The effect of task complexity on planning in preterm-born children
Bibliographic record
Abstract
OBJECTIVE: Planning is an important executive function (EF) skill that is fundamental to the capacity to achieve everyday goals that require a series of intermediate steps. This study examined the effect of preterm birth on planning skills in early and middle childhood using Tower problems that made different cognitive workload demands. METHOD: We administered a novel touchscreen Tower of Hanoi task (Monkey Tree Task; MTT) in three age cohorts (3, 6, and 9 years) to 485 children born between 2000 and 2010 (105 extremely low birth weight [ELBW], 248 late preterm [LP], and 132 term-born [Term]). RESULTS: Children born with ELBW completed significantly fewer Tower problems with higher cognitive demands than children born at Term or LP. Likewise, Term- and LP-born children completed more Tower problems than children born with ELBW. In the youngest cohort, Term-born children solved Tower problems more efficiently than either preterm group, and LP-born children solved problems more efficiently than those born with ELBW. However, there were no group differences in efficiency in the older age cohorts. Significant correlations between our MTT measures and performance on other EF tasks were found. CONCLUSIONS: The MTT captured significant performance differences in planning skills between children born term vs. preterm. This study provides important information on the impact that cognitive workload, as a function of Tower problem complexity, has on planning skills in preterm children. This study adds to a growing body of research that distinguishes LP birth as having subtle, but distinguishable, adverse neuropsychological outcomes at earlier ages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".