The role of intentionality in memory and learning: Comments on Popov and Dames (2022).
Bibliographic record
Abstract
This commentary is a reply to the article "Intent matters: Resolving the intentional versus incidental learning paradox in episodic long-term memory" by Popov and Dames (2022). In their article, the authors question the view that once adequate deep, elaborate, and organizational processes have been induced incidentally, the intention to learn adds nothing further to the level of subsequent retention. Opposing this view, Popov and Dames conclude that intention to learn is always necessary for good memory performance and support this claim with the results of 11 experiments in which they find strong effects of intentionality using mixed-list designs in which all items are processed semantically but only half need be remembered later. The present commentary suggests that intentionality leads to selectively greater amounts of item processing and organizational processing of the to-be-remembered items in mixed lists, and that these further operations result in higher levels of recall. In light of this interpretation, the commentary argues for the validity of the original conclusion that retention is determined by the qualitative type of processing carried out on the items to be remembered, however that processing is induced. The commentary concludes by discussing various factors that modulate the effect of intentionality on memory and learning, and by suggesting a scheme that may aid the understanding of these effects, and serve as a framework for future studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.047 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".