Spaced out: good discrimination but poor memory for spacing differences in houses
Bibliographic record
Abstract
Face recognition may differ from object recognition in being more affected by spacing between parts (e.g., distance between eyes) than by the features themselves. Here we examined adults' ability to discriminate and recognise houses in a design similar to that used previously with faces. Stimuli were photographs of houses in three conditions: houses that differed only in spacing between the windows and door; houses that differed only in features (the particular windows and door); and houses that were completely different. Subjects (N=24) completed a match-to-sample task followed by a 2AFC recognition memory task for each condition. The mean spatial frequency amplitude was matched across all sets. Overall, subjects were less accurate at the spacing task than the feature task (81% vs. 90%, t(23) = 4.45, p lt; .001 for discrimination; 47% vs. 89%, t(23) = 4.95, p lt; .001, for memory). Importantly, even for sets for which accuracy on the spacing and feature tasks were matched at the discrimination stage (81% vs. 84%, t(11) = 1.15, p [[gt]] .05), memory for previously seen spacing was at chance (50%, t(11) lt; 1), while memory for particular features remained high (85%, t(11) = 5.3, p [[lt]] .001). This contrasts with a previous study with faces in which adults remembered spacing information learned as part of another task at well above chance levels (Gilchrist & McKone, 2003). The current results for houses are consistent with different real-world processing demands for faces and houses. Facial features change as individuals talk, turn their head, or show facial expressions, and spacing information can be deduced relative to the same basic layout in every face. Houses, conversely, have constant features that can be distinctive and far fewer restrictions on basic structure (the door goes at the bottom but windows can go in any number of locations).
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".