What to do with Low-Priority Items: an ERP study of Resources Allocation in Visual Working Memory
Bibliographic record
Abstract
Visual working memory (VWM) is a limited resource, which may be distributed discretely or continuously, as predicted by opposing theoretical models. When presented with distracting information, it is most efficient to ignore or minimally process it. However, in many situations some items are more relevant than others and the target/distractor distinction is less clear. In a discrete resource allocation model, distractor items should be ignored completely to process target items in the limited slots of memory. In contrast, in the continuous model, distractors may be processed minimally with preference given to target items. One event-related potential (ERP) associated with VWM is sustained posterior contralateral negativity (SPCN), which is typically shown to scale with increasing load. In the present study, we used the SPCN to determine the extent to which low-priority items are processed. Participants were presented with four lateralized coloured objects while recording ERPs in three cue conditions: one-cue with 100% validity (no priority to non-target items), one-cue with 50% validity (low-priority to non-target items), and four-cues with 100% validity (all items given priority). In the 50% valid condition any of the uncued items could be probed; thus, these items should be allocated a portion of VWM resources in order to report the colour correctly. Results demonstrate that in the low-priority condition the SPCN amplitude was between the one-cue and four-cue conditions; thus, these items are not processed as targets (as in the four-cue condition) or ignored (as in the one-cue condition), but are meaningfully processed according to their priority. Further, frontal markers indicate that this condition required more executive control to preferentially maintain target items while meaningfully holding the low-priority items in memory. These results suggest that ERP markers of VWM maintenance reflect minimal but meaningful processing of low-priority items, as predicted by a continuous resource model. Meeting abstract presented at VSS 2016
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| 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".