Giftedness identification and cognitive, physiological and psychological characteristics of gifted children: a systematic review
Bibliographic record
Abstract
Despite the extensive history of investigation, characterization and diagnostics of giftedness is still a point of debate. The lack of understanding of the phenomenon affects the identification process of gifted children, development of targeted educational programs and state of research in the field of gifted education. In the current systematic review, we seek to delineate the specific aspects in which gifted children differ from their typically developing peers in cognitive abilities, psychophysiology and psychological characteristics. Secondly, we aim to document the prevalence and criteria of intelligence tests used to assess gifted children and adolescents. We reviewed 104 articles from more than 25 countries that examined a total of 77,705 children ages 5-18 years. Results reveal a discernible trend toward adopting more culturally appropriate measures for assessing giftedness in children. Findings highlight that gifted children generally outperform their peers in several cognitive domains such as verbal working memory, inhibition, geometric problem solving, attention-switching and elemental information processing, showcasing an accuracy-reaction time trade-off. Psychophysiological assessments demonstrate heightened and accelerated brain activity during complex effortful cognitive processes. Psychological and behavioral measures reveal that gifted children score higher on tests measuring intrinsic motivation, self-efficacy, and openness to experience; as well as achieving higher grades in school and employing better problem-solving strategies. Our systematic review can be beneficial in educational and research contexts, giving directions in assessment of giftedness and designing future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".