The role of object properties in item individuation: The effects of item heterogeneity and change
Bibliographic record
Abstract
Item individuation is the ability to consider an item as an individual, and it is necessary if a person is to attend, foveate, or touch a specific item among others. At present it is unclear what role (if any) object properties play in item individuation. One way to investigate individuation is to use the visual enumeration task. Enumeration (determining how many items there are) requires individuation because accurate response requires that each item be considered once and only once. It has long been known that there are differences between enumerating small and large numbers of items. Specifically, when there are 1–4 items, a rapid (40–100 ms/item), accurate, effortless, process called subitizing is used. In contrast, when there are larger numbers of items, a slow (200–350 ms/item), effortful, error-prone process called counting is employed. In a series of studies, the role of object properties in individuation was evaluated by having participants enumerate 1–9 items of various types. In the first, participants enumerated items that were either homogeneous or heterogeneous in their properties. In the second, participants enumerated 1–9 items, but the items constantly changed their properties, their position, or both while being enumerated. Item heterogeneity and property change have different effects depending on the number of items. This effect was interpreted as it relates to recent theories of visual-spatial enumeration and Pylyshyn's (1989) FINST hypothesis.
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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 | 0.007 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".