The role of articulatory suppression in immediate false recognition
Bibliographic record
Abstract
False memory for critical lures has been widely documented in long-term memory using the Deese/Roediger-McDermott paradigm. Recent evidence suggests that false memory effects can also be found in short-term memory (STM), supporting models that assume a strong relationship between short-term and long-term memory processes. However, no study has examined the role of articulatory suppression on immediate false memory, even though phono-articulatory factors are critically involved in STM performance and are an intrinsic part of all STM accounts. The current study proposes a novel paradigm to assess false memory effects in a STM task under both silent and articulatory suppression conditions. Using immediate serial recognition, in which participants had to judge whether two successive mixed lists of six associated and non-associated words were matched, we examined true recognition of matching lists and false recognition of mismatching lists comprising a critical lure or unrelated distractor in two experiments. Results from both experiments indicated reduced true recognition of matching lists and greater false serial recognition of mismatching lists comprising a critical lure under articulatory suppression relative to silence. These findings provide further support for some current models of verbal short-term memory, which posit a strong relationship between short-term and long-term memory processes.
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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 | 0.007 |
| 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) | 0.001 | 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".