How Working Memory Moderates Function Learning Behaviour: A Dual-Task Paradigm
Bibliographic record
Abstract
A breadth of research has demonstrated that many cognitive phenomena can be explained by a dual-processing account.However, little research has attempted to apply a dual-task paradigm to function learning.The present thesis aims to fill this gap in the literature by exploring the relationship between working memory and function learning behaviour.Eighty Carleton University students were randomly assigned to learn either a linear or bilinear function.Moreover, participants were randomly assigned to complete training and transfer under either single-or dual-task conditions.It was hypothesized that the secondary task would hinder performance resulting in a dependency on exemplar-based learning.Using a novel classification approach, the results showed that the secondary task reduced the stability of learning approach.However, the results remain inconclusive due to low power.Therefore, additional research is required to determine whether dual-task paradigms can be used to distinguish between rule-and exemplar-based processing in function learning.A Dual-Task Paradigm iii Acknowledgments To my colleagues, who have helped me through the highs and lows of my master's degree.To Guy, who's tutelage shaped the man I have become.To my mom Awatef, who was never short on love and support.Finally, to Fay for helping me keep my eyes on the stars and my feet on the ground.Thank you.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".