Visual working memory of multiple preferred objects
Bibliographic record
Abstract
A key debate regarding visual working memory (VWM) mechanisms focuses on the differences between discrete- versus continuous-resource models of VWM capacity limits. Recent findings have demonstrated that VWM resources can be allocated disproportionately according to the probability that an item would be probed, consistent with the continuous resource model. However, this finding was based on a single report on each trial, with the assumption that all items in the display would get the predicted quantity of VWM resources. The current study sought to address this methodological limitation, and determine whether multiple items are reported according to attentional prioritization during encoding. Using a two-item report task we tested the flexibility and quality of VWM when two items are cued during the encoding of a super-capacity display of six coloured items. To establish attentional priority, the cued items were probed on 50% of the trials, while in 25% of trials one cued item and one uncued item were reported, and two uncued items were reported on the remaining 25% of trials. Measures of precision, guess rate, and rate of non-target errors were taken from the three-component mixture model. Results show that participants reported the two cued items with approximately equal precision to each other, suggesting that in fact multiple items can be prioritized simultaneously. Uncued items were also half as likely to be correctly reported and three times as likely to be misremembered compared with cued items. The results are in line with the predictions of a flexible resource model in which VWM resources can be allocated across multiple items in accordance with the task demands. Meeting abstract presented at VSS 2017
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".