Memory without retrieval: Testing the direct-access account of the missing item task.
Bibliographic record
Abstract
In the missing item task, two short lists are presented. The test list contains all but one of the items from the study list in a new random order and the task is to report which item from the study list is missing. Murdock and Smith (2005) found that the time to correctly respond with the missing item was independent of the position of the missing item and was also independent of the list length. They argued that these data are difficult to accommodate by models that include a search process but are consistent with models that posit "direct access" such as the power set version of Theory of Distributed Associative Memory (TODAM). If direct access is occurring, redintegration cannot be occurring. Two experiments test the direct access account by determining whether two effects commonly ascribed to redintegration occur in the missing item task. Experiment 1 found a semantic relatedness effect and Experiment 2 found a word frequency effect. The presence of these effects is consistent with a redintegration account. Implications for TODAM and for an explanation based on the Feature Model are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".