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. 2025 Jun 27;96(5):1852–1861. doi: 10.1111/cdev.70003

Neural Maturity of Encoding States Supports Gains to Memory Precision in Childhood

Sagana Vijayarajah 1, Margaret L Schlichting 1,✉
PMCID: PMC12379853  PMID: 40579774

ABSTRACT

Despite substantial improvements to memory precision in childhood, the neural mechanisms underlying these changes remain unclear. Here, 40 children (7–9 years; 22 females, 18 males; majority White) and 42 adults (24–35 years; 22 females, 20 males; majority White) modulated their approaches to memory formation—focusing on the specific details to encourage precision or general category to encourage imprecision. Children and adults alike formed more precise memories under the specific task, yet adults' neural states were more cohesive as a group than were children's. Moreover, children's adoption of an adult‐like neural approach explained age‐related gains in memory precision (β = 0.08). Development unfolds as children—initially varying in their memory control—eventually adopt an adult‐like approach that benefits memory precision around age 9.

Keywords: adults, cognitive control, episodic memory, fMRI, memory discrimination, pattern similarity

One key function of memory is that it allows us to recall particular instances, discriminating them from other similar events (e.g., Bencze et al. 2021; Kim and Yassa 2013; McClelland et al. 1995). To support this kind of behavior, memories must be precise—that is, accurately capturing the input in high fidelity so as to prevent (sometimes disadvantageous; Dennis and Turney 2018; Garoff‐Eaton et al. 2006; Gutchess and Schacter 2012; Koutstaal and Schacter 1997; Vijayarajah and Schlichting 2023) generalizations to new stimuli that are similar (Bernstein et al. 2021; Forest et al. 2023; Poirier et al. 2012; Stark et al. 2010). The capacity to form such precise memories emerges over development: Children's ability to represent the precise physical location in allocentric spatial memory tasks (Lambert et al. 2015, 2017) or to discriminate studied items from similar lures (Ngo et al. 2018, 2019) improves drastically over the first few years of life. However, relatively less is known about the development of memory precision across later childhood. Here, we characterize age‐related differences in memory precision across children aged 7–9 years and adults, with a focus on how precision might be improved by modulating one's neurocognitive state during initial experience as memories are first formed.

Most work on the development of memory precision has been carried out in young children (6 years of age and under; Lambert et al. 2015, 2017; Ngo et al. 2018, 2019). However, there are a few studies suggesting that memory precision continues to improve through at least age 8 (Canada et al. 2019; Ghetti et al. 2010; Ghetti and Bunge 2012; Peng et al. 2023; Rollins and Cloude 2018), with potential transitions in the neural substrates supporting precise forms of encoding continuing even later (through age 11 and beyond; Fandakova et al. 2019; Ghetti et al. 2010; Rollins and Riggins 2018; Sastre et al. 2016). In addition, while much research on this topic has centered on the hippocampus given its role in rapidly forming pattern‐separated memories that can support behavioral precision (Canada et al. 2019; Kirwan and Stark 2007; McClelland and Rumelhart 1985; Stevenson et al. 2020; Yassa and Stark 2011), recent data (Johnson et al. 2021; Nash et al. 2021; Wahlheim et al. 2022) and perspectives (Amer and Davachi 2023; Kent et al. 2016) highlight a widespread network of neocortical sites in supporting this process. For example, task goals may encourage top‐down modulation of visual regions in adults (Al‐Aidroos et al. 2012; Bressler et al. 2008; Córdova et al. 2016; Tompary et al. 2016), which could support high‐fidelity representation of the visual information then fed into the memory system (Córdova et al. 2016; Murty et al. 2017; Ranganath et al. 2005; Wing et al. 2020). Underscoring the behavioral significance of such a mechanism, in both adults (Bowman et al. 2019; Davis et al. 2021; Hasinski and Sederberg 2016; Kim and Cabeza 2007; Kuhl et al. 2012) and children (Fandakova et al. 2019; Golarai et al. 2007; Rosen et al. 2018) precise representations of perceptual experience in visual regions have been linked to subsequent memory success. In light of the protracted development of both cognitive control mechanisms (Chatham et al. 2009; Davidson et al. 2006; Luna et al. 2010; Niebaum et al. 2021) that can support the adoption of a particular memory strategy (Abolghasem et al. 2023; Shing et al. 2008, 2010) and hippocampal‐neocortical networks (Blankenship et al. 2017; Calabro et al. 2019; Menon et al. 2005; Paz‐Alonso et al. 2013; Plachti et al. 2023), it follows that the capacity to promote precise memory formation would also continue to develop beyond late childhood. Yet, there is little empirical data speaking to this idea.

We asked whether developmental gains in memory precision may be at least in part attributable to differences in cognitive engagement during initial memory formation, and in particular the ability to orient in a way that may benefit later fine‐grained mnemonic discriminations. Here, children and adults were shown scene photographs during functional magnetic resonance imaging (fMRI) scanning as they performed a cover task. Later, we gave them a surprise memory test emphasizing details, thus requiring a high degree of mnemonic precision to achieve success. We manipulated cognitive engagement during memory formation by instructing participants to adopt one of two tasks during initial photograph viewing: For some scenes, they focused on its general type (i.e., scene category), which we expected to yield imprecise memories. For others, they were to consider the scene's specific, fine‐grained details (i.e., particular scene features), which we reasoned should enhance precision. Our main neural measure of interest was the degree to which the two tasks were reflected in distinct whole‐brain fMRI activation patterns, as an indicator of how closely participants' neural state aligned with task goals.

We then asked how neural states compared across individuals of similar ages. Given prior work in adults, we anticipated a memory advantage (Ambrus 2024; Chen et al. 2017; Richter et al. 2016) associated with shared states (Hasson et al. 2008; Koch et al. 2020; Sheng et al. 2023), as they may reflect top‐down attentive processing of studied content (Song et al. 2021). We also compared neural states across children and adults, reasoning that a given child's similarity to adults serve as a useful index of their neural maturity (Cantlon and Li 2013; Cohen et al. 2022; Moraczewski et al. 2018; cf. Petroni et al. 2018). We entertained several possibilities as to how children's and adults' neural implementation of the instructed tasks and their impact on later memory precision might compare. First, adults and children alike may form more precise memories during the specific task but do so using distinct neural strategies. Such an idea would be consistent with the notion that children have access to a different set of strategies than adults (Lemaire and Lecacheur 2011; Miller‐Goldwater et al. 2021; Shing et al. 2008; Siegler 1996), and thus implement the instruction of focusing on specific details in a different way. If we were to find such a unique but consistent child‐specific neural profile, a child showing an especially adult‐like state would not be expected to show any memory advantage. A second possibility is that even despite showing memory benefits associated with the specific task, children would be more variable in their neural implementation. In this case, states may be more adult‐like in older children, with their alignment to adults (i.e., neural maturity) being behaviorally beneficial. An alternative third possibility is that adults but not children would show memory benefits from the instruction to focus on the specific information during initial experience, which would suggest children's memory precision cannot be modulated by top‐down influences such as instruction.

1. Method

1.1. Participants

Forty children (M = 8.81 years; SD = 0.91 years; 7.11–10.04 years; 22 female and 18 male; 18 White, 13 mixed ethnicity, 5 Chinese, 3 South Asian, 1 Southeast Asian) and 42 adults (M = 29.09 years; SD = 3.21 years; 24.31–35.51 years; 22 female and 20 male; 17 White, 10 Chinese, 5 South Asian, 4 mixed ethnicity, 2 Black, 2 Southeast Asian, 1 Filipino, 1 Latin‐American) from Toronto, Canada participated in this experiment in 2022–2023. Four of these people were excluded for a Total Problems Score in the clinical range on the Child Behavior Checklist/6–18 2 children; Achenbach and Edelbrock (1991) or illness in the scanner (1 child and 1 adult). All participants provided consent/assent and were compensated for their time ($20 CAD per hour). Our participant exclusion criteria and research question were pre‐registered; all analyses were confirmatory except where noted.

1.2. Tasks and Behavioral Analysis

1.2.1. Incidental Encoding

Participants attended to general or specific features of scene photographs during fMRI scanning. Photographs were organized into 24 blocks (5 photographs per block, presented consecutively for 3.5 s followed by a 1 s inter stimulus interval [ISI]; divided into three runs of equal length; 12 each general/specific; Figure 1A; see Methods S1). Cues were presented prior to each block (5 s cue, 1 s ISI) to indicate the task for the upcoming block: Attend to the scene category for general or details for specific. Half of the blocks (6 each general/specific) ended with a question screen (5 s question, 1 s ISI) to ensure participants attended to the correct dimension: Participants indicated which of two photographs matched one from the preceding block on the cued dimension (a different photograph showing the same scene category for general or an identical photograph for specific; Figure 1B). Accuracy on these questions served as our metric of encoding task performance.

FIGURE 1.

FIGURE 1

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Tasks and memory behavior. (A) Encoding task. Top, cues (here, black star) preceded blocks and indicated participants' task for the upcoming block—orienting to either the general (scene category) or specific features (picture details) of each photograph. Bottom, each horizontal bar depicts a block from an example run. Blocks were ordered such that specific blocks were equally likely to be preceded by general (magenta) and specific (teal) blocks and vice versa. Blocks from an orthogonal baseline task (gray; see Methods S1) were also presented at the start, middle, and end of each run. Each functional run also included null fixation time at the start (3 s) and end (6 s) to account for stabilization and lag of the MR signal, respectively. (B) Question screens. Select blocks ended with a question screen that asked participants to either indicate the matching photograph (specific task, top) or scene category (general task, bottom). (C) Memory test. Left, participants were shown studied, lure (here, a visually similar classroom photograph), and unrelated new (bedroom) photographs at test. Right, participants indicated whether the photograph was or was not presented at encoding during the stimulus presentation or response window (depicted). (D) Subsequent memory precision for photographs from the specific task. Left, memory precision (d′) of studied Vs. lures as a function of age group (x‐axis). Larger circles represent group mean; points are individual participants; intervals are confidence intervals around the group mean. Right, Across‐participants correlation of children's age (as a continuous measure; x‐axis) and memory precision (y‐axis). Points are individual participants and band around the line represents the standard error. *p < 0.05, **p < 0.01.

1.2.2. Memory Test

After incidental encoding, participants completed a surprise old/new recognition test with photographs from encoding (“studied”; 120 photographs), similar new photographs yoked to each studied photograph (“lures”, 120 photographs), and photographs depicting new scene categories (“unrelated new”; Figure 1C). Participants were shown both studied photographs and their yoked lures at test. The photographs were presented one at a time and in a randomized order, such that either the studied photograph or its yoked lure could have appeared first at test. Participants indicated during the photograph presentation (2 s) or immediately following response window (2 s) whether the photograph had been shown at encoding (Figure 1C). Memory discrimination (d′; Banks 1970) of studied from lure photographs was our measure of memory precision.

1.3. MRI Data Analysis

We collected whole‐brain fMRI data during incidental encoding (2 mm isotropic voxels; TR = 1.5 s). Raw functional data were preprocessed, warped to the MNI152NLin2009cAsym template, and then submitted to GLM analyses to estimate neural patterns for each of the 24 blocks presented at encoding (12 general and specific each) in every participant (see Supplementary Methods for fMRI preprocessing and modeling details).

1.3.1. Identifying Task States in Children and Adults

We compared neural patterns for general and specific tasks to ask whether they evoked distinct brain states during encoding (i.e., whether neural patterns were more similar for a given participant performing the same versus different tasks). To do this, we correlated patterns for each pair of blocks (Pearson correlations followed by Fisher's z transformation; restricted to across‐run comparisons only; Mumford et al. 2012) and then averaged across same‐ (general–general; specific–specific) and different‐ (general–specific) task comparisons to characterize evidence for a reliable task‐related neural state (same > different task similarity; hereafter termed: “task state”) within participants. We did not do this analysis separately for the general and specific tasks because our goal was to identify a state that differentiated between the two tasks. We identified these states at (1) the whole brain level (i.e., in our mask; Supplementary Methods: Whole‐brain gray matter mask) using paired t‐tests and (2) on a voxelwise basis using a searchlight approach (Supplementary Methods: Voxelwise evidence of brain states).

1.3.2. Comparing Task States Across Participants

We also examined neural alignment across participants by quantifying the similarity of their evoked brain states to those of (other) children and adults. Specifically, for each participant, we correlated their voxelwise z‐statistics map of task state evidence with those from all other participants (Pearson correlation; Fisher's z‐transformed). We then averaged across comparisons to all children and adults, separately. This process yielded one value that reflected each participant's neural alignment with all (other) children and another value that reflected their neural alignment with all (other) adults. We then related participants' neural alignment to children/adults to subsequent memory precision using across‐participants linear regressions (R Core Team 2021) and a follow‐up exploratory mediation analysis (Revelle 2024; R Core Team 2021; 10,000 iterations).

2. Results

2.1. Behavior

Performance in both encoding tasks was well above chance in each age group (t‐test vs. 0.50; all p < 6.96 × 10−11, all Cohen's d > 1.54; Figure S1) and did not vary by task type (p = 0.20), suggesting both children and adults performed the tasks as instructed. Performance instead differed by age group (F = 15.16, p = 2.11 × 10−4, η 2 = 0.10), with adults outperforming children overall. At test, memory precision was above chance in both age groups and for both tasks (t‐test vs. 0; all p < 5.42 × 10−8 and all d > 1.17), with additional age group and task differences: Adults had more precise memories than children overall (main effect of age group: F = 9.79, p = 2.52 × 10−3, η 2 = 0.09; Figure 1D), and both groups formed more precise memories during the specific versus general task (main effect of task: F = 30.04, p = 5.75 × 10−7, η 2 = 0.09; no interaction of task and age group: p = 0.18). Moreover, age within the child group was positively correlated with memory precision for photographs that appeared during the specific (β = 0.26, SE = 0.11, t = 2.29, p = 0.03, Figure 1D) but not the general (p = 0.17) task. Therefore, orienting to specific versus general features benefited memory precision in both age groups, with older children especially able to form more precise memories under those conditions than younger ones.

2.2. More Widespread Neural Task States in Adults Than Children

We next asked whether children and adults showed evidence of reliable neural states during the encoding tasks (Figure 2A). Across the whole brain, adults (t = 5.93, p = 5.85 × 10−7, d = 0.93) but not children (p = 0.72) showed evidence of such states, in that blocks from the same task were more similar than blocks from different tasks. We saw the same pattern when subsampling participants to compare high‐performing children to low‐performing adults with worse memory (Supplementary Results), suggesting this effect is due to age rather than poorer memory. We next characterized where in the brain these states were represented using a searchlight, separately for children and adults. Many regions showed significant evidence of task states in adults, including ventral visual, parietal cortex, and lateral frontal regions (Figure 2B and Table S1). By contrast, only two clusters represented task states in children: one in lateral occipital cortex and one in inferior frontal sulcus. Therefore, while in adults task states were robust and distributed across the brain, in children such effects were more limited and present in only a few circumscribed regions.

FIGURE 2.

FIGURE 2

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Task state analysis and results. (A) Analysis approach. We characterized task states as greater similarity for neural patterns from the same (here, one comparison between two example specific task blocks; black arrow) versus different (two comparisons between a specific and general block; gray arrow) tasks. This analysis was performed in a searchlight, with the searchlight centred on every voxel in the brain (depicted in bottom with the pattern extracted from one sphere in the brain). (B) Pattern similarity searchlight results showing regions of significant task states in children (top) and adults (bottom). Clusters are significant after correction for multiple comparisons; brighter shades represent higher z‐values. (C) Neural alignment to children (values below 0) or adults (values above 0; y‐axis) as a function of age group (x‐axis). Larger circles represent group mean; points are individual participants; intervals are confidence intervals around the group mean. Significance markers indicate a difference from zero. ****p < 0.0001.

2.3. Adults' States Are Cohesive as a Group, While Children's Are Idiosyncratic

We reasoned that children's relatively more localized neural state representation may reflect their more idiosyncratic nature. To test this possibility, we first computed each participant's neural alignment (i.e., pattern similarity) with all other participants, and then separately averaged across comparisons with children and adults. Because participants' neural alignment to the two age groups were correlated (i.e., participants who showed greater similarity to adults also showed greater similarity to children; β = 0.68, SE = 0.21, t = 3.26, p = 1.68 × 10−3), we performed our analyses using a difference score (alignment to adults—children) to characterize the unique contribution of each alignment type (Figure 2C). We found that adults' states were more aligned with other adults than they were with children (t‐test vs. 0; t = 7.32, p = 6.70 × 10−9, d = 1.14), suggesting consistency across adults in their neural approach to the tasks. By contrast, children's states were not reliably aligned to either age group (p = 0.11) suggesting they leveraged idiosyncratic neural approaches.

2.4. Neural Alignment to Adult Over Child States Mediates Age‐Related Increases in Memory Precision

To test whether older children were more likely to adopt an adult‐like neural state to the benefit of memory precision than younger children, we asked how children's variability in their neural alignment to each age group relates to age and memory performance. Children's age was positively associated with neural alignment to adults (β = 0.01, SE = 0.01, t = 2.19, p = 0.04; Figure 3A; importantly, this finding was robust to analyses controlling for individual differences among in‐scanner motion; Supplementary Results), such that older children were more similar to adults than younger children were. Children with greater neural alignment to adults also demonstrated greater memory precision associated with the specific task (β = 5.89, SE = 3.94, t = 1.50, p = 0.03; Figure 3A); this relationship was not observed for memory precision associated with the general task (p = 0.72; not depicted). Together, these results suggest that while children as a group leveraged idiosyncratic neural approaches, older children were more likely to adopt an adult‐like approach, which in turn was associated with more precise memories—particularly in the condition that encouraged their formation.

FIGURE 3.

FIGURE 3

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Children's neural alignment to adults is related to their age and behavior. (A) Neural alignment to adults (values above 0) versus children (values below 0) is related to children's age (left; x‐axis) and memory precision (for photographs from the specific task; right; y‐axis). Points are individual participants; band around the regression line represents the standard error. (B) Results of the mediation analysis. Neural alignment to adults versus children was a reliable mediator of age‐related increases in children's memory precision during the specific task (indicated as c on the figure). The effect of age on memory precision was no longer significant when removing the influence of adults versus children alignment from the relationship (c′). *p < 0.05.

Given the significant associations among children's age, memory precision (from the specific task), and neural alignment to adult‐like states (Figure 1D, right and 3A), we next asked whether neural alignment to adults mediated the age‐related gains we observed in children's behavior. We considered neural alignment our mediator as opposed to age because we theorized neural alignment may influence age‐related changes in children's memory precision. We found that indeed, it did (β = 0.08, SE = 0.07, F = 3.81, p = 0.02; Figure 3B). Moreover, the effect of age on memory precision was no longer significant when removing neural alignment to adults (p = 0.14) from the relationship, suggesting that apparent age‐related differences in precision were attributable to neural similarity with adults.

3. Discussion

We sought to characterize the development of memory precision and how it might be enhanced through top‐down control from a neural approach. We found that children and adults alike oriented their attention in accordance with task goals, yielding more precise memories for scenes when they focused on specific details during initial experience. Among 7‐ to 9‐year‐olds, older over younger children showed greater memory precision, providing additional evidence of improvements in the ability to form high‐fidelity memories past the first few years of life (e.g., Canada et al. 2019; Ghetti and Bunge 2012; Rollins and Cloude 2018). At the neural level, while a widespread network of regions coded for task states in adults, in children the same phenomenon was observed only in lateral frontal and occipital cortices. Considering the similarity of whole‐brain patterns across participants revealed consistency in adults, yet idiosyncrasy in children. This idiosyncrasy varied by age: older children were significantly more similar to adults, suggesting convergence toward an adult‐like neural approach. Moreover, this age‐related difference in neural alignment was associated with superior memory precision and fully mediated the association between age and memory behavior.

Brain regions coding for the two distinct tasks were circumscribed in children, becoming widespread in adulthood. The role of a broad frontoparietal network in supporting precise memory formation in the mature brain is in line with recent theories (Amer and Davachi 2023) that have broadened conceptualizations of pattern separation beyond the hippocampus to additionally encompass neocortical influences. There are multiple possible mechanisms that might underlie this result in adults. For example, adults may be engaging with the tasks more deeply and performing additional memory elaboration relative to children (Ghetti and Angelini 2008)—processes known to involve frontal and parietal regions (McCormick et al. 2015; Staresina et al. 2009; Wing et al. 2013) through connectivity with the hippocampus (Schott et al. 2013). The nature of this elaboration may moreover vary according to task goals, being along detailed or more perceptually based lines for the specific task and along conceptual lines for general. Such a mechanism would yield neurocognitively distinct states throughout the brain, as we see here. By contrast, in children, task state coding appeared in just two clusters: lateral prefrontal and visual (lateral occipital) regions. Diffuse yet variable engagement across individual children (Durston et al. 2006) may explain our overall finding of reduced neural consistency in this age group. This signature may also suggest prefrontal modulation of visual processing, which is enhanced during the specific task. This interpretation would broadly be consistent with the univariate activation profile we observed for the specific task, which included lateral prefrontal, superior parietal, and lateral occipital cortex (Figure S2). Together, these signatures could reflect that, despite relatively early development of visual regions (Braddick and Atkinson 2011; Gogtay et al. 2006; Sowell et al. 2003), the top‐down modulation of perceptual processing may be only beginning to emerge in children aged 7–9 years, with benefits to memory precision. Additional refinements to neural cognitive control mechanisms (Bunge et al. 2002; Davidson et al. 2006; Paz‐Alonso et al. 2009) might explain the remaining differences between children's and adults' brains and behavior.

Comparing the consistency of neural task states between children and adults revealed that while adults were consistent as a group, children were variable. These results echo many others across domains. For example, there is greater consistency in how adults sample visual information (Açık et al. 2010; Helo et al. 2017; Rider et al. 2018) and segment continuous input (Benear et al. 2023; Ren et al. 2021) in comparison to children; and these phenomena can have consequences for memory (Benear et al. 2023). Children also show more variable neural responses than adults when viewing even the same movie (Cantlon and Li 2013; Moraczewski et al. 2018). Here, convergence across adults in neural states might indicate their expertise with strategic control of memory (Bunge et al. 2002; Chatham et al. 2009; Niebaum et al. 2021; Shing et al. 2010), as they are better able to follow task instructions, adopt an ideal encoding state, and/or structure their memories in a similar way to other individuals. By contrast, children's greater neural state variability as a group may be explained by a couple of different underlying phenomena: first, an individual child may be consistent in the particular neural states they engage, yet be different from other children; or second, a given child could be variable in the neural signature they evoke across blocks within the same task. We speculate that our data are more consistent with the former possibility, as children and adults similarly enjoy a memory benefit from the instruction to focus on specific information at encoding. Yet, both explanations are in line with the overlapping waves theory of strategy development (Siegler 1996), which suggests that children may have access to one or more strategies that are different from adults, which could in turn lead them to be (neurally) distinct from both adults and other children. Future work will be needed to formally characterize the degree of intra‐individual variability, and ask whether becoming more able to consistently engage a particular state across experiences shows a similar developmental trajectory to the inter‐individual shifts we characterize here.

The present study builds upon prior work characterizing neural alignment across individuals in development, which has been predominantly done using movie‐watching paradigms (Benear et al. 2023; Cantlon and Li 2013; Chen et al. 2017; Cohen et al. 2022; Moraczewski et al. 2018; Petroni et al. 2018). In these studies, the input is held constant (i.e., all participants watch the same movie) and researchers quantify the degree to which different people's neural activity fluctuates similarly over time in response. Such investigations have shown that in many regions, neural responses become more similar across development (Benear et al. 2023; Cantlon and Li 2013; Cohen et al. 2022; Moraczewski et al. 2018). Moreover, comparing children and adults has shown that greater neural maturity in children as they watch an educational TV program is associated with better behavior on separate assessments (Cantlon and Li 2013). Our approach extends this work by deriving neural patterns from fMRI data collected as participants performed a structured task. Moreover, here, stimuli associated with each task differed across participants, meaning neural patterns reflect a processing state evoked by task instructions rather than a response tied to particular input. Other research in adults has leveraged similar task‐based approaches to uncover neural similarities across participants in strategic approaches to encoding (Richter et al. 2016; Vijayarajah and Schlichting 2022) and common representations of learning materials predictive of shared memory contents (Sheng et al. 2023). Applying a similar approach to developmental questions could be a fruitful avenue for many research domains. While we show that a mature, adult‐like neural approach is beneficial in the context of a memory task, a similar method applied to other domains in which children particularly excel—for example, language‐learning—may reveal strikingly different outcomes. It might be the case that children and adults evoke distinct neural states during exposure to a new language, with a more child‐ (rather than adult‐) like state predicting rapid acquisition. While these particular outcomes are highly speculative, our successful use of task‐based alignment methods in children offers promise for myriad future developmental investigations.

Our findings highlight that across 7–9 years of age, older children over younger children were better able to modulate their neural state during initial experience to the benefit of memory precision. We show that age‐related differences in memory precision across children are mediated by alignment to adults' neural states as they engage in memory control. These results underscore the importance of neocortical and top‐down contributions to even precise memory formation, and highlight that the protracted nature of this change can explain differences in memory behavior across childhood.

Author Contributions

S.V. and M.L.S. designed research. S.V. performed research and analyzed data with input from M.L.S. S.V. and M.L.S. interpreted data and wrote the paper.

Ethics Statement

The experimental protocol was approved by the University of Toronto Research Ethics Board.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1.

CDEV-96-1852-s001.pdf (604.1KB, pdf)

Acknowledgments

We thank Dana Huang, Cindy Jean, and Hannah Chang for assistance with recruitment and data collection, and Amy Finn, Katherine Duncan, Michael Mack, and members of the Budding Minds Lab for helpful discussions.

Funding: This work was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN‐2018‐04933), NSERC Discovery Launch Supplement (DGECR‐2018‐00252), a Canadian Institutes of Health Research (CIHR) Project Grant (PJT‐178337), Canada Foundation for Innovation (JELF), Ontario Research Fund (36876), University of Toronto startup funds, and University of Toronto Arts & Science Tri‐Council Bridge Funding to MLS; a NSERC Postgraduate Doctoral Scholarship to S.V.

Data Availability Statement

Analysis scripts and anonymized behavioral data are available on OSF (https://osf.io/9qj8e/).

References

  1. Abolghasem, Z. , Teng T. H. T., Nexha E., et al. 2023. “Learning Strategy Differentially Impacts Memory Connections in Children and Adults.” Developmental Science 26: e13371. 10.1111/DESC.13371. [DOI] [PubMed] [Google Scholar]
  2. Achenbach, T. M. , and Edelbrock C. 1991. Child Behavior Checklist, vol. 7, 371–392. [Google Scholar]
  3. Açık, A. , Sarwary A., Schultze‐Kraft R., Onat S., and König P.. 2010. “Developmental Changes in Natural Viewing Behavior: Bottom‐Up and Top‐Down Differences Between Children, Young Adults and Older Adults.” Frontiers in Psychology 1: 207. 10.3389/fpsyg.2010.00207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Al‐Aidroos, N. , Said C. P., and Turk‐Browne N. B.. 2012. “Top‐Down Attention Switches Coupling Between Low‐Level and High‐Level Areas of Human Visual Cortex.” Proceedings of the National Academy of Sciences of the United States of America 109: 14675–14680. 10.1073/pnas.1202095109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Ambrus, G. G. 2024. “Shared Neural Codes of Recognition Memory.” Scientific Reports 14: 15846. 10.1038/s41598-024-66158-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Amer, T. , and Davachi L.. 2023. “Extra‐Hippocampal Contributions to Pattern Separation.” eLife 12: e82250. 10.7554/eLife.82250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Banks, W. P. 1970. “Signal Detection Theory and Human Memory.” Psychological Bulletin 74: 81–99. 10.1037/h0029531. [DOI] [Google Scholar]
  8. Bencze, D. , Szőllősi Á., and Racsmány M.. 2021. “Learning to Distinguish: Shared Perceptual Features and Discrimination Practice Tune Behavioural Pattern Separation (World).” 10.1080/09658211.2021.1924788. [DOI] [PubMed]
  9. Benear, S. L. , Popal H. S., Zheng Y., et al. 2023. “Setting Boundaries: Development of Neural and Behavioral Event Cognition in Early Childhood.” Developmental Science 26: e13409. 10.1111/desc.13409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bernstein, E. E. , van der Does F., Orr S. P., and McNally R. J.. 2021. “Poor Mnemonic Discrimination Predicts Overgeneralization of Fear.” Journal of Psychopathology and Behavioral Assessment 43: 152–161. 10.1007/s10862-020-09846-z. [DOI] [Google Scholar]
  11. Blankenship, S. L. , Redcay E., Dougherty L. R., and Riggins T.. 2017. “Development of Hippocampal Functional Connectivity During Childhood.” Human Brain Mapping 38: 182–201. 10.1002/hbm.23353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bowman, C. R. , Chamberlain J. D., and Dennis N. A.. 2019. “Sensory Representations Supporting Memory Specificity: Age Effects on Behavioral and Neural Discriminability.” Journal of Neuroscience 39: 2265–2275. 10.1523/JNEUROSCI.2022-18.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Braddick, O. , and Atkinson J.. 2011. “Development of Human Visual Function.” Vision Research 51: 1588–1609. 10.1016/j.visres.2011.02.018. [DOI] [PubMed] [Google Scholar]
  14. Bressler, S. L. , Tang W., Sylvester C. M., Shulman G. L., and Corbetta M.. 2008. “Top‐Down Control of Human Visual Cortex by Frontal and Parietal Cortex in Anticipatory Visual Spatial Attention.” Journal of Neuroscience 28: 10056–10061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Bunge, S. A. , Dudukovic N. M., Thomason M. E., Vaidya C. J., and Gabrieli J. D. E.. 2002. “Immature Frontal Lobe Contributions to Cognitive Control in Children: Evidence From fMRI.” Neuron 33: 301–311. 10.1016/S0896-6273(01)00583-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Calabro, F. J. , Murty V. P., Jalbrzikowski M., Tervo‐Clemmens B., and Luna B.. 2019. “Development of Hippocampal‐Prefrontal Cortex Interactions Through Adolescence.” Cerebral Cortex 30: 1. 10.1093/cercor/bhz186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Canada, K. L. , Ngo C. T., Newcombe N. S., Geng F., and Riggins T.. 2019. “It's All in the Details: Relations Between Young Children's Developing Pattern Separation Abilities and Hippocampal Subfield Volumes.” Cerebral Cortex 29: 3427–3433. 10.1093/cercor/bhy211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Cantlon, J. F. , and Li R.. 2013. “Neural Activity During Natural Viewing of Sesame Street Statistically Predicts Test Scores in Early Childhood.” PLoS Biology 11: e1001462. 10.1371/journal.pbio.1001462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chatham, C. H. , Frank M. J., and Munakata Y.. 2009. “Pupillometric and Behavioral Markers of a Developmental Shift in the Temporal Dynamics of Cognitive Control.” Proceedings of the National Academy of Sciences of the United States of America 106: 5529–5533. 10.1073/PNAS.0810002106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Chen, J. , Leong Y. C., Honey C. J., Yong C. H., Norman K. A., and Hasson U.. 2017. “Shared Memories Reveal Shared Structure in Neural Activity Across Individuals.” Nature Neuroscience 20: 115–125. 10.1038/NN.4450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Cohen, S. S. , Tottenham N., and Baldassano C.. 2022. “Developmental Changes in Story‐Evoked Responses in the Neocortex and Hippocampus.” eLife 11: e69430. 10.7554/eLife.69430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Córdova, N. I. , Tompary A., and Turk‐Browne N. B.. 2016. “Attentional Modulation of Background Connectivity Between Ventral Visual Cortex and the Medial Temporal Lobe.” Neurobiology of Learning and Memory 134: 115–122. 10.1016/j.nlm.2016.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Davidson, M. C. , Amso D., Cruess Anderson L., and Diamond A.. 2006. “Development of Cognitive Control and Executive Functions From 4 to 13 Years: Evidence From Manipulations of Memory, Inhibition, and Task Switching.” Neuropsychologia 44: 2037–2078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Davis, S. W. , Geib B. R., Wing E. A., et al. 2021. “Visual and Semantic Representations Predict Subsequent Memory in Perceptual and Conceptual Memory Tests.” Cerebral Cortex 31: 974–992. 10.1093/cercor/bhaa269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Dennis, N. A. , and Turney I. C.. 2018. “The Influence of Perceptual Similarity and Individual Differences on False Memories in Aging.” Neurobiology of Aging 62: 221–230. 10.1016/j.neurobiolaging.2017.10.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Durston, S. , Davidson M. C., Tottenham N., et al. 2006. “A Shift From Diffuse to Focal Cortical Activity With Development.” Developmental Science 9: 1–8. 10.1111/j.1467-7687.2005.00454.x. [DOI] [PubMed] [Google Scholar]
  27. Fandakova, Y. , Leckey S., Driver C. C., Bunge S. A., and Ghetti S.. 2019. “Neural Specificity of Scene Representations Is Related to Memory Performance in Childhood.” NeuroImage 199: 105–113. 10.1016/j.neuroimage.2019.05.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Forest, T. A. , Abolghasem Z., Finn A. S., and Schlichting M. L.. 2023. “Memories of Structured Input Become Increasingly Distorted Across Development.” Child Development 94: e279–e295. 10.1111/cdev.13940. [DOI] [PubMed] [Google Scholar]
  29. Garoff‐Eaton, R. J. , Slotnick S. D., and Schacter D. L.. 2006. “Not All False Memories Are Created Equal: The Neural Basis of False Recognition.” Cerebral Cortex 16: 1645–1652. 10.1093/cercor/bhj101. [DOI] [PubMed] [Google Scholar]
  30. Ghetti, S. , and Angelini L.. 2008. “The Development of Recollection and Familiarity in Childhood and Adolescence: Evidence From the Dual‐Process Signal Detection Model.” Child Development 79: 339–358. 10.1111/j.1467-8624.2007.01129.x. [DOI] [PubMed] [Google Scholar]
  31. Ghetti, S. , and Bunge S. A.. 2012. “Neural Changes Underlying the Development of Episodic Memory During Middle Childhood.” Developmental Cognitive Neuroscience 2: 381–395. 10.1016/j.dcn.2012.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ghetti, S. , DeMaster D. M., Yonelinas A. P., and Bunge S. A.. 2010. “Developmental Differences in Medial Temporal Lobe Function During Memory Encoding.” Journal of Neuroscience 30: 9548–9556. 10.1523/JNEUROSCI.3500-09.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gogtay, N. , Nugent T. F., Herman D. H., et al. 2006. “Dynamic Mapping of Normal Human Hippocampal Development.” Hippocampus 16: 664–672. 10.1002/HIPO.20193. [DOI] [PubMed] [Google Scholar]
  34. Golarai, G. , Ghahremani D. G., Whitfield‐Gabrieli S., et al. 2007. “Differential Development of High‐Level Visual Cortex Correlates With Category‐Specific Recognition Memory.” Nature Neuroscience 10: 512–522. 10.1038/nn1865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Gutchess, A. H. , and Schacter D. L.. 2012. “The Neural Correlates of Gist‐Based True and False Recognition.” NeuroImage 59: 3418–3426. 10.1016/j.neuroimage.2011.11.078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hasinski, A. E. , and Sederberg P. B.. 2016. “Trial‐Level Information for Individual Faces in the Fusiform Face Area Depends on Subsequent Memory.” NeuroImage 124: 526–535. 10.1016/j.neuroimage.2015.08.065. [DOI] [PubMed] [Google Scholar]
  37. Hasson, U. , Furman O., Clark D., Dudai Y., and Davachi L.. 2008. “Enhanced Intersubject Correlations During Movie Viewing Correlate With Successful Episodic Encoding.” Neuron 57: 452–462. 10.1016/J.NEURON.2007.12.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Helo, A. , van Ommen S., Pannasch S., Danteny‐Dordoigne L., and Rämä P.. 2017. “Influence of Semantic Consistency and Perceptual Features on Visual Attention During Scene Viewing in Toddlers.” Infant Behavior and Development 49: 248–266. 10.1016/j.infbeh.2017.09.008. [DOI] [PubMed] [Google Scholar]
  39. Johnson, S. A. , Zequeira S., Turner S. M., Maurer A. P., Bizon J. L., and Burke S. N.. 2021. “Rodent Mnemonic Similarity Task Performance Requires the Prefrontal Cortex.” Hippocampus 31: 701–716. 10.1002/hipo.23316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kent, B. A. , Hvoslef‐Eide M., Saksida L. M., and Bussey T. J.. 2016. “The Representational‐Hierarchical View of Pattern Separation: Not Just Hippocampus, Not Just Space, Not Just Memory?” Neurobiology of Learning and Memory 129: 99–106. 10.1016/j.nlm.2016.01.006. [DOI] [PubMed] [Google Scholar]
  41. Kim, H. , and Cabeza R.. 2007. “Differential Contributions of Prefrontal, Medial Temporal, and Sensory‐Perceptual Regions to True and False Memory Formation.” Cerebral Cortex 17: 2143–2150. 10.1093/cercor/bhl122. [DOI] [PubMed] [Google Scholar]
  42. Kim, J. , and Yassa M. A.. 2013. “Assessing Recollection and Familiarity of Similar Lures in a Behavioral Pattern Separation Task.” Hippocampus 23: 287–294. 10.1002/hipo.22087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Kirwan, C. B. , and Stark C. E. L.. 2007. “Overcoming Interference: An fMRI Investigation of Pattern Separation in the Medial Temporal Lobe.” Learning & Memory 14: 625–633. 10.1101/lm.663507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Koch, G. E. , Paulus J. P., and Coutanche M. N.. 2020. “Neural Patterns Are More Similar Across Individuals During Successful Memory Encoding Than During Failed Memory Encoding.” Cerebral Cortex 30: 3872–3883. 10.1093/cercor/bhaa003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Koutstaal, W. , and Schacter D. L.. 1997. “Gist‐Based False Recognition of Pictures in Older and Younger Adults.” Journal of Memory and Language 37: 555–583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Kuhl, B. A. , Rissman J., and Wagner A. D.. 2012. “Multi‐Voxel Patterns of Visual Category Representation During Episodic Encoding Are Predictive of Subsequent Memory.” Neuropsychologia 50: 458–469. 10.1016/j.neuropsychologia.2011.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lambert, F. , Lavenex P., and Banta Lavenex P.. 2017. “The “When” and the “Where” of Single‐Trial Allocentric Spatial Memory Performance in Young Children: Insights Into the Development of Episodic Memory.” Developmental Psychobiology 59: 185–196. 10.1002/dev.21479. [DOI] [PubMed] [Google Scholar]
  48. Lambert, F. R. , Lavenex P., and Lavenex P. B.. 2015. “Improvement of Allocentric Spatial Memory Resolution in Children From 2 to 4 Years of Age.” International Journal of Behavioral Development 39: 318–331. 10.1177/0165025415584808. [DOI] [Google Scholar]
  49. Lemaire, P. , and Lecacheur M.. 2011. “Age‐Related Changes in Children's Executive Functions and Strategy Selection: A Study in Computational Estimation.” Cognitive Development 26: 282–294. 10.1016/j.cogdev.2011.01.002. [DOI] [Google Scholar]
  50. Luna, B. , Padmanabhan A., and O'Hearn K.. 2010. “What Has fMRI Told Us About the Development of Cognitive Control Through Adolescence?” Brain and Cognition 72: 101–113. 10.1016/J.BANDC.2009.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. McClelland, J. L. , McNaughton B. L., and O'Reilly R. C.. 1995. “Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights From the Successes and Failures of Connectionist Models of Learning and Memory.” Psychological Review 102: 419–457. 10.1037/0033-295X.102.3.419. [DOI] [PubMed] [Google Scholar]
  52. McClelland, J. L. , and Rumelhart D. E.. 1985. “Distributed Memory and the Representation of General and Specific Information.” Journal of Experimental Psychology: General 114: 159–188. 10.1037/0096-3445.114.2.159. [DOI] [PubMed] [Google Scholar]
  53. McCormick, C. , St‐Laurent M., Ty A., Valiante T. A., and McAndrews M. P.. 2015. “Functional and Effective Hippocampal‐Neocortical Connectivity During Construction and Elaboration of Autobiographical Memory Retrieval.” Cerebral Cortex 25: 1297–1305. 10.1093/cercor/bht324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Menon, V. , Boyett‐Anderson J. M., and Reiss A. L.. 2005. “Maturation of Medial Temporal Lobe Response and Connectivity During Memory Encoding.” Cognitive Brain Research 25: 379–385. 10.1016/J.COGBRAINRES.2005.07.007. [DOI] [PubMed] [Google Scholar]
  55. Miller‐Goldwater, H. E. , Cronin‐Golomb L. M., Porter B. M., and Bauer P. J.. 2021. “Developmental Differences in Reactivation Underlying Self‐Derivation of New Knowledge Through Memory Integration.” Cognitive Psychology 129: 101413. 10.1016/j.cogpsych.2021.101413. [DOI] [PubMed] [Google Scholar]
  56. Moraczewski, D. , Chen G., and Redcay E.. 2018. “Inter‐Subject Synchrony as an Index of Functional Specialization in Early Childhood.” Scientific Reports 8: 2252. 10.1038/s41598-018-20600-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Mumford, J. A. , Turner B. O., Ashby F. G., and Poldrack R. A.. 2012. “Deconvolving BOLD Activation in Event‐Related Designs for Multivoxel Pattern Classification Analyses.” NeuroImage 59: 2636–2643. 10.1016/j.neuroimage.2011.08.076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Murty, V. P. , Tompary A., Adcock R. A., and Davachi L.. 2017. “Selectivity in Postencoding Connectivity With High‐Level Visual Cortex Is Associated With Reward‐Motivated Memory.” Journal of Neuroscience 37: 537–545. 10.1523/JNEUROSCI.4032-15.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Nash, M. I. , Hodges C. B., Muncy N. M., and Kirwan C. B.. 2021. “Pattern Separation Beyond the Hippocampus: A High‐Resolution Whole‐Brain Investigation of Mnemonic Discrimination in Healthy Adults.” Hippocampus 31: 408–421. 10.1002/HIPO.23299. [DOI] [PubMed] [Google Scholar]
  60. Ngo, C. T. , Lin Y., Newcombe N. S., and Olson I. R.. 2019. “Building Up and Wearing Down Episodic Memory: Mnemonic Discrimination and Relational Binding.” Journal of Experimental Psychology: General 148: 1463–1479. 10.1037/XGE0000583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Ngo, C. T. , Newcombe N. S., and Olson I. R.. 2018. “The Ontogeny of Relational Memory and Pattern Separation.” Developmental Science 21: e12556. 10.1111/desc.12556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Niebaum, J. C. , Chevalier N., Guild R. M., and Munakata Y.. 2021. “Developing Adaptive Control: Age‐Related Differences in Task Choices and Awareness of Proactive and Reactive Control Demands.” Cognitive, Affective, & Behavioral Neuroscience 21: 561–572. 10.3758/S13415-020-00832-2/TABLES/2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Paz‐Alonso, P. M. , Gallego P., and Ghetti S.. 2013. “Age Differences in Hippocampus‐Cortex Connectivity During True and False Memory Retrieval.” Journal of the International Neuropsychological Society 19: 1031–1041. 10.1017/S1355617713001069. [DOI] [PubMed] [Google Scholar]
  64. Paz‐Alonso, P. M. , Ghetti S., Matlen B. J., Anderson M. C., and Bunge S. A.. 2009. “Memory Suppression Is an Active Process That Improves Over Childhood.” Frontiers in Human Neuroscience 3: 24. 10.3389/neuro.09.024.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Peng, M. , Lovos A., Bottrill K., et al. 2023. “Extended Trajectory of Spatial Memory Errors in Typical and Atypical Development: The Role of Binding and Precision.” Hippocampus 33: 1171–1188. 10.1002/hipo.23576. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Petroni, A. , Cohen S. S., Ai L., et al. 2018. “The Variability of Neural Responses to Naturalistic Videos Change With Age and Sex.” eNeuro 5: 1‐13. 10.1523/ENEURO.0244-17.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Plachti, A. , Latzman R. D., Balajoo S. M., et al. 2023. “Hippocampal Anterior‐Posterior Shift in Childhood and Adolescence.” Progress in Neurobiology 225: 102447. 10.1016/j.pneurobio.2023.102447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Poirier, M. , Nairne J. S., Morin C., Zimmermann F. G. S., Koutmeridou K., and Fowler J.. 2012. “Memory as Discrimination: A Challenge to the Encoding–Retrieval Match Principle.” Journal of Experimental Psychology: Learning, Memory, and Cognition 38: 16–29. 10.1037/a0024956. [DOI] [PubMed] [Google Scholar]
  69. R Core Team . 2021. R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing . https://www.R‐project.org/. [Google Scholar]
  70. Ranganath, C. , Heller A., Cohen M. X., Brozinsky C. J., and Rissman J.. 2005. “Functional Connectivity With the Hippocampus During Successful Memory Formation.” Hippocampus 15: 997–1005. 10.1002/hipo.20141. [DOI] [PubMed] [Google Scholar]
  71. Ren, J. , Wharton‐Shukster E., Bauer A., Duncan K., and Finn A. S.. 2021. “Events Structure Information Accessibility Less in Children Than Adults.” Cognition 217: 104878. 10.1016/j.cognition.2021.104878. [DOI] [PubMed] [Google Scholar]
  72. Revelle, W. 2024. “Psych: Procedures for Psychological, Psychometric, and Personality Research (Version 2.4.3) [Computer Software].” https://cran.r‐project.org/web/packages/psych/index.html.
  73. Richter, F. R. , Chanales A. J. H., and Kuhl B. A.. 2016. “Predicting the Integration of Overlapping Memories by Decoding Mnemonic Processing States During Learning.” NeuroImage 124: 323–335. 10.1016/j.neuroimage.2015.08.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Rider, A. T. , Coutrot A., Pellicano E., Dakin S. C., and Mareschal I.. 2018. “Semantic Content Outweighs Low‐Level Saliency in Determining Children's and Adults' Fixation of Movies.” Journal of Experimental Child Psychology 166: 293–309. 10.1016/J.JECP.2017.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Rollins, L. , and Cloude E. B.. 2018. “Development of Mnemonic Discrimination During Childhood.” Learning and Memory 25: 294–297. 10.1101/lm.047142.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Rollins, L. , and Riggins T.. 2018. “Age‐Related Differences in Subjective Recollection: ERP Studies of Encoding and Retrieval.” Developmental Science 21: 1–11. 10.1111/desc.12583. [DOI] [PubMed] [Google Scholar]
  77. Rosen, M. L. , Sheridan M. A., Sambrook K. A., Peverill M. R., Meltzoff A. N., and McLaughlin K. A.. 2018. “The Role of Visual Association Cortex in Associative Memory Formation Across Development.” Journal of Cognitive Neuroscience 30: 365–380. 10.1162/jocn_a_01202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Sastre, M. , Wendelken C., Lee J. K., Bunge S. A., and Ghetti S.. 2016. “Age‐ and Performance‐Related Differences in Hippocampal Contributions to Episodic Retrieval.” Developmental Cognitive Neuroscience 19: 42–50. 10.1016/j.dcn.2016.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Schott, B. H. , Wüstenberg T., Wimber M., et al. 2013. “The Relationship Between Level of Processing and Hippocampal‐Cortical Functional Connectivity During Episodic Memory Formation in Humans.” Human Brain Mapping 34: 407–424. 10.1002/hbm.21435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Sheng, J. , Wang S., Zhang L., et al. 2023. “Intersubject Similarity in Neural Representations Underlies Shared Episodic Memory Content.” Proceedings of the National Academy of Sciences of the United States of America 120: e2308951120. 10.1073/pnas.2308951120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Shing, Y. L. , Werkle‐Bergner M., Brehmer Y., Müller V., Li S.‐C. C., and Lindenberger U.. 2010. “Episodic Memory Across the Lifespan: The Contributions of Associative and Strategic Components.” Neuroscience and Biobehavioral Reviews 34: 1080–1091. 10.1016/j.neubiorev.2009.11.002. [DOI] [PubMed] [Google Scholar]
  82. Shing, Y. L. , Werkle‐Bergner M., Li S.‐C. C., and Lindenberger U.. 2008. “Associative and Strategic Components of Episodic Memory: A Life‐Span Dissociation.” Journal of Experimental Psychology: General 137: 495–513. 10.1037/0096-3445.137.3.495. [DOI] [PubMed] [Google Scholar]
  83. Siegler, R. S. 1996. Emerging Minds: The Process of Change in Children's Thinking (pp. viii, 278). Oxford University Press. [Google Scholar]
  84. Song, H. , Finn E. S., and Rosenberg M. D.. 2021. “Neural Signatures of Attentional Engagement During Narratives and Its Consequences for Event Memory.” Proceedings of the National Academy of Sciences of the United States of America 118: e2021905118. 10.1073/pnas.2021905118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Sowell, E. R. , Peterson B. S., Thompson P. M., Welcome S. E., Henkenius A. L., and Toga A. W.. 2003. “Mapping Cortical Change Across the Human Life Span.” Nature Neuroscience 6: 309–315. 10.1038/nn1008. [DOI] [PubMed] [Google Scholar]
  86. Staresina, B. P. , Gray J. C., and Davachi L.. 2009. “Event Congruency Enhances Episodic Memory Encoding Through Semantic Elaboration and Relational Binding.” Cerebral Cortex 19: 1198–1207. 10.1093/cercor/bhn165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Stark, S. M. , Yassa M. A., and Stark C. E. L.. 2010. “Individual Differences in Spatial Pattern Separation Performance Associated With Healthy Aging in Humans.” Learning & Memory 17: 284–288. 10.1101/lm.1768110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Stevenson, R. F. , Reagh Z. M., Chun A. P., Murray E. A., and Yassa M. A.. 2020. “Pattern Separation and Source Memory Engage Distinct Hippocampal and Neocortical Regions During Retrieval.” Journal of Neuroscience 40: 843–851. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Tompary, A. , Duncan K., and Davachi L.. 2016. “High‐Resolution Investigation of Memory‐Specific Reinstatement in the Hippocampus and Perirhinal Cortex.” Hippocampus 26: 995–1007. 10.1002/hipo.22582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Vijayarajah, S. , and Schlichting M. L.. 2022. “Individual‐Specific Versus Shared Cognitive States Differently Support Complex Semantic and Perceptual Judgments.” Proceedings of the Annual Meeting of the Cognitive Science Society 44: 44. [Google Scholar]
  91. Vijayarajah, S. , and Schlichting M. L.. 2023. “Anterior Hippocampal Engagement During Memory Formation Predicts Subsequent False Recognition of Similar Experiences.” Journal of Cognitive Neuroscience 35, no. 11: 25. 10.1162/JOCN_A_02052. [DOI] [PubMed] [Google Scholar]
  92. Wahlheim, C. N. , Christensen A. P., Reagh Z. M., and Cassidy B. S.. 2022. “Intrinsic Functional Connectivity in the Default Mode Network Predicts Mnemonic Discrimination: A Connectome‐Based Modeling Approach.” Hippocampus 32: 21–37. 10.1002/hipo.23393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Wing, E. A. , Geib B. R., Wang W.‐C., Monge Z., Davis S. W., and Cabeza R.. 2020. “Cortical Overlap and Cortical‐Hippocampal Interactions Predict Subsequent True and False Memory.” Journal of Neuroscience 40: 1920–1930. 10.1523/JNEUROSCI.1766-19.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Wing, E. A. , Marsh E. J., and Cabeza R.. 2013. “Neural Correlates of Retrieval‐Based Memory Enhancement: An fMRI Study of the Testing Effect.” Neuropsychologia 51: 2360–2370. 10.1016/j.neuropsychologia.2013.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Yassa, M. A. , and Stark C. E. L. L.. 2011. “Pattern Separation in the Hippocampus.” Trends in Neurosciences 34: 515–525. 10.1016/j.tins.2011.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1.

CDEV-96-1852-s001.pdf (604.1KB, pdf)

Data Availability Statement

Analysis scripts and anonymized behavioral data are available on OSF (https://osf.io/9qj8e/).

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