From abstract to new question: what science allows us to affirm about search, learning, recovery and explanation

A Short response

The best scientific description of the process is that of a Iterative cycle of self-regulated research and learning, in which informational behavior alternates — formulating objective, seeking, evaluating and integrating sources —, generative learning — to elaborate, explain and represent —, recovery practice and metacognition — to judge what is known, test and calibrate. Curiosity can start or guide the cycle when there is a perceived gap, but it is not a requirement of every search or guarantee of learning [1, 15, 25].

There is no unique scientific nomenclature for the complete process. There are partial and partially overlapping models — information behavior, information seeing, exploratory search, inquiry-based learning, self-directed or self-regulated learning, generative learning, retrieval practice, self-explanation, teaching effect and post-recovery update — but none of them have been demonstrated as the name or as causal validation of the entire cycle [15, 16, 25, 56, 92, 102]. These constructs should not be treated as synonyms.

The most defensible formulation is therefore: a plausible practical architecture that combines distinct mechanisms, with separate evidence for some links and without direct causal test of the complete chain.

B Scientific concepts involved

1. Summary information and initial representation

A summary, guess or initial frame builds a provisional representation of the subject. It can activate prior knowledge and make explicit terms, relationships and gaps, but it can also crystallize errors. Prior knowledge influences the research strategy: family tasks favor theory-guided exploration; unknown tasks require greater data-driven exploration [34, 35]. A summary is not yet demonstrated learning: it is a product of understanding that needs to survive recovery, explanation and transfer.

2. Epistemic curiosity, gap, trust and relevance

In Loewenstein's formulation, curiosity is a perceived cognitive deprivation, produced by a gap between what is known and what is wanted to know [1]. The trivia literature often finds greater curiosity in intermediate trust, but the relationship is conditional: perceived importance can sustain curiosity even when confidence is low [2, 4].

It is crucial to separate:

• gap size: how incomplete is representation;
• uncertainty or trust: how likely a person thinks he is correct;
• importance or value: how much information is needed;
• recoverability: how accessible the answer seems;
• news and surprise: discrepancy between expectation and event;
• familiarity and feeling-of-knowing: metacognitive signs that may guide search, but are not curiosity by definition [3, 5, 7, 8, 9, 14].

The inverted U curve between trust and curiosity is a frequent trend, not a universal law. Literature also shows dynamic curiosity: interest can increase, decrease or change function as evidence reveals itself [10].

3. Information search and exploratory search

Information behavior is broader than typing queries: includes context, need, choice of sources, interpretation and use [15]. Information seeing is the active search; exploratory search combines locating, learning and investigating when the goal is still being formulated [25, 26]. The theory of information foraging describes as clues of value — information — and costs guide clicks, backtracking and abandonment [28, 29, 30]. Scent predicts navigation in specific tasks, but does not certify credibility, truth or learning.

4. Inquiry, guidance and self-directed learning

Inquiry-based learning includes guidance, conceptualization, investigation, conclusion and discussion, with return to questions and hypotheses [92]. Meta-analyses indicate that research with adequate guidance overcomes both unassisted discovery and, under certain conditions, less active instruction [20, 21, 22]. This does not imply total control of the apprentice: indicates scaffolding calibrated, feedback, prior knowledge and explicit goals.

Self-directed learning is multidimensional: self-management, self-monitoring, motivation and resource use are not the same thing [17, 18, 19]. Learning alone does not automatically mean directing one's own learning well, and autonomous search does not guarantee evaluation of sources or retention.

5. Learning, coding and generative learning

Learning here must mean relatively durable and usable change, not just exposure, satisfaction or time. Generative learning is a family of activities that organizes, integrates and transforms information, how to explain, summarize, visualize, compare, predict and teach [56, 57]. The effect depends on prior knowledge, quality of representation, modality, timing and congruence with the material [58, 59].

6. Recovery, spacing, feedback and transfer

Retrieval practice It's trying to retrieve information from memory before you consult it. It is distinct from restudy, search and explanation. In general, the recovery exceeds the restudy for delayed retention, although the restudy may win in the immediate test [37, 40]. Spacing recoveries is robust; there is no general superiority of expansion over uniform spacing [38, 39]. Feedback reduces the risk of consolidating errors, but incorrect multiple choice alternatives can create false paths [44, 45].

Transfer is application to different format, context, problem or inference. The meta-analysis finds positive, but selective and lower benefit for untested material, rearrangements and problems with worked examples [41].

7. Self-explanation, elaboration and generation

Self-explanation it is to produce for you relationships, justifications, inferences and repairs while studying. Self-explanation prompts have positive mean effect — approximately g = 0.55 in 64 reports and 69 effects — but the correlation between spontaneously explaining and learning does not prove that increasing speech causes gain [46, 47, 49].

Preparation adds relationships, examples, causes, analogies or consequences, preferably anchored in relevant knowledge [53]. Generation is to produce a response or item, and does not reduce to building mental model [54]. Generating learning is the broadest category [56, 57]. None of them should be confused with recovery: explaining may contain recovery, but also involves integration and new coding.

8. Explain, teach, teach effect and protégé effect

Prepare to teach, explain to a fictional audience, effectively teach and interactively tutor are different manipulations. There are studies favorable to the expectation of teaching and video teaching, but effects vary with orality, audience, time, notes, interaction and delay [60, 61, 62, 63, 64, 65]. Kobayashi's meta-analysis estimated a small effect to teach after studying — g = 0.27 —, greater under expectation of teaching — g = 0,48 — and close to zero without this expectation — g = -0,02 [66]. That doesn't authorize the rule to teach is always better. O protégé effect is a specific explanation of effort and responsibility for an apprentice, not synonymous with all self-explanation [60].

9. Metacognition, questions and illusion of depth

Understanding judgments predict performance only weakly: in a meta-analysis, the correlation without intervention was r = .178 [68]. Generating questions by itself did not detectably improve the accuracy of monitoring, while self-explanation showed a positive effect on this synthesis [68]. Metacognitive interventions have a small and heterogeneous effect [69].

Explaining can reveal missing links and reduce exaggerated confidence, as in studies of illusion of explanatory depth [72]. However, an incomplete explanation can generate a new search in some contexts, but not selectively in all [73]. Questions and explanations are diagnostic instruments; they do not replace performance tests.

10. Consolidation, reconsolidation and update

After recovery, the memory can be updated under certain conditions, but Not every recovery makes memory labile. Reconsolidation is a mechanistic hypothesis that usually requires a consolidated memory, directed reactivation, conditions of labilization, possible prediction error, temporal window and selective effects on the late test [76–90]. In humans, results are indirect, task dependent and compatible with alternatives such as extinction, interference, source confusion and state dependence [84, 89, 90].

11. Epistemic emotions

Curiosity, surprise, confusion, interest and pride are not interchangeable. Surprise and curiosity predict exploration in some studies; confusion and pride are less consistent [8]. Importance and valence affect the preference for knowing or ignoring information and recruit mesolimbic value, but this does not equal learning [14].

C Textual/visual integrated model

The model below is a representation of work, not a new theory or nomenclature and not a validated causal package.

[Informação resumida / palpite inicial]
             │
             ├── ativa conhecimento prévio e hipótese
             └── explicita confiança, importância e lacuna percebida
                              │
                              v
                  [Curiosidade / pergunta / objetivo]
                 (não necessária para toda busca)
                              │
                              v
       [Busca: consulta → fonte → comparação → síntese provisória]
             │                         │
             ├── pistas de valor/custo  ├── orientação e feedback
             ├── avaliação de evidência └── atualização da hipótese
             └── nova informação
                              │
                              v
          [Aprendizagem: integrar, organizar, prever, representar]
             │                         │
             ├── recuperação gerativa  ├── elaboração/autoexplicação
             └── exemplos/transferência └── possível preparação para ensinar
                              │
                              v
        [Teste sem consulta + feedback + confiança pós-teste]
             │                         │
             ├── retenção imediata     ├── retenção tardia
             └── aplicação/transferência
                              │
                              v
                [Explicação própria e checagem]
             │                         │
             ├── elos ausentes/erros   ├── integração de informação
             └── redução de excesso de confiança
                              │
                              v
               [Novas perguntas / nova busca focalizada]
                              │
                              └───────────┐
                                          v
                         [Recuperações espaçadas e testes tardios]

Transverse moderators: prior knowledge; calibrated confidence; importance; recoverability; type and quality of source; guidance; feedback; question format; time; recoverable difficulty; state and context; age; domain; quality of explanation; transfer objective.

Distinctions that the diagram preserves:

1. Search is a contextual activity; recovery is an attempt to access memory without consultation.
2. Self-explanation is generative production; teaching effect is a comparison of preparation or teaching with other conditions.
3. Curiosity is a motivational or epistemic state; surprise is discrepancy; post-response interest may have its own contribution.
4. Post-recovery update is a possible behavioral or cognitive result; reconsolidation is a stronger neurocognitive explanation and undiagnosed for explaining.

D Evidence Matrix

And What is proven, probable and speculative

Relatively proven, under the conditions studied

• Recovery practice tends to improve late retention more than equivalent restudy; the result is not simply an immediate performance effect [37, 40].
• Recovery spacing tends to favor retention, although there is no universal expansive calendar or agenda [38, 39].
• Appropriate guidance improves many forms of inquiry compared to unassisted discovery [20–22].
• Self-explanation prompts and some generative activities produce positive average gains, with heterogeneity [46, 56].
• Curiosity, especially in the face of recoverable gaps, is associated with search and memory in controlled paradigms; memory benefit is greater for the target than for incidental information [2, 11, 12].
• Subjective judgments of understanding are insufficient: they need to be confronted with recovery and application [68, 69].

Probable but conditional and not universal

• A local gap, important and perceived as answerable can increase search and commitment; importance can compensate low confidence [1, 4].
• Previous summaries and guesses can make the search more focused when they activate relevant knowledge and testable hypotheses, but they can also load errors [34, 35].
• Explaining after recovery can improve integration, reveal gaps and support retention when the explanation is generative, correct and followed by feedback [47, 49, 53].
• Teaching or preparing to teach can increase effort, organization and understanding, especially with adequate modality; there is no universal superiority over self-explanation [60–66].
• Recovery followed by discrepant information can update a prior representation under specific conditions, especially with appropriate forecasting and timing error [78–81].
• A routine that combines prediction, oriented search, recovery, explanation, feedback and spacing is pedagogically plausible, but this conclusion is of operational synthesis, not of an entire cycle test.

Speculative, exaggerated or not yet demonstrated

• The inverted U curve between trust and curiosity explains every human search. No: importance, novelty, progress, context and recoverability change the pattern [4, 9].
• Curiosity causes learning in general. Studies support associations and some memory benefits, but prior knowledge and metacognition also predict learning; there are results with no effect independent of curiosity [5, 6, 12].
• More clicks, time or questions mean more learning. Logs may reflect foraging, confusion, cost or procrastination; late testing and transfer is required [27, 28, 31, 32].
• Explain reconsolidated memory. The phrase is excessive and is not shown in all three levels; see the specific analysis below.
• Teaching is always better than studying or self-explanating. The results depend on expectation, act, audience, orality, feedback, material and delay [60–67].
• The autonomous investigation without orientation develops more learning. Meta-analyses favor calibrated guidance and show costs of unassisted discovery [20–24].
• The full cycle has been validated. The reports do not present tests that manipulate the entire chain and compare delayed retention, transfer and search quality.

Specific audit of the sentence explain reconsolidated memory

Behavioral level

What can be observed is that, in some conditions, recover and then explain whether it associates or causes improvement in memory, understanding, integration or transference. Self-explanation and elaboration studies support behavioral gains, but these gains can also result from deeper recovery, new coding, relational organization, feedback or additional practice [46–53]. In memory paradigms, a reminder followed by new information can increase intrusions, integration or update [78, 79]. This demonstrates performance change, not consolidation as a mechanism.

Cognitive level

Explaining can build a mental model, infer causal links, detect contradictions, correct error and integrate new information. This process is compatible with post-recovery update. It may also involve interference, source confusion, state dependence or extinction. Even when memory changes selectively after a clue, it is not automatically concluded that it has passed through a reconsolidation window [84, 89, 90].

Neurocognitive level

Reconsolidation is a stronger hypothesis: after reactivation, a memory can become labile and require stabilization processes; animal evidence includes pharmacological manipulations with temporal relationship and reactivation dependence [76, 77]. In humans, the results are indirect. Forecast error seems a possible border condition [81], but there are nulls [82], inconsistencies [83–86] and direct replications without benefit of the reactivation-extinction effect [87, 88]. fMRI and circuit changes do not prove the mechanism itself [80].

Verdict: the scientifically honest formulation is: explain after recovering can favor learning and behavioral or cognitive updating in some conditions; it is compatible with, but does not demonstrate or imply, neurocognitive reconsolidation. To test reconsolidation it would be necessary to manipulate reactivation, prediction error, timing and selectivity, include controls against extinction, interference and source confusion and to test late retention. The study routine does not provide this diagnosis.

F Complete article

Introduction

Studying often begins with a condensed representation: a summary, a hypothesis, an incomplete definition or a provisional explanation. This representation can expose a gap: I understand the result, but not the cause; I know the term, but I can't apply it; I have a hunch, but I don't know if it's true. The gap can produce curiosity and guide search. The search, in turn, provides material to learn; learning can be tested by recovery; the explanation can reveal errors and create new questions.

The sequence is intuitively convincing, but science does not authorize treating it as a single chain already demonstrated. There is relatively strong evidence for recovery and spacing, moderate for guidance in inquiry and self-explanation, and more conditioned for curiosity, exploratory search, teaching effect and post-recovery update. The central point of this review is to preserve what studies actually measure: curiosity is not search; search is not learning; explanation is not retrieval practice; teaching is not automatically protégé effect; and updating is not automatically reconsolidation.

Question

How to scientifically describe the process summary information → curiosity/lacuna → search → learning → recovery → explanation → new questions/search, what each link sustains, what are the limitations and how to turn it into a testable routine without promising more than evidence?

What Science Knows

Science knows that incomplete representations and prior knowledge modulate what is perceived as informative. Curiosity tends to be greater when there is a relevant and recoverable gap, but the pattern depends on confidence, importance, novelty and structure of the environment [1–5, 9]. You also know that search is iterative: objectives can be reformulated when new evidence arises, and value clues guide navigation [15, 16, 25, 28–30].

In inquiry and discovery, adequate support is decisive. Unassisted discovery overloads or disorients in many tasks; prompts, examples, feedback and guidance can preserve intellectual activity without leaving the learner without structure [20–24]. This is compatible with the idea of starting with a summary or guess and looking for precisely what is missing, as long as the search has source and verification criteria.

The most robust step for lasting learning is recovery. Trying to remember without consultation, correct and recover late tends to produce retention greater than reread. The benefit decreases when recovery is impossible, when the error does not receive feedback or when the test does not match the goal [37–45]. Therefore, the search must provide material; the recovery must check if something has been incorporated.

Self-explanation and generative learning can improve understanding, especially when the learner explains causal relationships, justifies steps, anticipates consequences, adds examples and verifies representation. However, a long explanation is not necessarily a good explanation; quantity may reflect verbal fluency, motivation or prior knowledge. Prompts and quality import [46–59].

Finally, teaching can alter effort and organization, but the result depends on preparing to teach, teach in fact, speak or write, have a real or fictitious audience and receive questions. The teaching effect label should be reserved for the studied comparison, and not used as a synonym to explain to you [60–67].

The Cycle

1. Summarize and predict. Produce a memory summary, a guess and a numerical trust. The goal is to make the representation examineable and reveal the gap.
2. Name the gap. Distinguishing I don't know the fact, I don't understand the mechanism, I can't apply and I don't know which source is reliable. Register importance and what would count as an answer.
3. Search with focus. Form few queries, choose sources by authority and triangulation, register evidence, inference and conflict. Avoid the search time being the only indicator of study.
4. Integrate. Compare the one found with the guess; rephrase a model, a causal sequence, a table or a distinction. Here is possible learning, not yet proven learning.
5. Recover without consultation. Close the sources and respond. Production should include facts, relationships, examples and at least one application when that is the objective.
6. Explain and check. Explain to yourself or a defined audience; mark each unsupported link; consult the source only to correct. Explanation is generative and diagnostic; it does not replace the test.
7. Ask again and space. Turn each error or missing link into focused question, seek only what is necessary and repeat recovery in delay. The cycle is iterative, not circular in order to ensure progress.

What we know about the cycle, and not just about its components

We know that components can combine consistently. Curiosity can motivate the search; guidance can prevent dispersion; recovery can test incorporation; explanation can diagnose and generate questions; spacing can consolidate access. That's a functional inference supported by distributed evidence.

We do not know, from these reports, whether the complete sequence systematically overcomes an alternative sequence, such as direct instruction plus recovery, or whether each component adds incremental value in any domain. We also do not know whether the curiosity that initiates a search is the same variable that improves memory, whether the effect of the explanation is independent of the embedded recovery or whether the gain results only from more time and effort. The absence of a complete cycle direct test is a central limitation.

What we don't know yet

• What is the minimum dose of curiosity, search and explanation that improves late retention without increasing cost or distraction.
• When the initial summary helps and when a wrong guess contaminates the search.
• If the exploratory search, with evaluation of sources, improves transference more than a short selection of reliable sources.
• Whether questions generated by the learner increase the quality of learning or just their perceived activity.
• Which type of explanation is best for which material, age and purpose.
• If teaching adds effect beyond recovering and self explaining, when time and feedback are matched.
• Under which conditions the update after recovery is reconsolidation, and not new encoding, interference or source confusion.
• If laboratory results with trivia, lists, conditioned fear or hidden words generalize for real study, complex texts and problems.

Possible practice

A defensible practice does not promise that curiosity will make you learn. It uses curiosity as a sign to formulate a question, seeking as acquisition and evaluation of evidence, recovery as a test, explanation as diagnosis and spacing as maintenance. Success is defined by late recall, correct explanation and transference, not by a sense of clarity, number of pages, clicks or online time.

Protocol

For a topic, the apprentice:

1. writes summary and guess without consulting;
2. records confidence from 0 to 100, importance from 0 to 100 and the gap in a sentence;
3. asks up to three verifiable questions;
4. search sources and records for each statement: source, evidence, inference and conflict;
5. updates the summary in a short version;
6. closes the sources and makes free memory;
7. writes a causal or procedural explanation and a counterexample;
8. checks, marks errors and remakes the answers;
9. performs a new test in 24 hours and then in 7 days;
10. includes a transfer question in a different context;
11. records new gaps only after the test, so as not to confuse exploration with domain.

This protocol is a Inferred operational proposal, not an intervention validated as a whole. Its efficacy should be evaluated by comparison and delayed retention.

Conclusion

The process describes a plausible architecture of self-regulated research and learning, but not a unique theory. The strongest evidence recommends a simple discipline: produce a forecast, search with guidance and criteria, recover without consultation, explain relationships, correct with feedback and revisit in delay. Curiosity and search can make the study more focused; they do not replace recovery and transference. Explain can update a representation; does not allow declaring reconsolidation. Scientific language must follow the actual size of evidence.

G Practical method

Method of a 45–60 minute session

0–5 min — Summary and forecast

• Write from memory what you understand.
• Give a provisional answer to the central question.
• Register trust and importance separately.
• Circle a causal link, term or application missing.

5–10 min — Verifiable questions

It converts the gap into questions that can be answered by evidence. Prefer which mechanism explains X or in what condition X ceases to occur to a merely defining question.

10–25 min — Targeted search

• Choose sources with authorship, method and access to the material.
• Register queries, discarded fonts and reason for disposal.
• Separate citation or fact, inference and hypothesis.
• Look for at least one border condition or conflicting result.
• Stop when the questions are answered or when the new search only repeats evidence.

25–35 min — Integration and recovery

Close the sources. Recover definitions, mechanism, conditions, limitations and an example. Do not consult during the first attempt.

35–45 min — Explanation and audit

Explain to a real or fictional reader, including what happens, why it happens, when it doesn't happen and how I would know I'm wrong. Mark links without support.

45–60 min — Feedback and next test

Compare with the fonts, correct in another color and generate an application issue. Schedule recovery in 24 hours and 7 days. If recovery fails completely, reduce difficulty, consult a short track and try again; do not interpret extreme failure as desirable difficulty automatically.

Decision rules

• High trust and error: prioritize diagnosis of illusion of understanding and feedback; not only increase exposure.
• Low trust and high importance: seek a reliable source and a minimal explanation; curiosity can be high even with little knowledge.
• High curiosity and recoverable response: turn it into question and late test; do not prolong the search without stopping criteria.
• Fluent explanation and poor recovery: treat as possible fluency or illusion; return to recovery.
• Many sources and little transfer: Stop collecting, formulate different problems and applications.
• Recovery failure: offer feedback, re-estue the minimum link and space a new attempt.

I Self-experiment

Objective

Test, in a person and in a set of topics, whether a routine that includes prediction, focused search, recovery, explanation, feedback and spacing produces better late retention and transfer than a condition of comparison. Self-experiment is exploratory: it does not test the universal validity of the cycle or reconsolidation.

Suggested design

Use two sets of comparable topics, A and B, chosen before studying and alternated to reduce order effects. If possible, balance difficulty, size, interest and prior knowledge. One condition may be:

• Cycle condition: abstract or guess → questions → documented search → synthesis → recovery → explanation → feedback → spacing.
• Comparison condition: reading or restudy of equivalent source → Recovery test with feedback → spacing.

The comparison should not be called pure control if the time, number of exposures or contact with material differ. Register duration and match time as much as feasible. Since there is no package validation, differences will not allow the effect to be attributed to curiosity, search or explanation alone.

Variables

Primary

1. Late recall: proportion of correct ideas without consultation in 24 hours, 7 days and 21–28 days.
2. Transfer: performance in problem, example or context not identical to study.
3. Quality of explanation: In advance initialize facts, causal relationships, conditions, counterexample and absence of contradiction; use equal criteria in both sets.

Secondary

• pre-test and post-test confidence;
• calibration error = difference between confidence and performance, defined before;
• number and quality of questions;
• queries, sources and search time;
• self-reported importance and curiosity;
• errors detected after feedback;
• interest, surprise and confusion apart, without treating them as synonyms.

Withdrawal and agenda

Do an immediate test just to diagnose, not to complete domain. Do tests without consultation on 24 hours, 7 days and 21–28 days; keep the questions comparable, but include at least one transfer item. Do not only use recognition if the goal is production: include free recall or response with few clues, because the format measures generational access [37–45]. Also register when the correct answer was given with low confidence or wrong with high confidence.

Criteria defined before starting

• State which outcome is primary and what difference would be educationally relevant to you.
• Do not change the rule after seeing results.
• Consider the promising cycle only if there is consistent improvement in the late test and not only in the immediate test; see if the improvement also appears in transference.
• If you have gained only in confidence, curiosity, time, number of questions or production of explanation, do not classify it as learning gain.
• If you have gained in memory, do not automatically classify it as reconsolidation.

These criteria are protocol choices, not universal thresholds derived from reports.

Vises and controls

• Effect of interest: curious topics may receive more time and effort; balance interest or treat it as a descriptive covariate.
• Prior knowledge: measure it before and do not directly compare very unequal topics.
• Difficulty and recoverability: an impossible gap can produce failure, not productive curiosity; adjust clues gradually.
• Effect of practice: use equivalent items, not the same item in all tests, when transfer is objective.
• Order effect: alter A and B and distribute schedules.
• Time and exposure: register and try to match duration; explanation can simply add study.
• Font bias: use equivalent sources and register conflicts; a single source does not prove true.
• Confirmation bias: include a border condition and contrary evidence.
• Expectation: knowing that a condition is new can increase motivation; do not call it a specific mechanism.
• Contaminated item: evaluate without looking at the condition when possible.
• Follow-up losses: record delayed, forgotten or remade tests.
• Generalization: a self-experiment N=1 does not estimate population, age, domain or modality.

Interpretative limitations

The comparison evaluates a combination of components. Even if the cycle wins, it will not be possible to know if the responsible person was curiosity, search, explanation, more effort, feedback or simply more time. Even if it does not win, this does not refute each component: a miscalibrated curiosity, bad source or superficial explanation can mask a recovery benefit. To isolate components, more topics and a factorial design or separate conditions would be needed, something that reports do not validate as a ready protocol.

Above all, this self-experiment cannot test the neurocognitive affirmation of reconsolidation. It can measure behavioral change after recovery and explanation; it does not measure molecular lability, reconsolidation window or specific neural mechanism.

J Comments

The references below consolidate the sources cited. The comment summarizes its function in the argument of this review, without adding findings beyond structured reports.

1. Loewenstein, G. (1994). The Psychology of Curiosity: A Review and Reinterpretation. Psychological Bulletin, 116(1), 75–98. DOI: https://doi.org/10.1037/0033-2909.116.1.75. — Formulates curiosity as deprivation by perceived gap and emphasizes that search can occur without curiosity.
2. Kang, M. J., et al. (2009). The Wick in the Candle of Learning: Epistemic Curiosity Activates Reward Circuit and Enhancements Memory. Psychological Science, 20(8), 963–973. DOI: https://doi.org/10.1111/j.1467-9280.2009.02402.x. — It relates intermediate trust, curiosity, search and memory in trivia; fMRI and memory do not establish general causality.
3. Singh, A., & Manjaly, J. A. (2021). The Effect of Information Gap and Uncertainty on Curiosity and Its Resolution. Journal of Cognitive Psychology, 33(4), 403–423. DOI: https://doi.org/10.1080/20445911.2021.1908311. — Separate gap and uncertainty in lexical task and shows that both predict curiosity.
4. Spitzer, M. W. H., Janz, J., Nie, M., & Kiesel, A. (2023). On the Interplay of Curiosity, Confidence, and Importance in Knocking Information. Psychological Research, 88, 101–115. DOI: https://doi.org/10.1007/s00426-023-01841-9. — It shows the importance of dislocating the relationship between trust and curiosity.
5. Wade, S., & Kidd, C. (2019). The Role of Prior Knowledge and Curiosity in Learning. Psychonomic Bulletin & Review, 26, 1377–1387. DOI: https://doi.org/10.3758/s13423-019-01598-6. — Distinguish estimated knowledge, objective knowledge and curiosity; limits simple causal narrative.
6. Chen, X., Twomey, K. E., Hayes, M., et al. (2025). The Limits of Curiosity? New Evidence for the Roles of Metacognitive Abilities and Curiosity in Learning. Metacognition and Learning, 20, article 1. https://doi.org/10.1007/s11409-024-09407-9. — Finds learning predicted by the metacognitive evaluation of gap, without significant independent effect of curiosity.
7. Brooks, G., Yang, H., & Köhler, S. (2021). Feeling-of-Knowing Experiences Breed Curiosity. Memory, 29(2), 153–167. DOI: https://doi.org/10.1080/09658211.2020.1867746. — It shows that feeling-of-knowing guides search, but is not identical to epistemic curiosity.
8. Vogl, E., Pekrun, R., Murayama, K., Loderer, K., et al. (2019). Surprise, Curiosity, and Confusion Promotion Knowledge Exploration: Evidence for Robust Effects of Epistemic Emotions. Frontiers in Psychology, 10, 2474. DOI: https://doi.org/10.3389/fpsyg.2019.02474. — Compares epistemic emotions and supports surprise and curiosity more consistently than confusion and pride.
9. Poli, F., Meyer, M., Mars, R. B., & Hunnius, S. (2022). Contributions of Expected Learning Progress and Perceptual Novelty to Curiosity-Driven Exploration. Cognition, 225, 105119. DOI: https://doi.org/10.1016/j.cognition.2022.105119. — It shows progress roles and novelty in exploratory choice; it does not support a single mechanism.
10. Hsiung, A., Poh, J. H., Huettel, S. A., & Adcock, R. A. (2023). Curiosity Evolves Information Unfolds. Proceedings of the National Academy of Sciences, 120(43), e2301974120. DOI: https://doi.org/10.1073/pnas.2301974120. — Demonstrates a dynamic character of curiosity when information reveals itself.
11. Fandakova, Y., & Gruber, M. J. (2020). States of Curiosity and Interest Enhancement Memory Differently in Adolescents and in Children. Developmental Science, 24(1), e13005. DOI: https://doi.org/10.1111/desc.13005. — Different curiosity, interest and surprise and shows selective benefit for trivia.
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98. Sinha, T., & Kapur, M. (2021). When Problem Solving Followed by Instruction Works: Evidence for Productive Failure. Review of Educational Research, 91(5). DOI: https://doi.org/10.3102/00346543211019105. — Meta-analysis of production failure with domain and design-dependent effect.
99. Nachtigall, V., Serova, K., & Rummel, N. (2020). When Failure Failures to Be Productive: Probing the Effectiveness of Productive Failure for Learning Beyond STEM Domains. Instructional Science, 48, 651–697. DOI: https://doi.org/10.1007/s11251-020-09525-2. — Shows limit of generalisation outside STEM.
100. Darabi, A., Arrington, T. L., & Sayilir, E. (2018). Learning from Failure: A Meta-Analysis of the Empirical Studies. Educational Technology Research and Development, 66, 1101–1118. DOI: https://doi.org/10.1007/s11423-018-9579-9. — Meta-analysis recording few usable experimental studies and heterogeneity.
101. Scardamalia, M., & Bereiter, C. (2006). Knowledge Building: Theory, Pedagogy, and Technology. In R. K. Sawyer, The Cambridge Handbook of the Learning Sciences, 97–18. URL: https://ikit.org/fulltext/2006 KBTheory.pdf. — Organizing model of Community progress; it does not guarantee individual learning.
102. Panadero, E. (2017). A Review of Self-Regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8, 422. DOI: https://doi.org/10.3389/fpsyg.2017.00422. — Offers self-regulation models to organize monitoring and control.
103. Tinajero, C., Mayo, M. E., Villar, E., & Martínez-López, Z. (2024). Classic and Modern Models of Self-Regulated Learning: Integrative and Componential Analysis. Frontiers in Psychology, 15, 1307574. DOI: https://doi.org/10.3389/fpsyg.2024.1307574. — Analyzes contemporary self-regulated learning components and models.