Effects of <i>Familiarity</i> and <i>Complexity</i> on Inhibitory Control in Elementary Science Learning
Bibliographic record
Abstract
Children often hold intuitive conceptions about natural phenomena that are inconsistent with scientific knowledge. For instance, it is common to believe that bigger or heavier objects sink more than others. Previous studies have highlighted the role of inhibitory control in suppressing intuitive conceptions and learning scientific concepts. However, the variables influencing the level of inhibitory control required to overcome intuitive conceptions remain largely unexplored. This research examines the effects on inhibitory control of two variables identified in the literature: the familiarity of intuitive conceptions and the complexity of scientific concepts. We hypothesized that higher levels of both variables would be associated with an increased need for inhibitory control, while lower levels would correspond to a decreased need for inhibitory control. Four negative priming tasks were designed and administered to children aged 10–12 years. Results indicate that high complexity is associated with a higher negative priming effect, which is representative of increased inhibitory control. Interaction effects suggest it is more challenging to resist a highly familiar conception when the scientific concept is complex to grasp. Our findings contribute to enhancing pedagogical reflections on teaching scientific content that requires inhibitory control.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".