A Tale of Two Flexibilities: Preschoolers' Developing Consecutive and Concurrent Cognitive Flexibility Skills
Bibliographic record
Abstract
Cognitive flexibility is the ability to think of something in more than one way.Research examining cognitive flexibility in 3-to 5-year-olds typically focuses on consecutive cognitive flexibility-the ability to consider several dimensions of a single stimulus one after another (e.g., sorting cards depicting blue boats and red rabbits by colour and then by shape).However, relatively little research examines preschoolers' concurrent cognitive flexibility skills-their ability to coordinate several dimensions of a single stimulus simultaneously (e.g., understanding that a blue boat can be both blue and a boat at the same time).The current work focuses on emerging concurrent cognitive flexibility skills in preschoolers.In Study 1, though a structural differentiation between consecutive and concurrent cognitive flexibility was not supported, an exploration of the data suggested that these skills are affected differently by abstraction demands-whether children had to induce the relevant dimensions on their own or were told which dimensions to consider.In this study, 121 preschoolers (Mage = 48.12months; SD = 5.37) received 6 different cognitive flexibility tasks.Consistent with Jacques and Zelazo's (2005) review, children found deductive tasks-tasks in which the experimenter provides all the information to the participants-easier than inductive tasks-tasks that required children to identify the relevant dimensions themselves-in the context of consecutive cognitive flexibility.In contrast, these children found inductive concurrent cognitive flexibility tasks easier than deductive concurrent cognitive flexibility tasks, indicating that abstraction demands affect children's performance on these two cognitive flexibility skills differently.In Study 2, this finding was partially replicated using an experimental design: under certain conditions, 5-year-olds (N = 76) found concurrent Deepthi Kamawar.Deepthi has encouraged me to ask questions and challenged me to come up with better answers.She supported me throughout this project and, indeed, the last eight years of my life.She is a mentor and a role model, and showed me more patience than I had reason to hope for.She made me a researcher and a clear thinker and her voice will follow me in every professional endeavour.I had fantastic committee members with whom I was honoured to work.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".