Location memory for dots in polygons versus cities in regions: Evaluating the category adjustment model.
Bibliographic record
Abstract
We conducted 3 experiments to examine the category adjustment model (Huttenlocher, Hedges, & Duncan, 1991) in circumstances in which the category boundaries were irregular schematized polygons made from outlines of maps. For the first time, accuracy was tested when only perceptual and/or existing long-term memory information about identical locations was cued. Participants from Alberta, Canada and California received 1 of 3 conditions: dots-only, in which a dot appeared within the polygon, and after a 4-s dynamic mask the empty polygon appeared and the participant indicated where the dot had been; dots-and-names, in which participants were told that the first polygon represented Alberta/California and that each dot was in the correct location for the city whose name appeared outside the polygon; and names-only, in which there was no first polygon, and participants clicked on the city locations from extant memory alone. Location recall in the dots-only and dots-and-names conditions did not differ from each other and had small but significant directional errors that pointed away from the centroids of the polygons. In contrast, the names-only condition had large and significant directional errors that pointed toward the centroids. Experiments 2 and 3 eliminated the distribution of stimuli and overall screen position as causal factors. The data suggest that in the "classic" category adjustment paradigm, it is difficult to determine a priori when Bayesian cue combination is applicable, making Bayesian analysis less useful as a theoretical approach to location estimation.
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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 | 0.014 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".