The Impact of Visual Working Memory Chunking on Visual Search
Bibliographic record
Abstract
The deployment of attention can be biased by temporal regularities in the environment (Zhao et al., 2013) and representations stored in visual working memory (VWM) (Moorselar et al., 2014). Similarly, regularities in the environment can also facilitate effective storage in VWM, meaning items can be stored more effectively due to chunking (Brady et al., 2009). We investigated how VWM representations of chunked and un-chunked colour pairs impact performance on a visual search task. Participants were presented with VWM displays consisting of three pairs of colours and asked to remember all the colours until presented with a four alternative forced choice for what colour was just presented at the cued location. Colour pairs were selected each trial using a joint-probability matrix, such that each participant was assigned four high-probability pairs to chunk. High-probability pairs were 80 times more likely to be presented than any low-probability counterpart. During the delay period of the VWM task, participants saw a visual search display and were asked to find the coloured diamond with a chip on the top or bottom, among five coloured distractors with chips on the left or right. Across two experiments, evidence of VWM chunking was observed through improvements in VWM accuracy for participants who post-hoc reported being aware of the high-probability colour pairings (F(1, 29) = 12.56, p < 0.001). These chunks, however, did not significantly guide attention during the intervening search task (F(1, 29) = 0.44, p = 0.51). Our experiments demonstrate that under VWM load, chunks exert no significant bias on visual search, despite sharing the qualities of regularity and maintenance in VWM. This provides indirect evidence that the benefits of VWM chunking may be subserved by long-term memory processes.
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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 | 0.018 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".