Dissociating feature complexity from number of objects in VSTM storage using the contralateral delay activity
Bibliographic record
Abstract
Many recent studies have examined the neural correlates of visual short-term memory (VSTM) maintenance using an ERP component known as the contralateral delay activity (CDA), whose amplitude corresponds to memory load within individuals and to memory capacity across individuals. The parietal distribution of the CDA makes it a particularly compelling locus of capacity-limited VSTM storage given that it overlaps with fMRI findings of feature- and location-based VSTM systems located in the superior and inferior intra-parietal sulcus. An under-explored question, however, is the extent to which the CDA indexes the feature complexity of items to be remembered or the number of objects/locations to be remembered, or both. We employed a lateralized change detection task in which the feature complexity and number of items to be remembered were independently manipulated. Items to be remembered were either simple features (shape, color, or orientation) or conjunctions of these features, and they were presented either at one location or at three locations. Behavioural results demonstrated that individuals performed comparably for simple features and conjunctions presented one at a time, while performance for simple features declined when three were presented at different locations relative to when they were conjoined in one object. We found that ERP amplitudes at the lateral, posterior sites that are typically measured in the CDA reflected the number of objects to be remembered, while more central, anterior sites indexed the complexity of the objects to be remembered. Thus, feature- and location-based systems in the parietal cortex can be dissociated even at the course spatial resolution of ERP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".