The irrelevant speech effect in backward recall is modulated by foreknowledge of recall direction and response modality.
Bibliographic record
Abstract
In backward immediate serial recall, participants recall lists of items immediately after their presentation by beginning with the last presented item and ending with the first presented one. Despite the similarities with forward recall in which participants recall the items from the first to the last presented, benchmark memory phenomena reliably found in forward recall are not constantly observed in backward recall. Here, we proposed a new framework called the encoding-retrieval matching (ERM) hypothesis to account for backward recall. The ERM retains the main features of the visuospatial hypothesis and the item-order trade-off hypothesis, the two dominant accounts of backward recall. According to the ERM, output modality and foreknowledge of recall direction influence the availability of visuospatial representations and the weight devoted to item and order processing. We tested the ERM with irrelevant speech, a well-known working memory factor disrupting forward recall. In two experiments, we manipulated recall direction (forward vs. backward), irrelevant speech (control vs. irrelevant speech), and response modality (manual vs. oral). As predicted by the ERM, when recall direction was unpredictable in Experiment 1, the magnitude of the irrelevant speech effect was larger in backward manual recall than in backward oral recall. In Experiment 2, recall direction was predictable. As predicted by the ERM, in backward recall, the irrelevant speech effect was reduced with a manual response and absent with an oral response. We concluded that ERM effectively accounts for the complex interplay between response modality, foreknowledge of recall direction, and benchmark memory effects in backward recall. (PsycInfo Database Record (c) 2021 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".