From association to gist: Some critical tests.
Bibliographic record
Abstract
We report the first evidence that the gist mechanism of fuzzy-trace theory and the associative mechanism of activation monitoring theory operate in parallel, in the recall version of the Deese/Roediger/McDermott illusion. In three experiments, we implemented a new methodology that allows their respective empirical indexes, gist strength (GS) and backward associative strength (BAS), to each be manipulated while the other is held constant. In Experiment 1, increasing GS increased false recall of missing words, but increasing BAS did not. In Experiments 2 and 3, however, increasing GS and increasing BAS both increased recall of missing words, and those effects were independent and additive. In all three experiments, GS and BAS affected true recall of list words in qualitatively different ways: (a) Increasing GS always improved true recall, regardless of whether BAS was high or low, but (b) increasing BAS impaired true recall when GS was high and improved true recall when GS was low. To pinpoint the retrieval loci of the two variables' effects, we analyzed the data of all experiments with the dual-retrieval model. Those analyses showed that the variables' respective effects were due to different retrieval processes. (PsycInfo Database Record (c) 2024 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 | 0.018 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.002 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 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".