Haptic Pictures: Fit Judgments Predict Identification, Recognition Memory, and Confidence
Bibliographic record
Abstract
DAngiulli et al (1998 Scandinavian Journal of Psychology 39 187-190) found blind and sighted (blindfolded) children identified common objects in raised-outline drawings explored haptically, and corrected themselves without feedback. The self-correction suggests that participants can assess the extent to which the referents they suggest as possible identifications fit the haptic pictures. Indeed, when we asked subjects to identify haptic pictures, and to judge how well the referents they mentioned fitted the pictures, their fit judgments predicted the accuracy of their suggestions. Also, when one group of subjects offered the suggestions and another group assessed the fit of the suggestions to the pictures, the fit judgments predicted the accuracy of the suggestions. Further, good fit predicted successful recognition memory. In addition, both high and low fit judgments were made confidently, so the range of confidence judgments was smaller than the range of fit judgments. Finally, visual judgments of fit by one group predicted the level of success of the suggestions from another (haptic) group. In sum, subjects assess their suggested identifications appropriately, most likely on the basis of object shape criteria, outlined surface edges, and use of a vantage point.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".