Working memory is the ability to hold and manipulate information in mind for short periods (e.g., remembering a phone number while dialing). Heavy reliance on technology can affect working memory in several ways:

• Reduced practice with internal storage: External tools (search engines, calculators, reminders) offload memory demands, so children have fewer opportunities to rehearse and retain information mentally, which can weaken working memory capacity over time.
• Increased cognitive load and distraction: Multitasking with devices (switching between apps, notifications) fragments attention and disrupts the rehearsal processes that support working memory, lowering accuracy on tasks requiring sustained mental manipulation.
• Enhanced visual-spatial skills for some tasks: Interactive digital games and apps can improve specific working-memory-related skills (especially visuospatial working memory) when designed for training, showing that effects depend on content and use.
• Developmental sensitivity: Because working memory develops through childhood, excessive externalization or distractive screen use during sensitive periods may have stronger negative effects than similar use in older individuals.

Overall, technology tends to shift which aspects of working memory are exercised: it can both erode routine rehearsal of information while, in some contexts, selectively strengthen capacity via targeted digital practice. (See Baddeley, 2003; Alloway & Alloway, 2010; Ophir, Nass & Wagner, 2009.)

When children (and adults) frequently switch among apps, respond to notifications, or attempt simultaneous activities on devices, several interacting cognitive mechanisms are taxed:

• Limited-capacity working memory: Working memory can hold and manipulate only a small amount of information at once. Rapid task-switching forces the mind to drop or truncate intermediate representations, so information needed for ongoing mental manipulation is lost or degraded (Baddeley 2003).

• Task-switching costs: Each switch carries a time and accuracy penalty as the brain reconfigures attention and goals. Those costs accumulate across many brief interruptions, reducing effective processing time and increasing errors (Monsell 2003).

• Disrupted rehearsal and consolidation: Sustained rehearsal (mentally repeating, organizing, or elaborating information) supports encoding into longer-term memory. Frequent interruptions prevent continuous rehearsal and interrupt the consolidation processes that follow focused effort, lowering learning and recall.

• Increased intrinsic and extraneous load: Multitasking raises intrinsic cognitive load by splitting processing resources between tasks, and extraneous load by forcing management of notifications, layouts, and app-switch mechanics. Together they leave fewer resources for deep comprehension or complex problem solving (Sweller 1988).

• Attentional fragmentation and shallow processing: Repeated shallow engagements favor surface-level processing—scanning and skimming—over the sustained, effortful attention required for critical thinking, inference, and transfer of learning.

Net effect: Accuracy and performance on tasks requiring sustained mental manipulation, complex reasoning, or working-memory-intensive operations decline under heavy device-driven multitasking. Mitigation includes structured, uninterrupted work periods, reducing notifications, and guiding children toward single-task engagement when deep learning is the goal.

References (select): Baddeley A. (2003) Working memory; Monsell S. (2003) Task switching; Sweller J. (1988) Cognitive load theory.

Sustained rehearsal—mentally repeating, organizing, or elaborating information—is a core cognitive practice that transfers items from fleeting attention into more durable memory stores. When a child maintains continuous focus on material they are encoding, several complementary processes operate: active rehearsal in working memory, deeper semantic elaboration, and the onset of consolidation (the neural stabilization and integration of memory traces).

Frequent interruptions from technology (notifications, app-switching, multitasking) break that continuity in three interrelated ways:

• They truncate rehearsal cycles. Repetition and elaboration require uninterrupted time; breaking that stream leaves representations weak and more susceptible to decay.
• They fragment attentional resources. Switching attention imposes cognitive switching costs that reduce the quality of encoding each time the child returns to the task.
• They impair early consolidation. Consolidation begins almost immediately after encoding and benefits from uninterrupted cognitive and neural activity; repeated disruptions interfere with the processes that stabilize memory traces.

The upshot: interrupted rehearsal produces shallower encoding and weaker consolidation, which lowers later recall and learning transfer. In short, technology-driven interruptions shift learning from sustained, deep processing toward fragmented, fragile traces.

Relevant empirical frameworks: models of working memory and consolidation (e.g., Baddeley, 2003) and research on multitasking and attention (e.g., Ophir et al., 2009) support this account.

Working memory has a narrow capacity: it temporarily holds and manipulates only a few items (Baddeley 2003). When a child rapidly switches between tasks or apps, attention must be repeatedly reoriented and intermediate mental representations—those partial steps, rehearsed items, or unfolding plans—are often dropped or truncated. Each switch forces the cognitive system to rebuild context and restart rehearsal, which wastes processing resources and degrades the fidelity of information needed for ongoing mental manipulation. Over time, frequent interruption reduces opportunities to sustain and practice the sequential mental operations that strengthen working-memory-dependent skills, making complex problem solving, comprehension, and multi-step tasks more error-prone. References: Baddeley (2003); see also research on multitasking and working memory (Ophir, Nass & Wagner, 2009).

When children repeatedly engage with fragmented, fast-paced digital content (short videos, feeds, notifications), their attention is trained to jump quickly between items and to favor immediate, surface-level information. This pattern has two closely related effects:

• Attentional fragmentation: Frequent context switches and external interruptions prevent the sustained focus needed to hold complex ideas in mind long enough to analyze them. Working memory and goal-directed attention become regularly disrupted, so tasks that require prolonged concentration are harder to initiate and complete.

• Shallow processing: Habitual scanning and skimming prioritize rapid recognition and retrieval of discrete facts over elaboration, integration, and inference. Deep processing—forming connections across ideas, generating explanations, and transferring knowledge to new problems—requires effortful rehearsal and reflection that shallow engagements do not cultivate.

Combined, these effects make critical thinking and higher-order learning less likely: children may know many isolated facts or cues but struggle to synthesize information, reason through arguments, or apply concepts in novel contexts. The remedy is not abstention alone but structured opportunities for extended, guided attention—dialogue, focused reading, project-based tasks, and environments with fewer interruptions—that rebuild habits of sustained, deep processing.

Key sources: Baddeley (working memory theory), Ophir et al. (2009) on multitasking and attention, and educational research on deep vs. surface learning.

Task‑switching costs refer to the measurable time and accuracy losses that occur when the brain shifts from one task or goal to another. Each switch requires cognitive resources to (a) disengage from the prior task, (b) reconfigure attention and working memory for the new task, and (c) suppress interference from the previous goal. This reconfiguration is not instantaneous: it incurs a delay and a higher likelihood of mistakes. When digital environments produce many brief interruptions (notifications, app switching, multitasking), these individual costs accumulate — shrinking the net time available for focused processing, fragmenting working memory rehearsal, and increasing overall error rates. In short, frequent switching turns small, recoverable delays into substantial loss of cognitive efficiency and accuracy. (See Monsell 2003; Ophir, Nass & Wagner 2009.)

Explanation: When children multitask with digital devices they must allocate limited cognitive resources across simultaneous activities (e.g., listening to a teacher, switching to a game, attending to notifications). This division raises intrinsic cognitive load because the child must process multiple task-relevant elements at once—each task’s mental requirements compete for working memory and attentional capacity. At the same time, the digital environment adds extraneous load: managing app interfaces, frequent notifications, navigation and switching costs, and fragmented layouts imposes additional, non‑essential processing demands that do not contribute to learning.

Sweller’s cognitive load framework (1988) distinguishes intrinsic load (determined by task complexity) from extraneous load (determined by how information is presented). Multitasking effectively increases intrinsic load by turning one learning episode into several concurrent episodes, and increases extraneous load by introducing interface and interruption overhead. The combined effect reduces available resources for germane processing—the deep comprehension and problem‑solving operations needed to form durable understanding—leading to shallower learning and poorer performance on complex tasks.

References:

• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science.
• Ophir, Nass & Wagner (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences.

Children’s cognitive systems—especially working memory and attention—are still maturing across early childhood and into adolescence. During these sensitive periods, brain circuits and behavioral routines that support sustained attention, effortful control, and information‑holding are more plastic and therefore more easily shaped by experience (Bunge & Wright 2007; Johnson 2011). Frequent externalization of cognitive tasks to devices (e.g., relying on apps to remember, multitasking across screens) or repeated exposure to highly stimulating, attention‑fragmenting media can reduce opportunities to practice and strengthen these internal capacities. By contrast, older individuals with more consolidated executive function are better able to compensate for such external supports or distractions.

In short: when working memory and related executive processes are still developing, excessive or distractive technology use can have disproportionately large negative effects compared with similar use later in life. That is why content, context (caregiver scaffolding), and limits matter most for younger children.

Selected supporting references:

• Bunge, S. A., & Wright, S. B. (2007). Neural plasticity in human development: Evidence from cognitive neuroscience. Developmental Neuropsychology.
• Johnson, M. H. (2011). Interactive specialization: a domain-general framework for human functional brain development? Developmental Cognitive Neuroscience.
• Lillard, A. S., & Peterson, J. (2011). The immediate impact of different types of television on young children’s executive function. Pediatrics.

Bunge and Wright (2007) reviews evidence from cognitive neuroscience showing how neural plasticity supports cognitive development across childhood and adolescence. It was selected because:

• Relevance to technology effects: The paper clarifies that brain systems for functions like working memory, attention, and executive control develop over extended periods and remain malleable. This developmental plasticity explains why environmental inputs — including patterns of technology use — can shape cognitive trajectories (for better or worse).
• Mechanistic grounding: Unlike purely behavioral studies, the review links changes in cognitive performance to underlying neural processes (e.g., maturation of prefrontal and frontoparietal networks), providing a biological basis for interpreting how repeated digital practices or distractions might reinforce particular skills or habits.
• Sensitive periods and resilience: Bunge & Wright emphasize windows of heightened sensitivity and the potential for later remediation, which supports the nuanced conclusion that technology’s impact depends on timing, dose, and context rather than being uniformly positive or negative.
• Implications for intervention: By documenting plasticity, the review supports targeted, developmentally informed strategies (guided use, training programs, and caregiver scaffolding) to harness technology for cognitive gains while mitigating risks.

In short, Bunge & Wright (2007) offers theoretical and empirical grounding for understanding how technology-related experiences can alter developing cognitive systems, making it a useful scientific foundation for the points about working memory, attention, and executive function in your summary.

Reference: Bunge, S. A., & Wright, S. B. (2007). Neural plasticity in human development: Evidence from cognitive neuroscience. Developmental Neuropsychology.

Mark H. Johnson’s 2011 paper outlines the “interactive specialization” framework as a domain-general account of how functional brain organization emerges during development. Rather than assuming brain regions are born prewired for specific cognitive functions, the model proposes that specialization arises through progressive interactions among brain areas and between brain and environment. Key points:

• Dynamic networks: Cognitive functions emerge from changing patterns of connectivity; regions become specialized through competitive and cooperative interactions within distributed networks.

• Experience‑dependent tuning: Neural circuits are shaped by input and behavior. Repeated engagement with specific tasks strengthens relevant connections and refines regional contributions.

• Developmental trajectory: Early brain responses are often broadly tuned and overlapping; over time, responses become more focal and functionally distinct as networks reorganize.

• Domain‑general mechanism: The same interactive, activity‑dependent processes operate across domains (perception, language, social cognition), explaining both typical specialization and variability across individuals and contexts.

• Explains plasticity and constraint: The framework accounts for flexible reorganization after atypical experience (e.g., sensory loss) while acknowledging that maturational changes and initial biases guide probable outcomes.

In sum, Johnson argues that functional specialization is not simply genetically preordained but is the product of ongoing, reciprocal interactions among neural regions and environmental input, producing the mature, domain‑specific brain architecture seen in adults. For further detail, see Developmental Cognitive Neuroscience 2011;1(1):7–21.

Johnson (2011) presents the interactive specialization (IS) framework, a useful domain-general account of how functional brain organization emerges through development. It was selected because it links neural processes to the behavioral outcomes discussed above (attention, working memory, language, social cognition) in a way that helps explain how technology use might shape development.

Key points of the paper relevant to the technology context

• Development as interactional: IS emphasizes that brain regions become functionally specialized through dynamic interactions with other regions and with experience. Thus, changing patterns of experience (e.g., heavy, repetitive engagement with screens) can alter the trajectory of specialization.
• Activity-dependent tuning: Neural circuits are sculpted by the tasks children perform. If technology displaces activities that normally drive particular circuits (face-to-face play, sustained reading, physical play), those circuits may develop differently, which helps explain observed changes in attention, social-emotional processing, and working memory.
• Domain-general mechanisms: IS is not tied to one cognitive domain; it explains how multiple functions (language, executive function, social cognition) can be affected by shifts in environmental input—consistent with the mixed effects of technology (some gains in visuospatial skills, potential losses in sustained attention or language).
• Sensitive periods and plasticity: The framework highlights developmental windows when experience has stronger impact. This supports concerns that early, unstructured tech exposure could produce larger effects on emerging systems (e.g., working memory, language) than similar exposure later.
• Individual differences and context: IS accounts for why similar experiences can lead to different outcomes depending on prior organization and co-occurring experiences (caregiver interaction, educational use), aligning with findings that content quality and mediation moderate technology’s effects.

Why this matters for policy and practice Johnson’s framework grounds the empirical findings reviewed in your context: it explains mechanistically how the amount, timing, and type of technology exposure can produce variable developmental outcomes. That makes IS a helpful theoretical bridge between neuroscience and developmental recommendations (moderation, guided use, preserving diverse, face-to-face experiences).

Reference Johnson MH. (2011). Interactive specialization: a domain-general framework for human functional brain development? Developmental Cognitive Neuroscience, 1(1), 7–21.

Bunge and Wright (2007) reviews how neural plasticity supports cognitive development across childhood and adolescence, linking brain changes to emerging abilities such as working memory, attention, and executive function. It was selected because:

• The paper synthesizes cognitive‑neuroscience evidence showing sensitive periods when experience shapes brain circuits—directly relevant to concerns about how early and repeated technology exposure can alter developmental trajectories.
• It connects neural mechanisms (e.g., synaptic pruning, myelination, prefrontal maturation) to behavioral outcomes like self‑regulation and working memory, providing a biological basis for interpreting both risks (e.g., distraction reducing rehearsal) and opportunities (e.g., targeted training improving visuospatial skills).
• The review emphasizes experience‑dependent plasticity, supporting the conditional claim in your summary: technology’s effects depend on content, timing, and context (co‑use, guidance), not on mere presence of devices.
• Methodologically, it bridges neuroimaging and behavioral studies, offering a rigorous foundation for drawing developmentally sensitive recommendations (age‑appropriate limits, guided use).

Reference note: Bunge & Wright is a widely cited, accessible review for linking brain development literature to practical developmental concerns about technology and learning.

Lillard & Peterson (2011) is cited because it provides clear, experimentally grounded evidence about how specific types of screen content can have immediate, measurable effects on young children’s executive function (EF). Key reasons for its inclusion:

• Experimental design: The study randomly assigned preschool children to watch either fast‑paced cartoons, educational TV, or to play a drawing game, then tested EF (working memory, inhibitory control, cognitive flexibility) immediately afterward. This causal approach strengthens claims about short‑term effects of media content on EF.

• Content specificity: Findings showed that fast‑paced, fantastical cartoons temporarily impaired children’s EF relative to educational TV and active play. This supports the point that not all screen time is equal—content and pacing matter.

• Developmental relevance: The sample focused on preschoolers, a period when EF is rapidly developing and particularly sensitive to environmental influences, aligning with concerns about developmental timing.

• Practical implication: The results justify recommendations for age‑appropriate, slower‑paced, and interactive media, and for prioritizing caregiver‑mediated or active play over passive viewing for supporting EF.

Reference: Lillard, A. S., & Peterson, J. (2011). The immediate impact of different types of television on young children’s executive function. Pediatrics, 128(4), 644–649.

Lillard and Peterson (2011) examined how different kinds of television exposure — specifically fast‑paced cartoons, a slower educational program, and drawing/quiet play — affected 4‑ to 6‑year‑olds’ immediate executive‑function performance (measures like working memory, cognitive flexibility, and inhibitory control). They found that children who watched a fast‑paced cartoon performed worse on subsequent executive‑function tasks than children who engaged in drawing or watched a slower, educational show.

Why this study was selected:

• Direct relevance: It provides causal, experimental evidence linking a common form of screen media to short‑term changes in executive function in young children.
• Nuance about content and pacing: The results show that not all screen time is equal — program characteristics (pace, content) matter for cognitive effects.
• Developmental focus: It targets preschool ages, a sensitive period for executive‑function development, supporting the broader point that timing and context of technology use influence developmental outcomes.
• Practical implications: The study strengthens recommendations for age‑appropriate content, limits on fast‑paced media for young children, and favoring interactive or slower formats and caregiver‑mediated activities.

Reference: Lillard, A. S., & Peterson, J. (2011). The immediate impact of different types of television on young children’s executive function. Pediatrics.

Certain interactive digital games and apps — especially those designed as cognitive training or that require manipulation of spatial information (e.g., puzzle games, block-builders, navigation tasks) — can strengthen visuospatial working memory and related skills. These activities repeatedly engage the mental processes that maintain and manipulate visual and spatial representations (holding locations, rotating shapes, tracking moving objects). With focused, repeated practice the relevant neural circuits show plasticity, producing measurable gains on trained tasks and, in some studies, on closely related visuospatial abilities.

Crucially, these benefits are neither automatic nor universal. Gains are most likely when:

• the software targets specific visuospatial processes (rather than being merely entertaining),
• practice is frequent and sufficiently challenging but adaptive,
• activities are age-appropriate and time-limited to avoid displacement of other important experiences (social interaction, physical play, sleep).

Meta-analyses and experimental studies indicate transfer is often narrow (improvements tend to be strongest for tasks similar to the training), so developers, educators, and caregivers should view such apps as one useful tool among many for supporting visuospatial and working-memory development.

Key references: studies on cognitive training and videogame effects (see e.g., Owen et al. 2010; Klingberg 2010; Sala & Gobet 2019 for discussions of training specificity and transfer).

Choosing age‑appropriate and time‑limited activities helps ensure technology supports rather than replaces key developmental experiences. Younger children need rich, face‑to‑face interaction to build language, attachment, and emotion‑regulation; unlimited screen time can displace caregiver talk and joint play that form the foundation for learning. For all ages, excessive device use reduces time for physical play (important for motor skills and health), peer play (important for social negotiation and empathy), and sleep (crucial for memory consolidation and self‑regulation). Time limits and curated, age‑matched content preserve benefits of digital tools—learning, practice of specific skills—while protecting opportunities for real‑world interactions and restorative sleep that underlie healthy cognitive, social, and physical development.

References: Radesky & Christakis (2016); Zimmerman et al. (2007); Cain & Gradisar (2010).

This selection captures core principles for improving working memory through technology-informed activities. Frequent practice provides repeated opportunities to engage the memory systems that need strengthening. Sufficient challenge ensures tasks push the learner just beyond current ability—promoting growth rather than rote performance. Adaptivity tailors difficulty in real time to the child’s performance, keeping challenges in the optimal learning zone (not too easy to be boring, not too hard to cause frustration). Together these features maximize effective rehearsal, maintain motivation, reduce wasted time on tasks that are either trivial or inaccessible, and help transfer gains to everyday cognitive demands. Empirical work on cognitive training and skill acquisition (e.g., adaptive working‑memory training studies; Baddeley, 2003; Alloway & Alloway, 2010) supports this combination as more likely to produce durable improvements than static or infrequent practice.

This software was chosen because its design intentionally engages core visuospatial processes rather than merely providing entertainment. Key reasons:

• Task structure focuses on spatial manipulation: Activities require mental rotation, spatial sequencing, and location memory (e.g., arranging objects, navigating grids), which directly exercise visuospatial working memory and mental imagery.
• Controlled cognitive demands: Levels systematically increase in complexity (more items, faster presentation, added interference), allowing targeted training of capacity and manipulation—not random, pleasurable stimuli.
• Feedback and adaptivity support learning: Immediate, informative feedback and adaptive difficulty keep tasks in the child’s zone of proximal development, promoting durable skill improvement rather than passive engagement.
• Minimal extraneous rewards: The interface emphasizes task performance over flashy animations or unrelated rewards, reducing distraction and ensuring cognitive resources are devoted to spatial processing.
• Evidence-based task types: The activities mirror well‑validated visuospatial tasks (e.g., Corsi block–type sequences, mental rotation puzzles) known to train spatial working memory in research (Alloway & Alloway 2010; Baddeley 2003).

In sum, the selection prioritizes software whose mechanics and progression are built to train visuospatial cognition, not merely to entertain.

When children rely on external tools—search engines, calculators, calendars, and reminder apps—they shift information and routine tasks out of their minds and into devices. That offloading reduces opportunities to rehearse, organize, and retrieve information internally, processes that build and maintain working memory and long‑term retention. Over time, fewer repeated acts of encoding and retrieval can mean weaker memory traces and less practiced memory strategies (e.g., chunking, rehearsal, prospective memory planning). The result is not an immediate loss of ability but a gradual reduction in the amount and variety of mental exercise that strengthens internal storage, especially when device use replaces activities (memorizing facts, doing mental arithmetic, planning without prompts) that normally train those capacities.

References: research on cognitive offloading and memory (see Sparrow, Liu & Wegner 2011 on the “Google effect”; reviews of technology’s effects on working memory and learning such as Radesky & Christakis 2016).

Selection explanation: Reliance on technology has weakened children’s deep reading skills—sustained, focused engagement with complex texts that fosters critical thinking, inference, and retention. Frequent multitasking, skimming, and scanning on digital devices trains a surface-mode of reading: children jump between links, consume short fragments, and rely on search rather than close interpretation. As a result, they often struggle with sustained attention, making it harder to follow extended arguments, notice subtle rhetorical cues, or integrate information across paragraphs and books. This shift can reduce comprehension depth and the ability to form nuanced, long-term understandings.

Relevant research:

• Maryanne Wolf, Proust and the Squid (2007) — on neurological effects of reading modes.
• Nicholas Carr, The Shallows (2010) — on how the Internet affects attention and deep thinking.

Frequent use of fast-paced, instantly rewarding digital media (games, social apps, short videos) trains children’s brains to expect constant novelty and quick feedback. This can weaken sustained attention and patience for slower, more effortful tasks such as reading, homework, or prolonged play. As a result, children may switch tasks more often, struggle to maintain focus on single activities, and show reduced ability to engage in deep concentration over time. (See: Christakis 2009 on media exposure; Radesky et al. 2020 on digital distraction.)

Frequent multitasking across devices trains children to switch rapidly between tasks and streams of stimulation instead of maintaining prolonged, goal‑directed attention. This repeated shifting reduces practice of sustained attention and makes it harder to resist distractions—weakening inhibitory control (the ability to suppress impulses) that supports delayed gratification and deliberate planning. Over time, neural systems involved in executive function (prefrontal networks) receive less opportunity to mature through focused practice, so children show shallower concentration, more impulsive responding, and poorer task persistence (see Levy et al., 2023; Diamond, 2013).

Relying heavily on technology—especially passive, screen-based activities—can reduce children’s practice of executive skills such as working memory, inhibitory control, cognitive flexibility, planning, and sustained attention. Choosing activities that actively engage these capacities (interactive games that require strategy, hands-on play, problem-solving tasks, social play that requires turn-taking and self-control) helps children exercise and strengthen executive functions. Limiting passive screen time and encouraging guided, purposeful tech use (apps that scaffold problem-solving, cooperative digital activities, or tech-assisted projects combined with adult support) preserves opportunities for self-regulation, goal-setting, and flexible thinking.

Relevant research: work by Adele Diamond on executive functions; American Academy of Pediatrics guidance on screen time and development.

Inhibitory control is the ability to suppress impulses, ignore distractions, and resist automatic or habitual responses in order to pursue a goal. It is a core component of executive function that supports tasks like waiting one’s turn, staying focused on schoolwork, and regulating emotional reactions.

Why it was selected

• Developmental importance: Strong inhibitory control in childhood predicts better academic achievement, social adjustment, and long‑term outcomes (e.g., Moffitt et al., 2011).
• Vulnerability to technology patterns: Frequent rapid switching between apps, constant notifications, and habitually rewarding digital content encourage reactive, stimulus‑driven attention rather than deliberate, controlled responding. This environment can make practicing sustained inhibition harder and may weaken the skills that support self‑regulation.
• Intervention potential: Unlike some traits, inhibitory control can be strengthened through targeted activities (structured play, games requiring turn‑taking, mindfulness, guided use of technology), especially when caregivers or educators scaffold and model self‑control.

Practical implications

• Set limits on uninterrupted screen time and reduce multitasking during homework.
• Encourage activities that require delay of gratification and sustained attention (reading, board games, sports).
• Use technology purposefully: choose apps and games that require planning, problem‑solving, or sustained engagement, and co‑use devices to scaffold self‑regulation.

References for further reading: Lillard & Peterson (2011) on pretend play and executive function; Radesky & Christakis (2016) and Christakis (2019) on screen use and attention/self‑regulation; Moffitt et al. (2011) on long‑term outcomes of childhood self‑control.