Selection in working memory is resource-demanding: Concurrent task effects on the retro-cue effect
Bibliographic record
Abstract
In a retro-cue paradigm, after memorizing a set of objects, people are cued to remember only a subset. Improved memory from the retro-cue suggests that selection processes can benefit items stored in working memory. Does selection in working memory require attention? If so, an attention-demanding task should disrupt retro-cue effects. Studies using a dual-task paradigm have found mixed results, with only one study (Janczyk & Berryhill, Attention, Perception, and Psychophysics, 76 (3), 715-724, 2014) showing a decreased retro-cue effect by a secondary task. Here we explore a potential issue in that study - the temporal overlap of the secondary task response with the memory test presentation. This raises questions about whether the secondary task was impairing selection processes in memory or was impacting the memory response. We replicated their paradigm by inserting a tone discrimination task at the retro-cue offset, but we also included a condition in which the tone task and the memory test were temporally separated. In Experiment 1, performing the tone task did not impair the retro-cue effect. In Experiment 2, we added an articulatory suppression task as in Janczyk and Berryhill's study, and we found that the requirement to execute the tone task impaired retro-cue effects. This impairment was independent of whether the tone and memory tasks overlapped. These findings suggest that internal prioritization can be impaired by dual-task interference, but may only occur when such interference is robust enough, for example, due to switching between multiple tasks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".