Chunking in Visual Working Memory changes the Guidance of Attention in a Visual Search
Bibliographic record
Abstract
Pairing two colors regularly across displays enables them to be accessed by visual working memory (VWM) more efficiently in a process often called chunking (Brady et al., 2009). Previous investigations have revealed that this VWM advantage may be due to the contribution of explicit LTM (Huang & Awh, 2018; Ngiam et al., 2019). Simultaneously, colors actively maintained in VWM guide the deployment of attention (van Moorselar, 2014) separately from representations maintained in LTM (Carlisle et al., 2011). We asked if the representational changes that underlie chunking also impact how those VWM representations guide attention. Using an incidental capture design, we presented participants with four reliable (high-probability) color pairs across 10 blocks of VWM tests or visual search trials. Attentional guidance of the maintained colors was calculated as the difference in reaction time as a function of the distance between the search target and the maintained color pair. Across three experiments, we found that participants with full explicit awareness of the color pairings were significantly more accurate than unaware participants in the VWM task (F (1, 266) = 22.93, p < 0.001), replicating Ngiam et al. (2019). Surprisingly, aware participants were significantly less guided toward those high-probability pairs in the search task when compared to unaware participants (F (1, 36) = 4.77, p < 0.05). This slowing is attributable to chunking, as it is limited to aware participants maintaining high-probability pairs. There is no difference between aware and unaware participants for low-probability pairs (F (1, 32) = 0.187, p = 0.67) or search displays with single colors (F (1, 42) = 1.207, p = 0.28). Overall, participants who leveraged chunked representations to improve in the VWM task show slowed attentional capture of maintained colors during the visual search task.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".