Dynamic Representations in Visual Working Memory
Bibliographic record
Abstract
Despite people’s subjectively rich visual experiences, the amount of information they can actively represent in their minds at a given time is severely limited by the capacity of visual working memory (VWM). To characterize this cognitive bottleneck, past studies have primarily employed static visual stimuli and, therefore, it is not yet clear how VWM represents dynamically changing visual information. Previous research suggests that VWM might utilize two distinct mechanisms to maintain an active representation of a changing stimulus: When a stimulus goes through a continuous (e.g., gradual) change, VWM keeps up with the change by updating its existing representation of the original stimulus. When a stimulus goes through a discontinuous (e.g., sudden) change, VWM resets its content by first discarding its original representation of the stimulus and then re-encoding a new representation. To test this hypothesis, we measured an electrophysiological correlate of VWM load (the contralateral delay activity or CDA) while participants tracked the characterizing identity (e.g., shape, color) of a dynamically changing stimulus. Here, we predicted that 1) the CDA amplitude remains sustained when a target object goes through a continuous identity change and 2) the CDA amplitude reduces to zero shortly after a target object goes through a discontinuous identity change. Our experiments confirmed both of our hypotheses when the stimulus went through dynamic shape or color changes. Taken together, our findings provide support for the existence of two distinct mechanisms through which VWM keeps track of dynamically changing visual information; updating and resetting.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".