Learning and Generalization in Haptic Classification of 2-D Raised-Line Drawings of Facial Expressions of Emotion by Sighted and Adventitiously Blind Observers
Bibliographic record
Abstract
Sighted blindfolded individuals can successfully classify basic facial expressions of emotion (FEEs) by manually exploring simple 2-D raised-line drawings (Lederman et al 2008, IEEE Transactions on Haptics 1 27-38). The effect of training on classification accuracy was assessed by sixty sighted blindfolded participants (experiment 1) and by three adventitiously blind participants (experiment 2). We further investigated whether the underlying learning process(es) constituted token-specific learning and/or generalization. A hybrid learning paradigm comprising pre/post and old/new test comparisons was used. For both participant groups, classification accuracy for old (ie trained) drawings markedly increased over study trials (mean improvement --76%, and 88%, respectively). Additionally, RT decreased by a mean of 30% for the sighted, and 31% for the adventitiously blind. Learning was mostly token-specific, but some generalization was also observed for both groups. The sighted classified novel drawings of all six FEEs faster with training (mean RT decrease = 20%). Accuracy also improved significantly (mean improvement = 20%), but this improvement was restricted to two FEEs (anger and sadness). Two of three adventitiously blind participants classified new drawings more accurately (mean improvement = 30%); however, RTs for this group did not reflect generalization. Based on a limited number of blind subjects, our results tentatively suggest that adventitiously blind individuals learn to haptically classify FEEs as well as, or even better than, sighted persons.
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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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".