The variant in question, known as rs10191329, sits in the stretch of DNA between two genes called DYSF and ZNF638. It does not change any protein sequence, yet a large genome-wide association study of more than 12,000 people with multiple sclerosis previously linked carrying its A allele to greater cross-sectional physical disability. Subsequent work connected the same allele to brain atrophy, thinning of the retinal nerve fibre layers, and elevated levels of serum neurofilament light chain, a blood biomarker of neuroaxonal damage. Together, these findings pointed toward a mechanism centred on the central nervous system itself rather than on the immune attack that defines relapsing disease.

That pattern intrigued the study’s authors. If the A allele erodes the nervous system’s resilience, they reasoned, its influence might not stop at physical disability. Cognitive impairment is one of the most common and disabling features of multiple sclerosis, and the concept of cognitive reserve, the brain’s capacity to tolerate damage while preserving function, is well established across neurodegenerative conditions including Alzheimer’s and Parkinson’s disease. The team therefore hypothesised that rs10191329 might influence cognition not only in multiple sclerosis but in healthy people and in other neurological disorders, and turned to the UK Biobank to test the idea at population scale.

The UK Biobank recruited roughly half a million participants aged 40 to 69 between 2006 and 2010, collecting genetic data alongside questionnaires, physical assessments, imaging and biological samples. From this resource the researchers constructed five mutually exclusive cohorts of European ancestry: people with a recorded multiple sclerosis diagnosis, people with Parkinson’s disease, people with all-cause dementia, people with migraine as a negative disease control, and healthy controls. After excluding participants with missing genotype data, the final analytical sample comprised 399,031 individuals, including 373,530 controls, 2,026 people with multiple sclerosis, 2,337 with dementia, 1,466 with Parkinson’s disease and 19,672 with migraine.

The cognitive outcomes came from three tests administered at recruitment. Reaction time was measured in a computerised card-matching task, with higher scores indicating slower responses. Fluid intelligence was scored as the number of correct answers out of 13 on a reasoning test, with higher scores indicating better performance. Prospective memory, the ability to remember to carry out an intended action, was assessed as a binary outcome based on whether participants correctly recalled a task instruction on their first attempt. The researchers confirmed that the multiple sclerosis, Parkinson’s and dementia cohorts showed the expected pattern of poorer performance compared with controls and the migraine group, validating the measures.

Statistical models adjusted for age, sex, the first four genetic principal components and the Townsend deprivation index, a measure of socioeconomic status. Linear outcomes were rank-inverse normal transformed to satisfy model assumptions, and false discovery rate correction was applied across all tests simultaneously. Sensitivity analyses added educational leaving age as a covariate and repeated the models under dominant and recessive genetic frameworks. As a positive control, the team also examined the relationship between the variant and self-reported disability claims among the multiple sclerosis cohort.

The results were striking in their direction, if modest in their magnitude. Among healthy controls, each copy of the A allele was associated with slower reaction time, a beta of 0.01 standard deviations per allele; lower fluid intelligence, a beta of minus 0.02 standard deviations; and higher error rates on the prospective memory task, an odds ratio of 1.06. All three associations reached study-wide significance. In the multiple sclerosis group, carriers of two A alleles were 5.9 percent more likely to claim disability allowance than common-allele homozygotes, 52.4 percent versus 46.5 percent, although the regression estimate did not reach statistical significance. In the other disease cohorts, associations were directionally concordant but not significant, with slower reaction time in multiple sclerosis, Parkinson’s and dementia, lower fluid intelligence across all cohorts, and impaired prospective memory in dementia.

Power calculations help explain the pattern of significance. Using reaction time as an example, the study had 98 percent power to detect a per-allele effect larger than 0.15 standard deviations in the multiple sclerosis cohort of roughly 2,000 people, but only 63 percent power for a 0.1 standard deviation effect. In other words, the analysis was well equipped to detect moderate genetic effects within disease groups but underpowered for the small effects observed in the enormous control sample. The authors are careful to note that the cognitive tests were designed as pragmatic population measures, not as validated outcomes for multiple sclerosis, and that prevalent cases in the Biobank are biased toward milder disease, both of which would attenuate detectable associations.

One obvious alternative explanation is education. A previous Mendelian randomisation analysis suggested genetic overlap between the severity locus and educational attainment, and cognitive reserve is strongly shaped by education. Yet adjusting for educational leaving age, a proxy for educational attainment, did not meaningfully attenuate the associations, making it unlikely that schooling alone accounts for the signal. The variant’s effects, whatever their mechanism, appear to operate at least partly independently of educational background, consistent with a direct influence on brain health rather than a purely sociodemographic confound.

The broader implication is that a variant discovered as a modifier of multiple sclerosis severity may act through general properties of central nervous system resilience, influencing how the brain performs even in the absence of disease. The effect sizes are small, and the authors emphasise that large disease-specific cohorts are needed to determine whether the variant shapes cognition within multiple sclerosis and other disorders, and whether its influence extends to populations of non-European ancestry, which this study did not examine. Still, the convergence of evidence, spanning disability, brain atrophy, neurofilament light chain, retinal thinning and now cognition, sketches a coherent picture of a genetic locus that quietly sets the baseline health of the nervous system, and in doing so helps determine how hard neurological disease hits when it arrives.

Subject of Research: Association between the multiple sclerosis severity allele rs10191329A and cognitive function in the UK Biobank

Article Title: The Multiple Sclerosis Severity Allele rs10191329A and Cognitive Function: A UK Biobank Study

Article References: Zimianiti, I., Waters, S., Harroud, A., Stridh, P., Dobson, R., & Jacobs, B. M. (2026). The Multiple Sclerosis Severity Allele rs10191329 A and Cognitive Function: A UK Biobank Study. Annals of Clinical and Translational Neurology, 13(10), 2143-2147. https://doi.org/10.1002/acn3.70458

Image Credits: AI Generated

DOI: 10.1002/acn3.70458

Keywords: multiple sclerosis, rs10191329, UK Biobank, cognitive function, genetics, neurodegeneration, DYSF-ZNF638 locus, neurofilament light chain, cognitive reserve, fluid intelligence, reaction time, prospective memory

Episodic foresight is often described as mental time travel. It allows a person to simulate a possible future event, recognize what it will demand, and organize current behavior accordingly: packing a raincoat because a storm is forecast, or grabbing a permission slip because it must be handed in tomorrow. Psychologists regard it as the most flexible and functionally powerful form of prospective cognition, and a critical prerequisite for independent living. Yet almost everything previously known about episodic foresight in autism came from studies asking participants to describe imagined future events, rating how vivid, detailed or specific those descriptions were. A 2023 meta-analysis of 31 such studies found moderate-to-large autism-related differences across measures of detail, experiential quality and specificity, and a recent longitudinal study showed that while future-scenario imagination developed over three years in non-autistic adolescents, it remained stable in autistic adolescents.

What those paradigms could not answer was whether the reported differences in imaginative richness translate into practical consequences — whether autistic children actually act differently when the future matters. To find out, the team turned to the child version of Virtual Week-Foresight, a computerized board game that is currently the only standardized behavioral measure meeting the stringent experimental criteria laid out by Suddendorf and Corballis for demonstrating genuine episodic foresight. Those criteria are designed to rule out four alternative explanations for actions that happen to benefit the future, ensuring that success truly reflects acting with the future in mind rather than habit, chance or instruction-following.

In the game, children roll a die and move a token around a board representing a virtual day. As they pass green ‘S’ squares, they draw Situation Cards presenting everyday scenarios, such as having breakfast, and choose among options. Embedded among twenty such cards — many of them deliberate distracters — are seven episodic foresight problems and seven contexts in which those problems can be resolved. Along the way, Daily Activity Cards offer chances to pick up one item from a list of five, four of which are useless distracters. The design simulates how foresight problems arise in real life: buried in the flow of ongoing activity, requiring the child to spot a future need, self-generate a solution, acquire the right item, and then remember and choose to deploy it at the right future moment. The task yields two scores: item acquisition, reflecting the ability to take appropriate preparatory steps, and item use, reflecting the ability to follow through when the problem reappears.

Forty autistic children aged 8 to 12, all with IQ scores above 80 and no intellectual impairment, completed the task alongside 55 age- and IQ-matched non-autistic controls. All children were tested at home in distraction-free rooms, and the autistic children’s diagnoses were verified through clinician reports and the Social Communication Questionnaire, on which the autistic group scored substantially higher, confirming poorer social and communication skills. Crucially, the two groups were statistically indistinguishable in age, overall cognitive ability and retrospective memory, although the autistic group did perform more poorly on standardized tests of executive function and were rated by parents as showing lower functional self-direction in daily life.

The results were unexpected and, in their way, more revealing than a simple deficit story. On a mixed-model analysis of variance, the groups diverged not in what they acquired but in what they did with it. Autistic children were just as successful as their non-autistic peers at identifying future problems and acquiring the items needed to resolve them — the preparatory, construction phase of foresight appeared fully intact. But when the moment of resolution arrived, they were significantly less likely to actually use those items, a difference that was statistically reliable and could not be explained by disengagement or misunderstanding of the task, since the same children had demonstrably grasped the problems and their solutions moments earlier. Both groups found item use harder than item acquisition, but the gap was wider for the autistic children.

Could broader cognitive weaknesses explain the follow-through failure? The researchers tested this directly. Children completed the Trail Making Test and the Color-Word Interference Test from the Delis-Kaplan Executive Functioning System, indexing cognitive flexibility and inhibition respectively, along with the List Memory Delayed subscale of the NEPSY-II to measure retrospective memory. Hierarchical regression analyses controlling for age and intelligence found that neither executive function nor retrospective memory uniquely predicted item acquisition or item use in either group. The autism-related difference in applying foresight therefore appears to be a specific challenge in its own right, not a downstream consequence of memory or executive difficulties — a finding the authors describe as raising new questions about what cognitive processes actually underpin the effect.

The study also hints at why previous research painted such a uniformly negative picture. Under the constructive episodic simulation hypothesis, imagining the future involves two phases: a basic construction phase that assembles a hypothetical event from retrieved information, and an elaboration phase that fleshes it out with rich episodic and contextual detail. Prior studies, which asked children to narrate detailed future scenarios, taxed both phases. Virtual Week-Foresight, by contrast, presents relatively obvious problems and solutions, so acquiring an item may demand only enough simulation to recognize a future need. On this reading, autistic children can construct adequate future representations to guide preparation, but falter at the point of elaboration — or at the distinct, self-initiated act of implementing an intention at the right time. Notably, earlier work on prospective memory in autism involved intentions set by experimenters; this study shows the difficulty persists even when the intention is self-generated.

The functional stakes are considerable. In the non-autistic control group, both item acquisition and item use correlated strongly with parents’ ratings of self-direction on the Adaptive Behavior Assessment System, the scale that asks how often a child routinely arrives on time or manages independent routines. Longitudinal research has documented a developmental gap of more than eight years in daily living skills between autistic and non-autistic adolescents by the end of adolescence, persisting into young adulthood, and this gap exists even among those with preserved intelligence. The new findings suggest a possible mechanism: knowing what the future needs is not the bottleneck; converting that knowledge into timely action is. Intriguingly, a parallel study in multiple sclerosis found the same acquisition-intact, use-impaired pattern in depressed patients who also showed poorer daily living skills, hinting that the implementation stage of foresight may be a vulnerable link across conditions.

The authors caution that only one domain of functional capacity was assessed, that parent report alone may miss school-based demands, and that the study was underpowered to test sex differences, though exploratory analyses found no male-female differences in foresight performance among autistic children. Still, the implications are concrete. Support strategies for autistic children, the researchers suggest, should focus not on teaching children to anticipate problems — which they demonstrably can do — but on scaffolding the execution of self-generated intentions: prompts, routines and environmental cues that bridge the gap between recognizing a future need and acting on it. In a field long dominated by narratives of imaginative deficit, the study reframes the story in a more precise and arguably more hopeful way: the future is visible to these children; what is needed is help seizing it at the right moment.

Subject of Research: Episodic foresight and future-directed behavior in autistic children without intellectual impairment

Article Title: The Functional Application of Episodic Foresight in Autistic Children

Article References: Chua, S. J. S., Terrett, G., Coundouris, S. P., Rendell, P. G., Suddendorf, T., & Henry, J. D. (2026). The Functional Application of Episodic Foresight in Autistic Children. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07561-4

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07561-4

Keywords: autism spectrum disorder, episodic foresight, prospective cognition, future thinking, daily living skills, executive function, retrospective memory, Virtual Week-Foresight, developmental psychology, prospective memory, children, functional independence

The technique in question is called episodic future thinking, a term first introduced by cognitive scientists Cristina Atance and Daniela O’Neill to describe the capacity to pre-experience events that have not yet happened. When a person mentally simulates a specific future episode, such as picturing themselves handing a document to a colleague in a particular office at a particular time, the brain recruits much of the same neural machinery used to recall genuine past experiences. Previous work, including studies by Mark Altgassen and colleagues and by Anja Kretschmer-Trendowicz and her collaborators, had already shown that episodic future thinking instructions improve prospective memory in older adults, adolescents and children. What remained contested was the mechanism: did imagining the future strengthen the memory for what had to be done, or did it sharpen the ability to notice the cue that triggers the action?

Prospective memory is conventionally decomposed into two separable components. The prospective component concerns noticing that the moment for action has arrived, detecting the target cue among a stream of ongoing activity. The retrospective component concerns retrieving what the intended action actually was once the cue has been noticed. These two processes can fail independently: a person may register that something was supposed to happen without recalling what, or recall the intention perfectly but never encounter or recognize the triggering cue. Because behavioral accuracy alone conflates the two, the Chinese research team employed a multinomial processing tree model, a mathematical framework originally developed by William Batchelder and Donald Riefer and adapted for event-based prospective memory by Rebecca Smith and Ute Bayen, which uses patterns of correct responses, errors and guesses to estimate the separate probabilities of the prospective and retrospective processes.

The researchers conducted two behavioral experiments with university students using a dual-task paradigm, in which participants performed an ongoing task while also holding an intention to respond to specific target cues. Experiment 1 used a between-subjects design with 112 participants, crossing two groups, an episodic future thinking group and a control group, with two levels of cue focality. Focal cues are ones that overlap with the processing demands of the ongoing task, so that the target is processed in the natural course of the ongoing activity, whereas non-focal cues require a shift of attention away from the ongoing task to be noticed. This focality distinction matters because prominent theories, notably the multiprocess framework of Mark McDaniel and Gilles Einstein, predict that non-focal intentions demand resource-demanding monitoring while focal intentions can benefit from relatively spontaneous retrieval.

The results of Experiment 1 were striking in their simplicity. Participants who had engaged in episodic future thinking before encoding the intention showed significantly higher prospective memory accuracy than controls, and crucially, this benefit emerged for both focal and non-focal cues. The advantage was not modulated by focality, suggesting that the strategy does not merely help in the easy, spontaneous-retrieval case but extends to situations where the cue is harder to catch. Equally informative was what did not change: episodic future thinking did not degrade performance on the ongoing task, and it did not alter the speed of prospective memory responses. In other words, the boost came free of charge, without the additional attentional cost that some monitoring-based strategies impose on whatever else a person is doing at the same time.

Experiment 2 pushed the logic further. If episodic future thinking works by strengthening the retrospective component, making the memory content itself more durable, then increasing the difficulty of that component should interact with the strategy’s benefit. The team tested 70 participants under the non-focal condition while deliberately increasing the difficulty of the retrospective component, requiring participants to hold and act on more demanding information about what to do when the cue appeared. The multinomial processing tree analysis delivered a decisive answer: even when the retrospective component was made harder, episodic future thinking significantly enhanced only the prospective component, the cue detection and monitoring process, and left the retrospective component statistically untouched.

This pattern of findings supports what the authors describe as a dual-pathway mechanism. Episodic future thinking appears to improve prospective memory through two complementary routes: it facilitates spontaneous retrieval, allowing the intention to pop into mind without deliberate search, and it strengthens controlled cue monitoring, the more effortful process of checking the environment for the triggering cue. The core effect, however, sits squarely on the monitoring side. By vividly simulating the future episode at encoding, a person apparently builds a richer, more accessible representation of the cue context, which lowers the threshold for noticing that cue when it later appears in the stream of ongoing activity. The intention is not better stored as content; it is better armed to be triggered.

The technical contribution of the study lies in its methodological rigor. Behavioral accuracy alone could never have distinguished between a monitoring account and a content-strength account, because both predict higher hit rates. By fitting a multinomial model to the full pattern of responses, including trials where participants correctly performed the ongoing task but missed the intention, the researchers could estimate latent process parameters that are not directly observable. The appendix parameters of the model, including probabilities of recognizing prospective memory targets, recognizing non-targets, and guessing, allowed the team to isolate exactly where in the cognitive architecture the improvement occurred. This modeling approach, increasingly common in prospective memory research, transforms a coarse accuracy difference into a mechanistic claim.

The practical implications reach well beyond the laboratory. Prospective memory failures are a hallmark of healthy aging, and they are profoundly disrupted in conditions such as Parkinson’s disease, attention deficit disorders and after brain injury. Prior trials, such as the randomized-controlled study by Stacey Goedeken and colleagues on encoding strategy training in Parkinson’s patients, hint that teaching people to encode intentions through future simulation could become a low-cost, widely applicable rehabilitation tool. The present findings refine the prescription: the strategy works because it primes cue monitoring, so it should be paired with interventions that make cues salient in the real environment, from phone notifications to strategically placed objects. The fact that the benefit carries no ongoing-task cost is particularly important for everyday multitasking, where remembering to remember often competes with driving, working or conversing.

The study also connects to a broader theoretical debate that has animated the field since Einstein and McDaniel’s foundational work in 1990. For decades, researchers argued over whether prospective memory is driven mainly by automatic, spontaneous processes or by effortful, capacity-consuming monitoring. The multiprocess view holds that both operate, with cue focality determining which dominates. The new evidence adds a twist: an encoding strategy can simultaneously boost both pathways, and its signature effect is on the monitoring process even for cues that should, in principle, be caught spontaneously. As future thinking research expands, with meta-analytic work by Gioia Cona and colleagues mapping the shared neural correlates of future simulation, prospective memory and delay discounting, the picture emerging is of a mind that remembers the future by first living it, and that the act of pre-living an intention quietly recalibrates the attentional systems that will later recognize the moment has come. The Henan team’s work, supported by the Henan Provincial Science and Technology Research Project, marks a substantial step toward making that recalibration deliberate, teachable and clinically useful.

Subject of Research: Cognitive mechanisms by which episodic future thinking enhances prospective memory encoding and retrieval

Article Title: Episodic future thinking as an effective encoding strategy for prospective memory: unraveling its underlying cognitive mechanisms

Article References: Guo, Y., Mao, J., Ma, F., Gan, J., & Li, Y. (2026). Episodic future thinking as an effective encoding strategy for prospective memory: unraveling its underlying cognitive mechanisms. Current Psychology, 45(19), Article 1571. https://doi.org/10.1007/s12144-026-10122-8

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10122-8

Keywords: episodic future thinking, prospective memory, cue focality, spontaneous retrieval, cue monitoring, multinomial processing tree model, retrospective component, prospective component, dual-task paradigm, encoding strategy, cognitive psychology, memory

Distributed practice refers to the scheduling of learning episodes across multiple sessions separated by intervals of time, rather than massing them together in a single marathon session. The effect is well documented in laboratory settings: material reviewed in spaced sessions is retained substantially longer than material reviewed in back-to-back sessions of equal total duration. The benefit appears across verbal learning, mathematics, motor skills, and classroom subjects, and it scales with the length of the spacing intervals up to a point, provided that the delays do not push review beyond the point of forgetting. In practical terms, a student who studies a topic for one hour on four separate days typically outperforms a student who studies for four hours in one sitting, even though the total time invested is identical.

Given this evidence, the persistence of cramming among university students is a puzzle that researchers have approached from several angles. One line of work suggests that students hold flawed beliefs about their own learning. Massed study feels effective because it produces rapid, visible progress in the moment, a fluency illusion that students mistake for durable mastery. Spaced study, by contrast, introduces a degree of difficulty and forgetting between sessions that feels less productive even though it is precisely that difficulty which strengthens long-term retention. Surveys and classroom studies have repeatedly found that students rate cramming as at least as effective as spacing, and sometimes more so, despite objective outcomes pointing the other way.

A second line of explanation focuses not on beliefs but on behavior. Even students who know that spacing works may fail to translate that knowledge into action. Distributed practice is, at its core, a planning and self-regulation problem. It requires a student to anticipate future deadlines, allocate multiple study sessions across weeks, and initiate study at the intended times despite competing demands, social distractions, and the constant pull of more urgent-feeling tasks. Prospective memory failures, poor time management, and simple procrastination can all erode an intention to space out studying long before the exam arrives. In this view, the bottleneck is not ignorance of the strategy but the execution of it.

The new research tackles this execution problem using implementation intentions, a self-regulation technique introduced by the psychologist Peter Gollwitzer. An implementation intention takes the form of an if-then plan: if situation X arises, then I will perform response Y. Rather than merely intending to study in a distributed fashion, a student might commit to the specific plan that if it is 7 p.m. on Monday, Wednesday, and Friday, then I will review my lecture notes for twenty minutes. The technique works by linking a concrete cue to a concrete action, which delegates the initiation of behavior to the environment rather than relying on in-the-moment willpower. Hundreds of studies across health behavior, goal pursuit, and education have shown that implementation intentions increase the rate at which intentions are converted into action, particularly when the gap between intention and behavior is large.

Applying this framework to study scheduling, the researchers investigated whether prompting university students to form if-then plans would increase their use of distributed practice and, in turn, improve their learning outcomes. The work sits at the intersection of cognitive psychology and educational intervention design, and it reflects a broader movement in the science of learning: moving beyond demonstrating that strategies work in the laboratory toward understanding how to get students to adopt them in the messy, self-directed context of real university life. University study is an ideal test bed for this question because, unlike secondary school, it places the burden of scheduling almost entirely on the learner, with few external structures to enforce regular review.

The study’s findings speak to two distinct audiences. For learning scientists, the research clarifies the anatomy of the strategy-use gap. The problem is decomposed into components: do students believe spacing works, do they intend to use it, do they plan for it, and do they actually do it? By measuring these stages separately, the research can pinpoint where the chain breaks. The evidence indicates that knowledge and intention are not the whole story; the translation of a general intention into a concrete, cue-linked plan is a critical and often missing step. Students who formed specific if-then plans were better positioned to distribute their study sessions across time than students who held only vague intentions to space their learning.

For educators and institutions, the practical implications are encouraging because the intervention is cheap, brief, and scalable. Implementation intentions require no technology, no additional instructional time to speak of, and no restructuring of courses. A short prompt at the start of a course, a worksheet embedded in a learning management system, or a nudge in a first-year study-skills seminar could plausibly teach students a planning habit that generalizes across subjects. The approach also complements other evidence-based techniques such as retrieval practice and interleaving, which face similar adoption problems. A student who has planned spaced review sessions has created the schedule slots into which retrieval practice can then be placed, suggesting that combining planning interventions with strategy training may be more powerful than either alone.

The research also carries a note of caution about overestimating what any single technique can achieve. Implementation intentions increase the likelihood that a planned behavior is initiated, but they do not guarantee that the behavior is high quality, that the chosen intervals are optimal, or that students will persist when plans collide with real life. Effective distributed practice also depends on sensible interval lengths, which in turn depend on when the material will be tested. A plan that spaces review too widely relative to an imminent exam can backfire, and students need guidance on calibrating intervals, not just on sticking to a schedule. The most defensible reading of the evidence is that if-then plans are a valuable delivery mechanism for good study strategies rather than a substitute for them.

More broadly, the study contributes to a reframing of study advice that has been gathering momentum in educational psychology. Telling students what to do, the traditional approach of study-skills workshops and learning-to-learn courses, has produced disappointing effects on actual behavior. The emerging alternative treats studying as a goal-pursuit problem and borrows the self-regulation tools that have proven effective in other domains: concrete planning, cue-based triggers, monitoring, and environmental structuring. On this view, the science of learning has two jobs. The first is to identify which cognitive strategies produce durable learning, a task largely accomplished for distributed practice. The second, newer and arguably harder, is to engineer the conditions under which students actually deploy those strategies, week after week, in the service of their own goals. This research on implementation intentions is a step in that second direction, and it suggests that one of the most effective things a university could teach its students may not be another study technique at all, but a simple grammar for turning good intentions into scheduled action.

Subject of Research: The use of implementation intentions to support university students' adoption of distributed practice as a learning strategy.

Article Title: Understanding and supporting university students’ use of distributed practice via implementation intentions

Article References: Understanding and supporting university students’ use of distributed practice via implementation intentions. (n.d.). https://doi.org/10.1038/s41539-026-00448-0

Image Credits: AI Generated

DOI: 10.1038/s41539-026-00448-0

Keywords: distributed practice, spaced practice, implementation intentions, university students, self-regulated learning, study strategies, cramming, prospective memory, learning science, npj Science of Learning, retrieval practice, educational psychology