Why Does Visual Working Memory Ability Improve With Age: More Objects, More Feature Detail, Or Both? A Registered Report Part 1

Abstract

We investigated how visual working memory (WM) develops with age across the early elementary school period (6–7 years), early adolescence (11–13 years), and early adulthood (18–25 years). The work focuses on changes in two parameters: the number of objects retained at least in part, and the amount of feature detail remembered for such objects. Some evidence suggests that, while infants can remember up to three objects, much like adults, young children only remember around two objects. This curious, non-monotonic trajectory might be explained by differences in the level of feature detail required for successful performance in infant versus child/adult memory paradigms. 

Adolescence is a critical period in life that not only affects physical development but also has a profound impact on memory. Early adolescence is at the peak of brain development. Learning, training, and exercise during this period play an important role in the development and improvement of memory.

During early adolescence, the brain begins to mature and the number of neurons and synapses gradually increases. At the same time, people's learning abilities will also improve during this period, making it easier for young people to succeed when learning new knowledge and skills. In addition, through appropriate training and exercise, the potential of the brain can be further explored and stimulated, and the efficiency of memory and learning can be improved.

Therefore, in early adolescence, special attention needs to be paid to the cultivation and exercise of memory. You can improve your memory level and ability by reading more, taking more notes, and doing more exercises. At the same time, you must also ensure adequate sleep and rest, which are very important for brain development and memory improvement.

In short, early adolescence is a period that is conducive to brain development and memory improvement. We should actively use the characteristics and advantages of this period to improve our memory and learning abilities through appropriate training and exercise and lay a solid foundation for future learning and growth. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material that has many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.

Click know 10 ways to improve memory

Here, we examined if changes in one of two parameters (the number of objects, and the amount of detail retained for each object) or both of them together can explain the development of visual WM ability as children grow older. To test it, we varied the amount of feature-detail participants needed to retain. In the baseline condition, participants saw an array of objects and simply were to indicate whether an object was present in a probed location or not. This begins with a titration procedure to adjust each individual’s array size to yield about 80% correct. In other conditions, we tested the memory of not only location but also additional features of the objects (color, and sometimes also orientation). Our results suggest that capacity growth across ages is expressed by both improved location memory (whether there was an object in a location) and feature completeness of object representations.

Working Memory (WM) is the system that holds mental representations available for processing for use in higher-level cognitive activities (e.g., Logie & Cowan, 2015). WM capacity is thought to be a crucial determinant of cognitive development in childhood (Bayliss, Jarrold, Gunn, & Baddeley, 2003; Holmes, Gathercole, & Dunning, 2010) and individual differences in intellectual abilities (Conway, Kane, & Engle, 2003; Jarrold & Towse, 2006). Generally, WM performance improves as children grow older (e.g., Brockmole & Logie, 2013; Cowan, Fristoe, Elliott, Brunner, & Saults, 2006; Cowan, Morey, AuBuchon, Zwilling, & Gilchrist, 2010; Cowan, Naveh-Benjamin, Kilb, & Saults, 2006; Gathercole, Pickering, Ambridge, & Wearing, 2004; Isbell, Fukuda, Neville, & Vogel, 2015; Riggs, McTaggart, Simpson, & Freeman, 2006; Riggs, Simpson, & Potts, 2011), and understanding this development has important consequences for educational settings.

For instance, children’s ability to follow instructions may be constrained by WM capacity (Jaroslawska, Gathercole, Logie, & Holmes, 2016). However, despite the consensus that WM abilities improve as we reach adulthood, it is unclear which aspect of WM drives this improvement. Numerous candidate processes have been proposed, tested, and rejected. For instance, WM development does not seem driven by the improved ability to allocate attention effectively (Cowan et al., 2010; Morey et al., 2010), improved object knowledge (Cowan, Ricker, Clark, Hinrichs, & Glass, 2015), or reduced memory encoding limitations (Cowan, AuBuchon, Gilchrist, Ricker, & Saults, 2011).

Here, we focus on two factors that may explain visual WM improvement as children grow older from the elementary school years to adulthood. The first factor on which we focus is the increase in the number of objects that can be retained in WM, and the second is the amount of feature detail retained for each object. Consider when someone asks a child to remember three animals: a bird, a fish, and a giraffe. To distinguish a bird from a fish, they need to rely on certain features of these objects (do they have wings, do they have a beak?). The animals may also differ in size, color, and other features. It is possible that retaining three separate objects (or animals) is too much, and children will forget one of the animals. Or, they may retain something about each animal, but not all the features. For instance, they may remember that one animal was yellow, but forget its other features, and recall that another animal was a bird, while forgetting its color.

A large body of research suggests that adults typically can remember three to four items when there is no way to combine the presented items into fewer, larger chunks (Cowan, 2001; Luck & Vogel, 1997). However, when item complexity increases, featural detail is not complete (Cowan, Blume, & Saults, 2013; Hardman & Cowan, 2015; Oberauer & Eichenberger, 2013, although see Luck & Vogel, 1997). Cowan et al. (2013) presented arrays of colored shapes and required memory of only colors, only shapes, or both. After a brief retention period, participants judged whether a visual array differed from a comparison probe item that was presented (‘change’) or did not differ from it (‘no change’). 

Young adult participants remembered something about around three items on average in all conditions but, when responsible for both features, they often forgot either the shape or the color. Similar results were found for multi-featured objects with 4 – 6 features (Hardman & Cowan, 2015; Oberauer & Eichenberger, 2013). Although it is beyond the scope of the present work, making adult participants responsible for two features instead of just one may also reduce the precision of memory, such as its exact location on a circle representing possible orientations or colors (Fougnie, Asplund, & Marois, 2010).

We hypothesized that the number of objects and featural detail of those objects may follow separate developmental trajectories, based on an intriguing paradox in the literature on memory ability in infants, children, and young adults. 

While adults typically make errors if the number of items to hold in mind exceeds three to four items (Cowan, 2001; Luck & Vogel, 1998), preschoolers and children who just started school seem able to retain only about 2 to 2.5 items (e.g., Cowan et al., 2005; Cowan, Nugent, Elliott, Ponomarev, & Saults, 1999; Riggs et al., 2006; Simmering, 2012). Surprisingly, though, there is evidence to suggest that 18-month-old infants may remember around three objects (e.g., Ross-Sheehy, Oakes, & Luck, 2003; Zosh & Feigenson, 2015). This would lead to the unsettling conclusion that memory capacity decreases with age in young children. That conclusion would be unwarranted, however, since studies use different paradigms than studies of WM in older children. For example, Feigenson and Carey (2003; 2005) found that 14-month-old infants searched for the correct number of items when up to three objects were hidden. In such infant research, participants have attributed a memory capacity of three simply by remembering that three items were there. 

In contrast, paradigms used with older children typically involve detecting changes to (or reproducing) items based on features such as color, shape, or orientation (e.g., Burnett Heyes, Zokaei, van der Staaij, Bays, & Husain, 2012; Cowan et al., 2006; Heyes, Zokaei, & Husain, 2016; Riggs, Simpson, & Potts, 2011; Sarigiannidis, Crickmore, & Astle, 2016), requiring participants to remember what they saw, instead of simply indicating that they saw something.

Indeed, when exploring infants’ memory for item features, memory capacity estimates are lower. Zosh and Feigenson (2012) tested whether infants remembered item features by replacing hidden objects that the infant has seen with hidden objects that have not been seen. If infants remember feature-detail they should notice when one object has been switched out for another, and search for the missing item. If, in contrast, infants only remember that they saw some object, but not what it was (i.e., no feature-detail), they would not notice the switch, and therefore not search for the original object. Using this approach, Zosh and Feigenson found that 18-month-olds appeared to remember sufficient featural detail to distinguish between objects (i.e., noticing identity switches) when tasked with remembering one or two objects. 

The infants were allowed to retrieve the objects from a container and kept searching for the remembered objects when the new objects were found in the container instead, presumably thinking that the old objects must still be in there. However, when three objects were hidden, the infants no longer appeared to notice such switches since they stopped searching after three objects. Thus, despite remembering the presence of three objects, they appeared to remember three-object arrays with less featural detail than and two-object arrays. Interestingly, when the identity change was more pronounced – the researchers replaced an object with a nonsolid substance – infants did seem to notice, even at set size three. This indicates that while some feature detail was retained, the representation may be too weak to differentiate between two solid objects, but sufficient to distinguish between more distinctly different representations (i.e., a solid vs. a non-solid object). Similar results have been found in infants as young as six months old, who seemed to remember the categorical identity (ball vs. doll head) of a hidden object but failed to remember its perceptual identity (e.g., its color; Kibbe & Leslie, 2019).

Thus, the marked increase in WM ability seen from toddlerhood to adolescence (e.g., Cowan et al., 2006; Cowan et al., 2010; Cowan, Naveh-Benjamin, Kilb, & Saults, 2006; Riggs et al., 2006; Riggs et al., 2011) may not be driven by the ability to retain more items, but instead by increased feature-detail retained for the remembered items. Differences in what constitutes ‘remembering an object’ in typical infant paradigms – compared to paradigms used with children and adults – may explain this counterintuitive U-shaped function of memory capacity with age. If, like infants in some procedures, children merely need to know if something was there (without remembering featural detail), their estimated memory capacity (k), should be about three items, similar to estimates obtained for infants in the aforementioned procedures and young adults in testing procedures like ours. If so, this would suggest that the number of objects that can be held in mind is constant across the human lifespan, but the amount of detail per item may explain the memory improvement associated with development. Consistent with this possibility, children who are only able to retain one or two items in visual WM still typically judge that they have about three items in mind when asked about an array of colors before an objective test (Blume, 2018). In these cases, the children may remember certain objects for which they do not realize that they no longer retain the critical feature to be tested (in Blume’s procedure, color).

As these examples suggest, measuring the act of ‘remembering an object’ is not necessarily straightforward. Indeed, the relationship between features and objects, and the space they occupy in working memory, is a contentious issue. Some research has suggested that a specific number of items can be held in memory regardless of the number of features per item (Luck & Vogel, 1997; Luria & Vogel, 2011; Vogel, Woodman, & Luck, 2001). However, others have found that remembering additional features does impair memory (Cowan et al., 2013; Cowan & Hardman, 2015; Oberauer & Eichenberger, 2013). Cowan and Hardman (2015) used a similar paradigm to ours and found that both increases in the number of objects and feature load impaired memory, in adult participants. The model that was fit to all three of these more recent data sets is one in which there is a limit to about 3 objects, and also a limit to the number of features per object for briefly presented arrays. By including a baseline condition titrated to yield a constant performance level across age groups, we plan to examine whether adding features to be remembered to each object creates more difficulty for younger children than for older children or adults.

We approached the measurement of objects remembered in two ways. Firstly, we measured object memory as remembering that something was present in a specific location. This something-therefore implementation hews closely to the concept of an ‘object file’ (Kahneman & Treisman, 1984). In this view, visual events are likened to reports to a police station, where a new file is opened for each novel event, by its time and location. Then, more features can be added (such as details about the crime or, for a visual object in our study, its color and orientation). Our ‘was-something-there’ question may be like asking whether an object file was created, given that the objects do not move within an array. 

Then, additional details that may have been added to the object file (color and orientation) were sometimes probed. This order of testing fits with the idea that location is used to access specific visual features (Nissen, 1985), and has a special status in binding visual features (e.g., Kahneman, Treisman, & Gibbs, 1992; Treisman & Zhang, 2006; Wheeler & Treisman, 2002). Nevertheless, even if the percept is created via such a location-specific object file, it is theoretically possible that, for some objects, location is subsequently forgotten while the color or orientation of the object is retained. Indeed, other research suggests that location and feature information are not necessarily integrated. 

While location appears crucial for initial perceptual binding, location’s special status might be lost once representations are formed in WM, which operates according to different principles than visual attention and perception (e.g., Hedayati & Wyble; Logie, Brockmole, & Jaswal, 2020). We allowed for the possibility that exact location could be misremembered while other features were remembered in a second measurement of objects in memory, namely the number of objects for which at least one feature was remembered (in addition to using the first quantification, object locations correctly remembered).

This theoretical question that we asked may be orthogonal to some other questions one could ask about the development of WM. For example, it is possible that what develops is the speed of refreshing the representations of items in working memory (e.g., Gaillard, Barrouillet, Jarrold, & Camos, 2011). Even if that is the case, one can still ask whether the developing rate allows retention of more items, more features per item, or both. Similarly, there may be developmental increases in knowledge and strategies (e.g., Cowan, 2016) but their development would not settle the issue of whether the advances that occur affect the number of WM representations or their detail, the latter determining if the representations are sufficient to answer experimental test questions.

We tested if developmental changes from childhood- to adulthood are driven by remembering more objects, and/or remembering objects with richer feature detail, by asking participants to remember objects at different levels of featural complexity. Below, we outline how we conceptualized the completeness of object representations, define the terms we will use, and present the key questions we addressed.

Experimental Aims and Hypotheses

In practical applications of knowledge about working memory, as in education, one potential way to work around WM limitations and facilitate learning is to adjust the presentation of materials by reducing the number of parts to be held in mind independently (see Cowan, 2014; Gathercole & Alloway, 2007). To do this, it is useful to know if the number of chunks or the amount of feature detail – or both – tends to overload young children’s WM. Returning to the animal example above, we would be interested in whether young children’s WM limitations are caused by the number of objects (animals), or the number of features (the feature complexity of those animals). Using simpler three-feature objects, we aimed to answer two questions. First, do children remember fewer objects? Second, does increasing the complexity of the memory task (i.e., asking participants to remember more features per object), influence performance equally across development?

We tested the hypothesis that what improves with development is the completeness of object representations but not the number of objects in WM per se. According to this hypothesis, the reason that young children do more poorly than adults on remembering arrays of, say, colored squares (e.g., Cowan et al., 2005) is that the colored squares are two-featured objects with location and color as distinguishing features, and children may remember the locations of just as many objects as adults while remembering fewer colors or may remember at least one feature (location or color) of just as many objects as adults while remembering fewer features overall.

This feature enrichment hypothesis of developmental change leads to two predictions: (1) Regardless of age, the number of objects at least partially in WM should be about three, but (2) for such objects, younger children should be less able to remember features (i.e., their performance will be more impaired when asked to remember additional feature detail about these objects). We examined developmental changes in both of these parameters (number of objects and number of features within objects) using a version of methods previously used in adults with multi-featured objects (Cowan et al., 2013; Cowan & Hardman, 2015 ), adapted here to the study of child development. 

Importantly, we included a baseline ‘was something-there’ condition, making our paradigm conceptually related to some used in infant research, to achieve some general comparability to capacity estimation methods in such studies. We used a task in which the kind of visual display is always the same, but in which the information required for perfect task performance varies. Sometimes participants were only responsible for remembering whether an object was present at a particular location on the computer screen (baseline); other times, for remembering object location and color (one added feature); and still other times, for remembering object location, color and orientation (two added features). 

The advantage of this design is that the perceptual complexity of memory items is identical in all conditions, which is important as more complex items may be harder to remember because they are more challenging to perceive in a limited time frame (Eng, Chen, & Jiang, 2005), a factor outside the scope of our study. However, participants may disregard the task instructions or prefer to focus us a certain feature regardless of the task instructions. Such preferential encoding should be especially noticeable in the ‘any one-feature trial block’ (in which any one of the three features can be probed). We also included a control analysis to detect the selective dropping of the second feature (see Supplementary Materials, Section 2).

Specific Hypotheses and How They Will Be Tested

Hypotheses are summarized in Table 1. There we state only hypotheses in which conditions differ, but for any of them the opposite, null hypotheses also can be demonstrated given our Bayesian methods of inference.

First, according to a capacity increase hypothesis of developmental growth, we might find that older participants retain more objects in the Location condition (Hypothesis H1A) and that this advantage should extend to Location tests within every condition (Hypothesis H1B), with no claim of developmental change in the feature detail for remembered objects. That would fit with suggestions that WM improvement during childhood is due to a discrete increase in visual WM capacity, i.e., that the maximum number of objects that can be held in visual WM increases (e.g., Cowan, 2016) while the level of feature-detail of each successfully encoded object remains constant with age. For instance, Riggs and colleagues (2011) compared memory performance for single- and multi-feature objects across three age groups: 7-year-olds, 10-year-olds, and adults. 

While adults remembered more objects than young children, the multi-feature condition did not incur additional performance deficits in any age group, compared to trials in which only one feature could change. This suggests that the number of integrated multi-feature object representations in WM changed with age. However, in Riggs et al.’s (2011) single-feature condition only orientation could change, while in the multi-feature condition, either color or orientation could change. Memory performance for color is typically better than that for orientation (see Cowan & Hardman, 2015; Oberauer & Eichenberger, 2013; Peich, Husain, & Bays, 2013). Indeed, Riggs et al.’s adults also performed equally well in the single- and multi-feature conditions, in contrast to Cowan and Hardman (2015; see also Oberauer & Eichenberger, 2013). 

It is possible that a general boost for color memory masked the detrimental effect of remembering two features. Therefore, to rule out this possibility in this study, we systematically examined memory for some features while varying the demand to remember other features. For example, we examined memory for object location in three conditions: when it alone must be remembered, when location and color must be remembered, and when location, color, and orientation all must be remembered.

The second potential outcome accords with the feature enrichment hypothesis of developmental growth. According to that hypothesis, all participants could retain an equal number of objects, but older participants will retain more feature detail for each object. For everyone, it was expected that performance on location memory would decline as the need to retain additional features is added (Hypothesis H2A), but according to the feature enrichment hypothesis, this decline will be steeper for younger participants (Hypothesis H2B). Similarly, color memory should decline when memory for orientation is also required (Hypothesis H2C), and according to the feature enrichment hypothesis, this decline should be steeper for younger participants (Hypothesis H2D). 

Improved memory stemming from increasingly detailed representations of remembered objects, rather than an increase in the number of objects, fits with the literature suggesting that infants can remember about three objects at once (Oakes & Luck, 2013; Zosh & Feigenson, 2015), but with limited feature-detail (see Zosh & Feigenson, 2012). This account might also align with accounts of feature-binding deficits in young children, compared to conditions when only one feature is required (see Cowan et al., 2006; Lorsbach & Reimer, 2005). If the feature enrichment hypothesis completely accounts for the development of working memory, the assumption is that previous findings of increasing capacity with age were obtained because younger children more often forgot the tested feature (e.g., color), while still retaining knowledge of where about 3 objects were located.

These opposing outcomes (capacity increase vs. feature enrichment) can be checked in a different manner that is not dependent on the special status of any one feature or the total feature load. Specifically, in our final testing block, each trial included only one probe, which could be Location, Color, or Orientation, unknown to the participant until the probe is presented. Based on this trial block, as explained later, we could use a recent WM model to estimate the number of trials for which at least one feature is known, a type of k that could increase with development (Hypothesis H1C), and alternatively, we could also estimate whether the total number of features known for at-least-partly-known objects increases with development (Hypothesis H2D) (cf. Cowan et al., 2013; Hardman & Cowan. 2015; Oberauer & Eichenberger, 2013).

A third potential outcome is that older participants would retain more objects and more feature detail for those objects; both the capacity increase and the feature enrichment hypotheses could be correct. As children develop, both parameters may increase and contribute to improved WM ability. There are similar, though not identical, findings in the literature. In particular, recent work has shown that both the capacity (the number of objects in WM) and the precision with which such objects were remembered were greater in adults than children at a set size of two objects (Sarigiannidis, Crickmore, & Astle, 2016). Increases in both the number of items retained and precision with age were also found for memory for tone series (Clark et al., 2018). Similarly, children’s memory precision when reproducing the orientation of one or three bars improved with age, using both cross-sectional (Burnett Heyes et al., 2012) and longitudinal data (Heyes, Zokaei, & Husain,2016). This age benefit was significantly greater in a three-bar than the one-bar condition, which may reflect that the number of high-precision slots increased with age. We were not investigating precision and do not know if precision of any feature plays a comparable role in development to what we are investigating, the number of features per object.

Finally, children and young adults theoretically could retain equal objects and levels of feature detail. This overall null hypothesis is unlikely given past research, as young adults typically outperform children and adolescents on visual WM tasks (e.g., Brockmole & Logie, 2013; Cowan et al., 2005, 2006; Gathercole et al., 2004; Isbell et al., 2015; Riggs et al., 2006).

Method

Proposed Sample Characteristics

We planned to recruit 40 children (6 – 7 years old), 40 early adolescents (11 – 13 years old), and 40 college-age adults (18 – 25 years old). This sample size was selected following Bayes Factor design analysis simulations and simulation of Bayesian posteriors (see details below). Moreover, if evidence for age differences in k between age groups (Hypothesis H1A, Analysis 1) were inconclusive (defined as a Bayes Factor between 0.33 and 3), we would recruit 10 more participants per age group and reanalyze, a maximum of two times (see Schönbrodt & Wagenmakers, 2018). Eligible participants reported having normal or corrected-to-normal vision and normal color vision and speaking English fluently. The study has been approved by the local research ethics committee (Institutional Review Board) at the University of Missouri. All participants (or, for child participants, their legal guardians) provided informed consent before participation. We outline detailed exclusion (and replacement) criteria in the Supplementary materials (see Section 1), based on near-floor performance (below .55 proportion correct), near-ceiling (above .97), performance below 90% on the perceptual matching task, as well as failure to complete the task.

Demographic Information to be Collected

Participants and their parents or guardians reported their age (measured in months) and gender (female, male, other/prefer not to say), and we will report mean age and gender ratios by age group. Optional demographic information about participant race and ethnic group was gathered for research participation monitoring purposes and federal funding requirements but was not reported or analyzed in this study.

Experimental Procedure

Overview.—Our original plan was to collect data in person. However, due to due COVID-19 pandemic, data was collected online through an online video call with the experimenter. Therefore, participants did not receive books and stickers as originally planned. While undergoing written and oral consent procedures, participants learned of cash payment. The phases of the experiment, in order, included participant instructions and engagement, a perceptual matching task, titration of set sizes to adjust the difficulty to accommodate individual differences in ability level, and the experiment proper. The titration procedure was based on an array of multi-featured cats followed by a probe location at which a cat was or was not placed, with recognition of that location tested. The experiment proper included trials with set sizes equal to and one above the result of titration, divided into trial blocks with each trial including one probe (testing location), two probes (testing location and color), and three probes (testing location, color, and orientation) in that order or the reversed order. Finally, each participant will complete a block in which, on every trial, any one feature (Location, Color, or Orientation) is probed. We estimate that the total time of testing will last between 45 and 55 minutes including all procedures.

Participant instructions and engagement.—Before starting the experiment, the experimenter will use a cover story to improve task understanding and engagement. They will tell participants that we need help figuring out which cats were having fun at a birthday party (e.g., was a cat with a hat of this color at the party?). When the probe is the same as an item in the memory array (‘the cats at the party’), participants should press ‘YES’, if it is different from all such items, they should press ‘NO’. 

All participants will see on-screen task instructions before starting the experiment, while the experimenter reads them aloud. On-screen, written instructions will also appear before each new block of trials (see Supplementary Materials, Section 8, for details on participant instructions). Participants will receive feedback after each trial; a green tick-mark (✓) will indicate correct, and a light red cross (✗) incorrect, responses. Also, at the end of each block, participants will see numerous green tick marks on the screen, which represent all correctly answered trials, as well as a moving progress bar indicating how much of the study they have completed. Younger participants will receive stickers as further encouragement. An experimenter was available online via virtual communication software for questions and encouragement.

For more information:1950477648nn@gamil.com