Characterization of human tactile pattern recognition performance at different ages
Bibliographic record
Abstract
This study examined tactile pattern recognition performance in human observers (N = 44) in the context of a letter recognition task at the fingertip. Participants were recruited from three different age groups (youth, n = 17; young adults, n = 14; seniors, n = 13) to examine age-related differences in performance. The influence of gender (males vs females) and hand (right vs left) was also examined. Performance was characterized in terms of both response accuracy and associated response times (RTs). Patterns of confusion between letters were also examined. Results showed that age was the most important factor in determining the capacity of our participants to perform fast and accurate pattern recognition. In this respect, younger participants (i.e., youth and young adults) clearly outperformed seniors by showing not only better accuracy and less confusion but also 2-3 times faster RT. By comparison, the combined influence of "hand" and "gender" on recognition performance was only marginal. These results indicate that the ability to perform complex tactile pattern recognition is already well established in youth 10-14 years of age with only minor refinements occurring later in early adulthood. With advancing age, such ability becomes far less efficient, as judged by the drastic increase in RT observed in seniors, in spite of a relatively good accuracy. This suggests that alterations not only at the peripheral receptor level but also at the central processing level might play an important role in limiting the ability of seniors to perform fast and efficient pattern recognition at the fingertip.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".