The Effect of a Working Memory Load on the Intention‐Superiority Effect: Examining Three Features of Automaticity
Bibliographic record
Abstract
Summary The intention‐superiority effect refers to the finding that intentions are more accessible than other memory contents. Our primary goal was to test for automatic processing in this effect, testing three features of automaticity: unintentionality, effortlessness, and lack of awareness. We used a postponed‐intention paradigm with short action scripts. The intention‐superiority effect was defined as greater accessibility in a lexical decision task (LDT) for words from to‐be‐performed scripts than to‐be‐remembered scripts. Working memory load was experimentally manipulated to assess automatic processing. A general intention‐superiority effect was found, demonstrating the automatic feature of unintentionality, and it was not diminished by a high load, demonstrating the automatic feature of effortlessness. Also, participants who reported that they lacked awareness of the link between the LDT and encoded scripts showed a larger intention‐superiority effect than participants who were aware. Therefore, this study demonstrated an implicit intention‐superiority effect, which was actually larger than the explicit effect. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".