The Effect of Categorisation on Sensitivity to Second-Order Relations in Novel Objects
Bibliographic record
Abstract
Adults appear to be more sensitive to configural information, including second-order relations (the spacing of features), in faces than in other objects. Superior processing of second-order relations in faces may arise from our experience of identifying faces at the individual level of categorisation (eg Bob versus John) but other objects at the basic level of categorisation (eg table versus chair; Gauthier and Tarr, 1997 Vision Research 37 1673- 1682). We simulated this learning difference with novel stimuli (comprised of blobs) by having two groups view the same stimuli but learn to identify the objects only at the basic level (based on the number of constituent blobs) or at both the basic level and individual level (based on the spacing, or second-order relations, of the blobs) of categorisation. Results from two experiments showed that, after training, observers in the individual-level training group were more sensitive to the second-order relations in novel exemplars of the learned category than observers in the basic-level training group. This is the first demonstration of specific improvement in sensitivity to second-order relations after training with non-face stimuli. The findings are consistent with the hypothesis that adults are more sensitive to second-order relations in faces than in other objects, at least in part, because they have more experience identifying faces at the individual level of categorisation.
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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 | 0.006 |
| 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 | 0.001 |
| 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".