Visual event boundaries trigger forgetting despite active maintenance in visual working memory
Bibliographic record
Abstract
The contents of visual perception are inherently dynamic-just as we experience objects in space, so too events in time. The boundaries between these events have downstream consequences. For example, memory for incidentally encountered items is impaired when walking through a doorway, perhaps because event boundaries serve as cues to clear obsolete information from previous events. Although this kind of "memory flushing" can be adaptive, work on visual working memory (VWM) has focused on the opposite function of active maintenance in the face of distraction. How do these two cognitive operations interact? In this study, observers watched animations in which they walked through three-dimensionally rendered rooms with picture frames on the walls. Within the frames, observers either saw images that they had to remember ("encoding") or recalled images they had seen in the immediately preceding frame ("test"). Half of the time, a doorway was crossed during the delay between encoding and test. Across experiments, there was a consistent memory decrement for the first image encoded in the doorway compared to the no-doorway condition while equating time elapsed, distance traveled, and distractibility of the doorway. This decrement despite top-down VWM efforts highlights the power of event boundaries to structure what and when we forget.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".