Using affective ratings to test competing hypotheses about differences in active and accessory states in visual working memory.
Bibliographic record
Abstract
The multiple state theory of working memory suggests that representations held in working memory are separated into two states: a currently-relevant active representation and accessory memory items held for future use. While the characteristics and consequences of active versus accessory states have been the subject of several investigations, the exact neurocognitive mechanisms that move representations between the two states remain unclear. Of the two competing hypotheses, one suggests that inhibition is applied to keep a representation in an accessory state, while the other suggests that accessory representations simply receive less top-down cortical amplification than active representations, but are not subjected to inhibition. Here we capitalize on the different affective consequences for stimuli whose memory representations are subjected to inhibition (negative ratings) or active enhancement (positive ratings) to test these competing hypotheses. On each trial participants memorized four items and then were cued to focus on a single item within memory. They then completed either a visual search or an affective evaluation task. Search times were slower when a search distractor matched the colour of the active item but not when it matched the colour of the accessory item, replicating findings of a division in working memory whereby only active items guide attention. Also, accessory items were affectively devalued compared to baseline and active memory items. This finding of devaluation supports the hypothesis that inhibition is used to keep representations in an accessory state, and adds to past findings that similar mechanisms of attention and emotion govern prioritization in working memory and the prioritization of external stimuli. Meeting abstract presented at VSS 2018
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".