The Effects of Divided Attention on Encoding and Retrieval Processes: The Resiliency of Retrieval Processes
Bibliographic record
Abstract
We have recently cast doubt (Craik, Govoni, Naveh-Benjamin, & Anderson, 1996; Naveh-Benjamin, Craik, Guez, & Dori, 1998) on the view that encoding and retrieval processes in human memory are similar. Divided attention at encoding was shown to reduce memory performance significantly, whereas divided attention at retrieval affected memory performance only minimally. In this article we examined this asymmetry further by using more difficult retrieval tasks, which require substantial effort. In one experiment, subjects had to encode and retrieve lists of unfamiliar name-nouns combinations attached to people's photographs, and in the other, subjects had to encode words that were either strong or weak associates of the cues presented with them and then to retrieve those words with either intra- or extra-list cues. The results of both experiments showed that unlike division of attention at encoding, which reduces memory performance markedly, division of attention at retrieval has almost no effect on memory performance, but was accompanied by an increase in secondary-task cost. Such findings again illustrated the resiliency of retrieval processes to manipulations involving the withdrawal of attention. We contend that retrieval processes are obligatory or protected, but that they require attentional resources for their execution.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".