The Intention Interference Effect
Bibliographic record
Abstract
Intentions have been shown to be more accessible (e.g., more quickly and accurately recalled) compared to other sorts of to-be-remembered information; a result termed an intention superiority effect (Goschke & Kuhl, 1993). In the current study, we demonstrate an intention interference effect (IIE) in which color-naming performance in a Stroop task was slower for words belonging to an intention that participants had to remember to carry out (Do-the-Task condition) versus an intention that did not have to be executed (Ignore-the-Task condition). In previous work (e.g., Cohen et al., 2005), having a prospective intention in mind was confounded with carrying a memory load. In Experiment 1, we added a digit-retention task to control for effects of cognitive load. In Experiment 2, we eliminated the memory confound in a new way, by comparing intention-related and control words within each trial. Results from both Experiments 1 and 2 revealed an IIE suggesting that interference is very specific to the intention, not just to a memory load.
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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 | 0.003 | 0.022 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".