Finding maximal and minimal elements in a set is capacity-unlimited and massively-parallel
Bibliographic record
Abstract
Traditional accounts of working memory are divided into two irreconcilable camps: memory is either thought to be capacity-limited to 3 – 4 items, or to be resource-limited as the number of items grows. Here, we show that certain computations – namely identifying the maximal and minimal element along a dimension such as length – is neither capacity- nor resource-limited: i.e., finding the maximal or minimal element in a scene is automatic, efficient, and effortless. In three separate experiments, observers are shown 5 – 7 colored lines on the screen (e.g., a blue, a yellow, a green, a purple, a red, a black, a white) for 1200 milliseconds. In Experiment 1 (N = 40), participants are asked to either perform a pairwise comparison (e.g., "Is the blue line longer than the yellow line?") or a maximal comparison (e.g., "Is the blue line the longest?"), thereby requiring them to attend, remember, and compare all seven lines. We find that the maximal comparison is faster and more accurate that the pairwise comparison, even though the computation requires the representation of all lines (Fig1). In Experiment 2 (N = 80), participants identify the color of a particular line in the sequence (e.g., "Which line is the second longest?"). We find a pronounced advantage in accuracy and RT for identifying the longest and shortest lines, with an increasing, serial cost to each successive line in the sequence (Fig2). Finally, we replicate these effects developmentally and find that the identification of the maximal element is easier compared to pairwise from at least age 2 onward. These results suggest that the computations supporting the identification of the maximal element are distinct and more efficient than those supporting the identification and comparison of only two items, providing a challenge to traditional views of working memory capacity limits. Meeting abstract presented at VSS 2017
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".