradigm (Monsell, 1978) where participants are presented with a set of to-be-remembered items
and then asked to do a filler task. They are then presented with a probe item and asked to determine
whether it belonged to the previous set or not. Crucially, on recent negative trials, the item did not belong
to the set of items presented in the current trial, but was in the previous trial. They found that participants
were slower and less accurate on these recent negative probes than when tested with items that were
novel (not shown in the previous trial). The left IFG (inferior frontal gyrus) of the lateral PFC was more
active when interference was high (i.e. in the recent negative trials). (Besides the recent probes task,
other classic paradigms have also been used in recent imaging studies of PI, such as directed forgetting
(Bjork et al., 1968). In the directed forgetting task, participants are presented with information to
memorize, but then asked to forget a subset of that information. Participants are then presented with a
probe and asked to identify whether that probe belonged to the to-be-remembered set or not. PI occurs
when the information that was to be forgotten is activated during retrieval. Nee and his colleagues found
that the same network was activated in both their recent probes and directed forgetting tasks (Nee et al.,
2007).)
In addition to the IFG, other regions contributing to the network have been identified, in the frontal
cortex, most notably, the dorsolateral prefrontal cortex (dlPFC) and the pre-supplementary motor area
(pre-SMA), as well as areas outside of the frontal cortex, such as the posterior parietal cortex (Badre &
Wagner, 2005; Bunge et al., 2001; Feredoes et al., 2006; Jonides & Nee, 2006; Mecklinger et al., 2003).
Not surprisingly, given the central role of interference resolution in WM, these are also the same areas
that have been consistently implicated in WM tasks in general (Curtis & D’Esposito, 2003; Duncan &
Owen, 2000). Using a recent probes task, Oztekin, Curtis and McElree (Oztekin et al., 2009) found
differential activation to interference in both the IFG and areas in the MTL. Crucially, they found activity
in the IFG in the presence of PI, regardless of whether the subject was successful on the task. In contrast,
activity in the MTL appeared to be correlated with correct responses, suggesting that the MTL is crucial
for being successful at the resolution of PI. The involvement of the MTL (in particular, the
parahippocampal cortex) was confirmed in a subsequent fMRI study using MVPA (multivoxel pattern
analysis) (Oztekin & Badre, 2011). A developmental lesion study in primates showed further converging
evidence regarding structures in the MTL and the connections between the MTL and the PFC in the
resolution of PI. Weiss and colleagues (Weiss et al., 2015) showed that neonatal lesions to the perirhinal
cortex in adult monkeys resulted in an ability to inhibit the effects of PI. That is, in tasks that used repeated
stimuli and therefore had high PI, the animals were more likely to commit errors than when tested with
trial-unique stimuli. This data confirms that areas in the MTL (parahippocampal and/or perirhinal cortex)
are also important nodes in the network involved in the resolution of PI.
A review by Irlbacher, Kraft, Kehrer and Brandt (2014) asked the question of whether the
involvement of the different areas of the PI resolution network, as well as their timing, may differ
depending on the type of control processes used to resolve interference A highly influential general
framework of cognitive control distinguishes two types of control processes: proactive versus reactive
control (Braver, 2012; Braver et al., 2007). For example, in the recent probes task, the participant can
only begin to address the effects of PI once the negative probe has been introduced (reactive control). If,
however, after the participant has been exposed to several recent negative probe trials, he or she might
begin to anticipate and try to prepare for the interference before the onset of the negative probe (proactive
control). In their review, Irlbacher and colleagues find some evidence for the differential activation
patterns (proactive versus reactive) within the network of areas outlined above, in time, but with a
substantial overlap. From our developmental perspective, it is important to note that the current view is
that young children are only able to engage in reactive control, with proactive control only emerging in
mid-childhood (Chatham et al., 2009; Chevalier et al., 2015). - Working memory is more limited in children
Infants’ working memory is more limited than adults’, and capacity steadily increases across
development (Kaldy & Leslie, 2003, 2005; Kibbe & Leslie, 2013; Pelphrey et al., 2004; Ross-Sheehy et
al., 2003; Simmering, 2012). (Infant studies have focused on visual WM, since using verbal stimuli is not
8
possible.) Beyond infancy, WM capacity continues to grow, reaching adult levels by late childhood
(Cowan et al., 2005; Gathercole et al., 2004; Riggs et al., 2006). In a highly influential review, following
the roadmap laid out by Dempster, (1981), Cowan aimed to identify the factors underlying WM
development in both younger and older children (Cowan, 2016). Besides the growth of pure ‘scope’
(capacity) of WM, high-level mnemonic strategies such as chunking and verbal rehearsal also affect WM
capacity estimates. But young children (under 5 years of age) are less adept at spontaneously using such
strategies (see Elliott et al., 2021), and this may make them more susceptible to the effects of PI.
Beyond these differences in strategy use, Cowan pointed out the difficulties connecting
performance measures in infants versus children that stem from the inevitable differences in task
demands and how performance is quantified in different tasks. After surveying the literature on children
under 6 years of age, he concluded that more research is needed on the influence of cognitive control,
and whether the scope of WM develops independently from these mechanisms. The goal of this review
then, is in line with Cowan’s suggestion, as we argue that the development of cognitive control
mechanisms underlying PI resolution is a significant factor driving increases in WM capacity. - PI affects memory in children
Following the discovery of PI as a crucial aspect of memory in studies with adults (Wickens et al.,
1963; Wickens, 1970), developmental researchers became interested in studying its effects in young
children. Most of these early studies involved school-age participants, with the exception of three studies
that studied preschool-age (4–5-year-old) children (Rosner, 1972; Esrov et al., 1974; Reutener & Fang,
1985). However, instead of quantifying the effect of PI on WM itself, the main goal of these studies was
to use the PI buildup-and-release paradigm as a tool to study categorization and concept formation in
children. These early studies in the 70’s and 80’s were followed by others designed to measure PI’s
effects on school-age children’s memory. We have summarized all previous studies of PI effects in
children in Table 1. Across all 15 studies, it is clear that PI is a robust phenomenon throughout
development, and that the effect is stronger in younger children (one exception is Chiappe et al. (2000),
which we discuss below.)
9
Table 1. Studies that investigated the effect of proactive interference (PI) on children ’s WM performance. The list is ordered by the age
of the youngest participants. STM - short term memory, Brown-Peterson - Brown-Peterson task (see text for details.)
10
To date, the strongest evidence for the claim that PI is not simply present but is higher in younger
children comes from a meta-analysis by Kail (2002). He reanalyzed a large set of previous studies (26
studies, 86 datasets) that used a Brown-Peterson task to study working memory in 4-14-year-old children.
The Brown-Peterson task is a classic paradigm used to measure memory capacity. Here, lists of words
are given to the participants and, after a brief retention period where verbal rehearsal is blocked (usually
by counting backwards), the participants are asked to recall words from the list. Since these were not PI
studies per se, Kail analyzed whether there was a decrease in performance across the first three trials,
as would be expected as the detrimental effects of PI accumulate. The meta-analysis revealed that the
effect of PI was considerable, more so in younger than in older children, but the ability to cope with PI
increased steadily with age. In a second, empirical study, Kail tested 9-13-year-old children and college
students in a Brown-Peterson task, with 4 consecutive trials (Kail, 2002). Here, he found a similar pattern
to what was shown in the meta-analysis: performance decreased across trials and younger children were
more susceptible to PI than older children and adults (see Figure 4).
Figure 4. Results of Kail (2002). 9- to 12-year-old children and young adults were tested in four trials of
a Brown-Peterson task. The effect of proactive interference decreased with age.
This same developmental trend was confirmed by Carriedo and collaborators (Carriedo et al.,
2016). There, participants performed a guided recall of items from word lists. They found that the
proportion of errors due to intrusions from previous lists (i.e., errors due to PI) decreased from 7 to 15
years, at which age the ability to inhibit the previous list intrusions appeared adult-like. In the visual
domain, Loosli and colleagues (Loosli et al., 2014) found similar results using a recent probes task in 8-
14-year-olds. In this task, children were presented with a target set that consisted of four pictures of
nameable animals, followed by a brief retention period. Then they were presented with a probe picture
and asked to report whether it matched an animal from the target set. On some trials, the probe item was
not in the target set, but had been in the target set of the previous trial, setting up an opportunity for PI.
Children (8-10 and 11-14-year-olds) committed more PI-related errors than young adults. In the same
study, they also conducted a N-back task with repeated items. Here, children were shown a sequence of
pictures of animals. With each subsequent picture, the children were to determine whether the animal
was the same as that presented two pictures prior. In a critical lure condition, the target picture did not
match the one two images prior, but instead the one three images prior, thereby provoking PI.
Surprisingly, the younger child group made fewer PI-related errors than young adults. The authors
11
suggest that the n-back task is particularly challenging, and indeed the data showed that the younger
children had more difficulty remembering the items two positions back. In order to see the effects of PI,
they argued, one not only has to remember the item two positions back but also three positions back
once the lure is introduced. (This may be a factor as well in the study of Chiappe et al. (2000), mentioned
above. There, the youngest age group (6-9 years old) did not have more overall intrusion errors than
older children or adults, as one might expect. But they did have by far the lowest overall memory for word
list items. Following the explanation of Loosli et al. (2014), the 6-9-year-olds may have had fewer intrusion
errors simply because they had fewer remembered items to intrude.)
But what is the mechanism that underlies the development of PI resolution? Recall the model of
memory retrieval by Anderson and colleagues discussed in Section 3 above (Anderson, 2003; Anderson
& Hulbert, 2021; Anderson & Neely, 1996; Levy & Anderson, 2002), where retrieval involves a decision
between candidates that have been activated based on recall cues, where competing candidates need
to be actively inhibited. With this model in mind, we can hypothesize two potential processes. (1) There
could be a reduction in cue ‘overlap’ across development (the tendency to activate multiple memories by
a single cue). This could happen through more precise memory encoding processes with age (Burnett
Heyes et al., 2012; Guillory et al., 2018). On the other hand, the growth of children’s knowledge base
means that the same cue will be associated with more potential retrieval candidates. (2) Children get
better at exerting the inhibitory control that is needed for candidate inhibition. This is a more likely
explanation, as deficits in inhibitory control in children have been well-documented in multiple domains
(Davidson et al., 2006; Durston et al., 2001; Wolfe & Bell, 2004). Clearly, this is an important question for
future research.
PI effects in children have also been demonstrated outside of the laboratory, such as in math
education. The learning of a series of math facts with shared numerals, for instance multiplication
problems, is a situation where interference strongly affects learning (De Visscher & Noël, 2014). For
example, learning the problem 9 x 3 = 27 is harder than learning 5 x 5 = 25 because there are more
multiplication problems that contain the numerals {9, 3, 2, 7} relative to {5, 2}. To capture this, De Visscher
& Noel (2014) assigned an interference parameter to multiplication problems according to the number of
numerals shared with other problems. They also weighted them according to the order in which they are
typically taught to children (from ‘2 times’ multiplication tables to ‘9 times’ tables). They found that the
interference parameter could predict performance on multiplication problems on previously published
reaction time data from young adults (Campbell, 1997). They also found that the level of interference was
positively correlated with reaction time in a speeded task in both 8- and 10-year-olds and in a new study
with young adults. They further argued that the interference effect might be one of the mechanisms
behind dyscalculia, a learning disability where individuals struggle with the learning of math facts (De
Visscher et al., 2015).
Despite the importance of PI in memory and the evidence demonstrating the effect of PI in older
children, very few studies to our knowledge have explicitly looked at the effects of PI in children younger
than 4 years of age. One study in 5-7-month-olds exploited the effects of PI to demonstrate that infants
were able to form categories of faces (Tyrrell et al., 1990). Here, when infants were familiarized with a
set of face stimuli that were highly similar to the test stimuli (e.g, right-side up photographs) they were
less likely to show a novelty preference than infants who were familiarized with stimuli that differed greatly
from the test stimuli (e.g., familiarized with upside down caricatures of faces and tested with right-side up
photographs of faces). Besides that, there have been a handful of other WM studies with infants that,
while not explicitly designed to investigate PI, have invoked PI to explain their results (Choi et al., 2018;
Oakes & Kovack-Lesh, 2013). This significant gap in the developmental literature most likely stems from
a challenge in tailoring classic WM tasks to children with weak or no expressive language skills. However,
further study of PI in infants and young children would be possible if based on paradigms used
successfully in the study of visual WM.
- The network underlying PI resolution is immature in children
Neuroimaging studies of WM development began in the mid-90’s (Bunge et al., 2002; Casey et
al., 1995; Klingberg et al., 2002; Nelson et al., 2000). These fMRI studies were complemented by EEG
and recently by functional near-infrared imaging (fNIRS) studies (Bell & Wolfe, 2007; Perlman et al.,
2016). These studies have shown that activity in the fronto-parietal system during WM tasks emerges
12
early (Fitch et al., 2016), and the same areas that show WM load-dependent activity (intraparietal,
superior frontal and dorsolateral frontal regions) become gradually active in children as well (Luna et al.,
2010; Yaple & Arsalidou, 2018).
While there has been extensive research on the WM network in general, very little work has
focused on disentangling PI effects in development. No studies thus far have used a recent probes task,
nor analyzed performance in the N-back task with repeated versus unique stimuli to measure brain
activity during PI resolution in children. Some of the studies on WM processes have invoked overlapping
systems. For example, Crone and colleagues (Crone et al., 2006) studied the neural mechanisms
underlying maintenance and manipulation of information in a WM task using fMRI in 8-12-year-olds, 13-
17-year-olds, and adults. Although this study did not investigate PI directly, the task required a
manipulation of information that necessitated the inhibition of recently encoded information. On a typical
trial, participants were presented with three pictures of familiar objects followed by a direction either
“forward” or “backward.” Next, participants were presented with a picture of one of the previously
presented objects and were asked whether this item had been the first, second, or third item presented.
“Forward” direction did not require the manipulation of information, in contrast with the “backward” or
manipulation trials which required the participant to reorder the items in their mind and overwrite the
recently encoded, salient forward order. Younger children’s (ages 8-12) performance was well below
older children’s (ages 13-17) or young adults’. Crucially, the imaging results revealed that older children
and adults recruited the right dlPFC and bilateral superior gyrus (in the posterior parietal cortex) during
the delay period, whereas the 8-12-year-olds failed to recruit these areas.
If we turn our attention specifically to the left IFG, the area that has consistently been invoked in
interference resolution in adults, there are a handful of studies that have found protracted development.
For example, an fMRI study found weaker top–down modulatory influences from the inferior frontal area
to parietal and temporal regions in 9-12-year-olds (Bitan et al., 2006), however this was not in the context
of a WM task. In a verbal WM task, Vogan and colleagues found lower activation of the left IFG in 9-15-
year-old children (Vogan et al., 2016) compared to adults, and activity in this region was related to
performance (along with the left middle frontal gyrus and bilaterally in the superior parietal gyrus).
Aside from studies that can only provide indirect evidence for the mechanisms of interference
resolution in children, to our knowledge, there has been only one study that directly investigated the
neural substrates of PI in children (Polspoel et al., 2019). The same research group that identified PI as
the main factor behind arithmetical problem-solving in children (see Section 5 above) conducted an fMRI
study on interference and load (problem size) in adults, and found that – just as in studies of WM using
the recent probes task – the left IFG showed differential activation related to interference (De Visscher et
al., 2018). In the Polspoel et al. (2019) study, they tested 9-10-year-olds in the same paradigm. They
found clear behavioral effects of both interference and load on children’s performance. They also found
a strong effect of load on the activity of the posterior parietal cortex, frontal cortex (precentral gyrus), and
the occipital cortex (fusiform gyrus). However, for the interference effect, the results of this study were
unfortunately inconclusive, as no significant activation differences between low- and high-interfering
problems were found in the full factorial model, or in the whole-brain contrasts when correcting for multiple
comparisons. The authors provided some methodological reasons for this surprising result.
Overall, the lack of studies on the neural mechanisms of PI resolution in children is a significant
gap in the literature, and future research (using fMRI, EEG, and fNIRS) should aim at characterizing the
development of these mechanisms. - How PI affects estimates of infants’ working memory capacity
Most developmental research has measured WM capacity by presenting participants with
consecutive trials containing highly similar, if not identical, stimuli. Unwittingly, as we have seen from this
review, this creates an ideal context for PI. Ironically, this repeated-stimuli-over-trials design has been
used to ensure that the infant (or primate) was, in fact, using WM to solve the task (Mishkin, 1978). In the
primate neurophysiological literature, this became known as trial-unique versus trial-non-unique
presentation (Stern et al., 2001). The logic of this design was that when a series of trials contains repeated
stimuli, the participant is required to update their mental representations on every trial, therefore ensuring
that they are exploiting WM and not ‘long-term' recognition memory alone. This design directly entangles
PI with estimates of WM capacity. Since in these paradigms average performance over all trials is used
13
to estimate WM capacity, it is very likely that we have been (perhaps considerably) underestimating
children’s WM capacity.
As an exercise to gain insight, we conducted a meta-analysis of trial-by-trial data from infant
studies that attempted to characterize visual WM capacity using a paradigm with multiple trials containing
repeated stimuli. We adopted the same method as Kail (2002) to test whether infants’ performance
dropped across trials. (Note: just as in Kail (2002), the analyzed studies were not specifically designed
to test effects of PI.) We followed the standard guidelines for meta-analyses (Harrer et al., 2021). Papers
were found by conducting a search on PubMed using the keywords “visual working memory” and “infant”
in October 2020. The search yielded a total of 24 potentially relevant papers. The studies used one of
three paradigms: change detection, violation of expectation, or Delayed Match Retrieval. We ultimately
decided to only analyze results from studies using one of these paradigms: violation-of expectation. We
did not include change detection studies for two reasons. (1) This paradigm measures memory processes
at a very short timescale (hundreds of milliseconds versus several seconds in other WM paradigms), and
(2) in these studies, the repetition of items from trial to trial was randomized for each participant, and
there was no way of extracting data to contrast performance in repetition versus no-repetition trial pairs.
We decided not to include Delayed Match Retrieval studies in the meta-analysis, because (1) the task
demands were different from that of the violation-of-expectation task (rule learning plus anticipatory
looking versus passive detection of novelty), and (2) the dependent variables were also different (2-
alternative choice versus looking time). 19 of the 24 studies were eliminated because the authors did not
use a violation-of-expectation paradigm, or did not present infants with at least three experimental trials
(necessary to see the trial-by-trial buildup of interference). If all other criteria were met but the authors
did not report trial-by-trial data, the authors were contacted for their raw data. The final data set included
5 studies (15 experiments, 401 infants), all of which used a violation-of-expectation paradigm, and –
coincidentally – all employed a between-subjects design (see Table 2). The infants in this final set of
Table 2. Studies that were included in our meta-analysis. These studies investigated infants’ visual WM
using repeated items over multiple trials in the violation-of-expectation paradigm.
14
studies were between 6 and 12 months of age (Mean age = 7.5 +/- 1.9 months). Unfortunately, we could
not find any studies measuring WM capacity in toddlers (1-3-year-olds) with stimuli repeated across
multiple trials. Ultimately, our search resulted in a small set of methodologically highly homogeneous
studies. It should be noted that these 15 experiments were conducted by three researchers working in
the same laboratory, which limits the generalizability of the findings. Future studies designed specifically
to measure PI in infants are needed.
In violation-of-expectation tasks, infants are presented with a sequence of events (for example, a
triangle being hidden behind a screen on the left and a disk behind a screen on the right, e.g. Kaldy &
Leslie, 2003). After a short delay, the screens are removed to reveal either an unexpected outcome
(objects in the reversed position, a violation of spatio-temporal continuity), or an expected outcome
(triangle on the left, disk on the right). If infants remember ‘what was where’, they will look longer at the
unexpected outcome. In order to create a measure that can be used as a proxy for WM performance, we
subtracted the mean looking times of the expected outcome group from the unexpected outcome group
to calculate baseline-corrected mean looking times for the first three trials. Similarly to Kail (2002), we
analyzed the difference in the average corrected looking times between Trial 1 and Trial 2 and Trial 1
and Trial 3. We calculated a Hedges g and the variances to quantify the effect size of these differences
in each of the studies. We then ran separate random effect models for each of the two comparisons. The
overall effect of the Trial 1 - Trial 2 difference was not significant, but we found a significant overall effect
(p = 0.0002) of the Trial 1 - Trial 3 difference, with the heterogeneity among studies being non-significant
(p > 0.05) (See Figure 5). That is, we found a significant drop in looking times from Trial 1 to Trial 3 to
the unexpected outcome (where looking times were corrected with baseline looking times in the expected
condition). Our interpretation of this finding is that PI affects 6-12-month-old infants’ WM performance
and could at least partially explain their low capacity previously measured in this paradigm. It is important
to note, though, that this re-analysis of previously published data does not allow us to claim that PI is the
only reason for the decline in infants’ performance across trials. It is likely that other factors, such as a
potential habituation to the surprising outcome, contribute to the drop in looking times. Looking forward,
a systematic comparison of WM tests with a series of trial-unique versus trial-non-unique stimuli could
quantify the PI effect in infants.
Figure 5. Forest plot depicting effect sizes (Hedges g) in our meta-analysis (5 articles, 15 studies, 401
infant participants) testing the difference between (baseline-corrected) Trial 1 and Trial 3 performance.
Overall effect size is 0.37, p = 0.0002.
15 - Summary and future directions
In this review and targeted meta-analysis, we tied together several threads in the literature to
support the argument that developmental increases in working memory capacity are driven by increases
in the ability to cope with proactive interference, and further, the implication that we have likely been
underestimating young children’s WM capacity.
We first reviewed the literature providing evidence that PI affects WM capacity in adults. We then
outlined the literature establishing that the resolution of PI in adults is mediated by a network including
areas of the frontal cortex, the posterior parietal cortex, and the MTL. Next, we showed that WM capacity
is more limited in children and that children are, in fact, sensitive to the effects of PI (see Table 2). Lastly,
we presented (so far, mainly indirect) evidence that the cortical network underlying PI resolution is
immature in children.
To date, there have been no studies directly measuring the effect of PI or underlying cognitive
mechanisms in children under 4 years of age. In order to help determine whether PI may be a limiting
factor on WM in early development, we conducted a targeted meta-analysis of a highly homogeneous
set of infant studies. Most developmental research measures WM capacity by presenting participants
with consecutive trials containing highly similar, if not identical, stimuli. Unwittingly, this has created an
ideal context for PI. Using a technique based on a similar meta-analysis of studies with older children
(Kail, 2002), trial-by-trial trends showed a pattern consistent with the accumulation of PI. This result
suggests that we may be underestimating WM capacity in early childhood, and we argue that research
explicitly measuring how the ability to cope with PI modulates WM capacity within, and across, age groups
is needed.
Finally, while a sophisticated description of the brain networks underlying interference resolution
in adults has emerged, very little work has focused on disentangling the neural mechanisms of PI
resolution in development. This is a significant gap in the literature, and we suggest that future EEG, fMRI
and fNIRS studies should directly investigate the development of the network underlying interference
resolution in WM across childhood.
16
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