Chunking in Visual Working Memory Implicates Long-Term Memory Representations
Bibliographic record
Abstract
Long-term learning may be leveraged by short-term memory stores in a process called chunking (Miller, 1959). Understanding how disparate visual working memory (VWM) representations are changed with chunking may help explain our rich subjective experience of VWM in the real world, in contrast to the stark capacity limitations observed in lab settings. In this thesis, I first tested how chunking interacts with proactive interference, one of the hallmarks of long-term memory (LTM) involvement. In my task, chunking still afforded an increase in accuracy, but I also observed an increased propensity to make lure selection errors when probed on swapping pairs, a signature of building proactive interference from LTM (Experiments 1-3). This lure selection was significantly different for swapping pairs and low-probability pairs based on explicit awareness of the pairings. I then explored how this offloading of VWM representations to LTM effects performance in a visual search task. In Experiment 4, I found that high-probability colour pairings, by their regularity alone, did not capture attention. There were no significant differences in response times when the high-probability pairs were on search targets compared to distractors. Experiment 5 revealed that maintained pairs can guide attention when participants are actively maintaining a pairing after a retro cue. Interestingly, unaware participants, who were less accurate on the memory task, were significantly faster when the pair was on a target, compared to a distractor, while chunking participants, were not. This replicates when participants are chunking but is not an essential difference between participants (Experiment 6). Lastly, behavioral evidence from experiment 7 again supports LTM contributions to chunking. An electrophysiological study focused on VWM markers of load, could however, not fully index a handoff of VWM to LTM. Overall, chunking in VWM recruits the systems of LTM, which makes chunked representations vulnerable to proactive interference, but also shields chunking participants from incidental capture of irrelevant colour stimuli in a visual search task. This work expands our understanding of the interactions between VWM and LTM and how that effects other aspects of cognition.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".