Why Does Visual Working Memory Ability Improve With Age: More Objects, More Feature Detail, Or Both? A Registered Report Part 3

Next, for the comparisons of k values between age groups (see Table 1, Analysis 1, 2, 3, and 6) we used a Bayes Factor Design Analysis (BFDA; Schönbrodt & Wagenmakers, 2018).

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This method is based on the concept of Bayesian hypothesis testing and model comparison (Jeffreys, 1961; Kass & Raftery, 1995; Wagenmakers et al., 2010; Wrinch & Jeffreys, 1921). Using effect sizes in the field, with 40 participants per group, 97.9% of samples showed evidence for H1 (BF > 6), 2.1% were inconclusive (0.1667 < BF < 6), and 0.0% showed evidence for H0 (BF < 0.1667).

For further details on our sample size determination procedures see the Supplementary Materials, Section 7. In addition to the procedures we have mentioned here, we include estimates for smaller effect sizes, taking into account possible publication bias, and we estimate the ability to find evidence for the null hypothesis.

Outcome-neutral criteria that must be met for successful testing of the stated hypotheses

Absence of near-floor and ceiling level performance.—We excluded and replaced such participants (see exclusion criteria above for details).

Selective dropping of the second feature.—Participants may strategically focus only on one feature in blocks in which they are asked to also remember color and orientation. If so, they may essentially perform the one-feature task again. It will be difficult to know whether this was driven by a decision to ignore a secondary feature, or an inability to remember it. To err on the side of caution, we report the number of participants whose color memory performance is less than .55. We re-run the main analyses without such participants (see Supplementary Materials; Table S1, Analysis 2), to see whether it influences the main outcome.

Accounting for attentional lapses (which may be more prominent in children). —We examined the number of trials with Reaction Times over 5 seconds, which are presumably from lapses of attention. If they exceed 5% of total overall experimental trials, we would run a separate control analysis without such trials (see Supplementary Materials; Table S1, Analysis 3).

Timeline for completion of the study and proposed resubmission date if Stage 1 review is successful

The completion date depends on official recommendations and university policy regarding social distancing due to the current global pandemic (COVID-19). Currently, the university is closed but, once we can return to normal operations, we estimate the completion of data collection in six months and completion of analysis and writing in an additional three months.

Online data collection

As this registered report received in-principle acceptance at the start of the COVID-19 pandemic, we were granted permission to collect the data virtually instead of in person. This led to some procedural changes, which we have outlined above. The preregistered protocol with in-principle acceptance was uploaded to the OSF before data collection (https://osf.io/ 59ekp/).

Final Sample of Participants

One child participant turned eight before completing their second session and was excluded from all analyses. Following prespecified rules, five participants were excluded from the main analyses and replaced (location-only performance < .55: one child and adolescent, location-only performance > .97: one adolescent and two adults). However, these five participants performed in the specified range in the any-one-feature condition and were included in those analyses. Overall, the study included data from 49 children (M = 6.6, SD = 0.5 years, 65.3% female, 36.7% male), 50 adolescents (M = 11.9, SD = 0.9 years, 46.0% female, 44.0% male) and 50 adults (M = 19.7, SD = 1.8 years, 72.0% female, 26.0% male, 2.0% non-binary). All participants resided in the United States, except for two participants who resided in the United Kingdom.

Results

To compare the capacity increase and the feature enrichment hypotheses of WM development, we included various measures of both object and feature memory. We will discuss our results in the following order: First, we will focus on our different estimates of WM capacity, including standard estimations based on performance for location memory probes, as well as estimations of the number of objects for which at least one feature was known. Then, we discuss feature memory – specifically, the number of known features within objects for which at least one feature was known. Finally, we discuss how asking participants to remember additional features impacted location and color memory, respectively.

WM Capacity estimates (k)

First, we explored developmental changes in object memory (WM capacity). WM capacity estimates (k) for location memory varied by age group in the location-only (‘was-somethingthere’) condition (BF10 = 6.1 × 1020, children: M = 1.7, SD = 0.5, early adolescents: M = 3.2, SD = 0.9, adults: M = 3.7, SD = 1.0. Children vs. adults, d = −2.67). Similarly, location k-estimates also varied by age group when participants also needed to remember color, and/or orientation (age group effect BF10 = 3.7 × 1070) with inconclusive evidence against a load effect (BF01 = 2.9), and evidence against an age group × load interaction (BF01 = 32.0). These values are shown in Figure 3. For details about how we obtained these hierarchical k-estimates see the Supplement, Section 9. Overall, these WM capacity analyses suggested that older participants tended to remember more objects than younger participants.

The estimated number of objects for which at least one feature was known

In this alternative measure of object memory capacity, an object was considered remembered if at least one of its three features – location, color, or orientation – was known. Older participants once more tended to remember more objects than younger participants. In the block in which each participant worked in their estimated span set size + 1 item, the estimated number of objects for which at least one feature was known was higher in older participants (BF10 = 1.9 × 103, Children: M = 4.2, SD = 1.1, Adolescents: M = 5.1, SD = 1.3, Adults: M = 5.6, SD = 1.2, Children vs. Adults, d = −1.15). When the set size was fixed at three items for all participants, age differences were again observed (BF10 = 1.3 × 104, Children: M = 2.93, SD = 0.06, Adolescents: M = 2.98, SD = 0.04, Adults: M = 2.98, SD = 0.04. Children vs. Adults, d = −1.12). This reliable result was obtained despite near-ceiling-level performance on the latter metric.

The number of known features within objects for which at least one feature is known

Finally, the number of known features within objects for which at least one feature is known appeared to differ between age groups when participants were asked to remember their assigned set size + one item (BF10 = 10.4, Children: M = 2.0, SD = 0.17, Adolescents: M = 2.2, SD = 0.23, Adults: M = 2.2, SD = 0.24. Children vs. Adults, d = −0.76). A similar pattern was observed when all participants had to remember three objects (BF10 = 2.1 × 1010, Children: M = 2.1, SD = 0.18, Adolescents: M = 2.4, SD = 0.25, Adults: M = 2.5, SD = 0.23, Children vs. Adults, d = −1.92). This suggests that, within known objects, older participants remembered more object features than did younger participants.

Memory for feature detail under memory load: Location

Next, we tested whether memory load (i.e., the number of features participants were told to remember and subsequently tested on) affected WM location performance. Location was the feature that was tested with a load of one, two, or three features. Using hierarchical Bayesian logistic regression, we found credible evidence that overall memory performance decreased as load increased (η = −0.23; SE = 0.08, 95% Bayesian Credible Interval; BCI [−0.39, −0.08]). There was also credible evidence that the children were outperformed both by the adolescents (η = 0.82; SE = 0.17, 95% BCI [0.49, 1.16]), and adults (η=1.18; SE=0.17, 95% BCI [0.86, 1.51]). There was no credible evidence that the load effect differed between children and adolescents (η=0.14; SE=0.10, 95% BCI [−0.06, 0.34]). However, the load effect seemed to differ between children and adults (η=0.22; SE=0.10, 95% BCI [0.03, 0.42]), see Figure 4. Yet, the BF in favor of the model not including the load by age group interaction was 220.4 over a model including this interaction.

Exploratory Analysis: Children vs. Adults.—Since this BF reflects a comparison with the full model (also including contrasts between children and adolescents), we did an exploratory analysis including only the children and adults. The BF in favor of the model not including this interaction was 3.9 over a model including it. Thus, the evidence is inconclusive regarding whether increasing feature load affected children’s memory performance more than adults. Interestingly, children’s average k-estimates appeared quite consistent across loads (see Figure 3) compared to the decrease observed for location memory accuracy (see Figure 4). These differences might indicate shifts in the proportion of hits and false alarms across load conditions, which influences k-estimates differently than accuracy scores.

Decreasing or increasing feature load.—Half of the participants experienced an increasing feature load, and the others a decreasing feature load. A planned control analysis suggested that feature-load order did not affect memory performance (decreasing feature load: M = .78, SD = .12, increasing feature-load: M = .77, SD = .12, evidence against including the block order factor, BF10 = 34.9). See Supplement, Section 10 for the complete model output.

The selective dropping of color memory.—Next, in our second planned control analysis, we excluded participants who performed less than <.55 in the color condition, which might indicate ‘selective dropping’ of that feature, which would be likely if participants decided to focus only on location memory because it was probed first. This excluded sixteen children, five adolescents, and two adults. We reran the analyses and the patterns were similar to those in the primary analysis (see Supplement, Section 11 for details).

Memory for feature detail under memory load: Color

Next, we tested whether an additional orientation memory impaired color memory load. We conducted a similar Bayesian regression analysis for color memory (comparing color memory in the Location + Color condition with color memory with Location + Orientation) by age group. As per the prespecified exclusion rule, 23 participants (16 children, five adolescents, and two adults) were excluded due to performing below <.55 in the color memory condition. We found credible evidence that memory performance decreased as load increased (η = −0.62; SE = 0.23, 95% Bayesian Credible Interval; BCI [−1.06, −0.19]). While there were credible performance differences between children and adolescents (η = .62; SE = 0.28, 95% BCI [0.07, 1.18]) and children and adults (η=0.99; SE=0.27, 95% BCI [0.45, 1.51]) in the model without the interaction, this was not the case in the model including the interaction (children vs. adolescents: η=0.01; SE=0.48, 95% BCI [−0.92, 0.97] and children vs. adults: η=0.30; SE=0.47, 95% BCI [−0.60, 1.26]), likely reflecting how the exclusion of 16 of the poorest performing children reduced age differences. However, the load effect seemed equal in both younger children and adolescents (η=0.42; SE=0.28, 95% BCI [−0.12, 0.98]), and younger children and adults (η=0.48; SE=0.27, 95% BCI [−0.03, 1.00]). The BF in favor of the model not including this interaction was 85.9 over a model including it.

Exploratory Analysis: Children vs. Adults.—Contrasting children and adults only, the BF in favor of the model not including this interaction was 3.5 over a model including it. Similar to the analysis above, the evidence that increasing feature load affected children’s memory performance more than it affected adults’ memory performance appears inconclusive.

Reaction Time

The number of trials with Reaction Times over 5 seconds did not exceed 5% of total overall experimental trials for either feature (Location, 4.1%, Color: 3.6%, Orientation: 3.3%). Thus, no planned control analysis excluding higher RT trials was needed.

Discussion

We sought to address whether developmental improvements in visual WM ability are expressed by location-memory (whether there was an object in a probed location, taken to reflect the presence of an object file as in the concept of Kahneman & Treisman, 1984), by feature completeness of object representations, or by both qualities. Overall, our results suggested that older participants retain more objects and more feature detail for those objects, supporting both the capacity increase and the feature enrichment hypotheses. Thus, as children develop, both the number of objects and the number of features remembered within those objects, appear to increase and contribute to improved WM ability. This aligns with previous research using different methods and materials (e.g., Clark et al., 2018; Sarigiannidis et al., 2016). We discuss the observed age differences in these two parameters in more detail next.

Do children remember fewer objects?

We observed strong evidence that younger participants remembered fewer objects than adolescents and adults. Average child WM capacity (k) in our ‘was-it-there’ condition was just under two items, while adolescents’ capacity was just over three, and adults’ capacity was closer to 4 items. Thus, WM capacity doubled between the early school years (6–7 years) and adulthood. This suggests that WM improvement during childhood is partially driven by a discrete increase in the maximum number of object locations that children can hold in visual WM (e.g., Cowan, 2016) and provides evidence for the capacity increase hypothesis of WM development.

Does increasing the complexity of the memory task (i.e., asking participants to remember more features per object) influence performance equally across development?

The answer to this question was less straightforward. We observed some evidence that the additional load requirement was more detrimental for children than adults for location memory. However, this effect was supported by credible intervals but was ‘inconclusive’ when using a Bayes Factor model comparison approach. The color memory analysis provided similar weak evidence against differential effects of additional load in the different age groups.

For these contrasts, we manipulated load demand through the instructions, while stimuli were consistent (i.e., each cat had a location, a color, and an orientation) across trials, keeping the perceptual load consistent. Our procedure differs from previous research in several ways. For example, we focused on location memory and used a titration procedure, so that participants performed the task at different set sizes, based on their memory ability. Some previous research has suggested that additional feature load was detrimental to young adults’ memory performance (Cowan & Hardman, 2015; Oberauer & Eichenberger, 2013). However, others found that increased feature load did not impact either adults' or children’s memory performance (Riggs et al., 2011). Our analyses do not provide clear evidence for or against age differences in this instructional load effect. Next, we discuss our final contrasts, which were devised to directly compare the capacity increase vs. feature-enrichment hypotheses in a manner that is not dependent on the load instruction.

Capacity increase vs. feature-enrichment

The number of objects for at which at least one feature was known.—We estimated the number of objects for which at least one feature was known based on an experimental condition where either location, color, or orientation could be probed. We observed age differences in this measure, further supporting the capacity increase hypothesis. Notably, these estimates were higher than the traditional k-estimates – children remembered at least one feature for just over four objects, adolescents for just over five, and adults for 5.6 items (see Figure 3). These results provide a potential explanation for the ‘infant paradox,’ i.e., evidence that infants seem able to hold more objects in WM (up to three objects, Ross-Sheehy, Oakes, & Luck, 2003; Zosh & Feigenson, 2015) than school-aged children (less than two items; for example, 1.7 objects in this study). As discussed above, infant memory is measured using simpler paradigms which allow noticing a change in one of many features (e.g., one item might be a doll, the other a ball, Kibbe & Leslie, 2019). Our measure of the number of objects for which at least one feature was known may provide a fairer comparison to infant measures, as it credits broader feature knowledge.

This more lenient measure of capacity also may explain discrepancies between how many colored squares participants believe they can hold in mind and their actual WM capacity, estimated using color probes (Blume, 2018; Forsberg et al., 2021b). Indeed, subjective memory ratings may capture a sense of knowing something about the object rather than remembering the probed feature specifically. Finally, these values appear higher than traditional k estimates of 3–4 objects in young adults (c.f. 5.6 objects for which at least one feature was observed in this study). This suggests that perhaps standard capacity estimates obtained by probing memory for one feature (e.g., color) may underestimate the number of objects for which something is known – be that the location, color, or orientation. However, arguably, location may have a special role in visual processing (see Tsal & Lavie, 1988; Yousif et al., 2021), and our location test may allow spatial perceptual grouping, which could potentially ‘inflate’ these capacity estimates (see Brady & Alvarez, 2015; Morey, 2019).

The estimated features known per object.—Within the known objects, older participants appeared to remember more features. Specifically, adults and adolescents remembered 2.2 out of 3 features per known object, compared to 2.0 features in children. This supports the feature-enrichment hypothesis of visual WM development. Notably, the development of feature knowledge appeared more pronounced at earlier developmental stages (i.e., the difference between childhood and adolescence appeared more prominent than the difference between adolescence and adulthood).

How do these findings relate to fundamental theories of cognitive development?

Different theories of cognitive development propose different mechanisms for developmental increases in WM capacity (for a review, see Cowan, 2022). In an Empiricist framework, WM changes are likely attributed to a child’s learning history while a Nativist framework would emphasize the role of the brain’s biological growth. A Cognitivist perspective would focus on identifying sub-processes that change with age, based on both learning and biological maturation (Cowan, 2016). Cognitivists also emphasize the interaction between the development of obligatory and voluntary processes that contribute to performance on WM capacity tests. For example, developmental capacity increases may be driven by increased use of grouping strategies, attentional refreshing of information, and verbal rehearsal. Increases in these processes may all be driven by developmental growth in a more general process, such as self-directed use of attention (see Cowan, 2022). This idea seems aligned with Dynamic Systems Theory (Spencer, 2020), which suggests that development in different processes may be captured more parsimoniously by considering more general developments in how the brain implements sustained activation. In such Dynamic Systems Theory accounts, basic changes in neural functioning, perhaps combined with environmental input (see Witherington & Margett, 2011), are believed to produce developmental capacity growth (e.g., Perone et al., 2021). In the context of our findings, the same improved ability for sustained activation – explained by the same biological brain maturation – may improve the ability to hold both object and feature memory in mind, without necessarily considering these processes as separate. Based on our data, we cannot discern whether the observed developmental increases in object and feature memory are driven by improvements in the same underlying developmental process or separate processes. However, our findings of developmental increases in both parameters suggest that a shared process is theoretically plausible, and may be a parsimonious explanation.

Developmental increases in both object and feature-memory: Theoretical and Practical Implications

Understanding the mechanisms of developmental WM constraints has implications for successful problem-solving in the classroom, as well as long-term learning (see Forsberg et al. 2021a; 2021b; 2021c). Our findings make an important theoretical contribution as they provide evidence for developmental increases in both object and feature memory – rather than one or the other. This suggests that as children mature, they can successfully hold: 1) an increased number of informational chunks, and 2) increasingly complex informational chunks, in WM. Our results also provide a likely explanation for the ‘infant paradox’ (i.e., seemingly better object memory in infants than in school-aged children), by demonstrating how children’s (and adults’) object memory capacity may be higher than typical estimates if broader feature knowledge is included in the memory estimate. Finally, our results align with conceptualizations of WM as being limited by both the number of object ‘slots’ (Adam et al., 2017) and the ‘precision’ (or featural detail) of memory representations (Ma et al., 2014).

To conclude, as children develop, both the number of objects and the number of features remembered within those objects, appear to increase and contribute to improved WM ability – supporting both the capacity increase and the feature enrichment hypotheses of WM development. While the mechanisms driving these improvements are yet to be determined, one parsimonious explanation would be developmental growth in a more general process, such as self-directed use of attention (see Cowan, 2022), perhaps underpinned by developments in how the brain implements sustained activation (e.g., Spencer, 2020). Our findings have important implications for the theoretical understanding of visual WM capacity increases across childhood, as they suggest that developmental improvements cannot be explained exclusively by either increases in the number of informational slots, or the ability to remember richer representations. Instead, developmental WM capacity growth is characterized by improvements in both these cognitive parameters.

Supplementary Material

Refer to the Web version on PubMed Central for supplementary material.

Acknowledgments

This research is supported by NIH Grant R01 HD-21338 to Cowan. We thank Bret Glass, Nathaniel Greene, Stephen Rhodes, Yu Li, and other members of the Working Memory Laboratory at the University of Missouri for helpful comments and advice. The original data, analysis code, and experimental script are available online.

References

1.Adam KC, Vogel EK, & Awh E (2017). Clear evidence for item limits in visual working memory. Cognitive Psychology, 97, 79–97. [PubMed: 28734172]

2.Allen RJ, Baddeley AD, & Hitch GJ (2006). Is the binding of visual features in working memory resource-demanding? Journal of Experimental Psychology: General, 135(2), 298 – 313. [PubMed: 16719655]

3. Bayliss DM, Jarrold C, Gunn DM, & Baddeley AD (2003). The complexities of complex span: explaining individual differences in working memory in children and adults. Journal of Experimental Psychology: General, 132(1), 71 – 92. [PubMed: 12656298]

4. Blume CL (2018). School-aged children’s awareness of their working memory contents. Doctoral dissertation, University of Missouri, Columbia, MO.

5. Brady TF, & Alvarez GA (2015). No evidence for a fixed object limit in working memory: Spatial ensemble representations inflate estimates of working memory capacity for complex objects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 921. [PubMed: 25419824]

6.Brockmole JR, & Logie RH (2013). Age-related change in visual working memory: a study of 55,753 participants aged 8–75. Frontiers in Psychology, 4, 12. [PubMed: 23372556]

7. Burnett Heyes S, Zokaei N, van der Staaij I, Bays PM, & Husain M (2012). Development of visual working memory precision in childhood. Developmental Science, 15(4), 528–539. [PubMed: 22709402]

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