Enhanced detection of visual stimuli projected on a tool
Bibliographic record
Abstract
The recruitment of bimodal visual-tactile neurons may explain the enhanced processing of visual stimuli near the hand. The present study investigated whether or not the ‘hand-related enhancement effect’ would extend to a novel tool after training. Participants (N=32) were asked to press a button as rapidly as possible with their right hand when they detected a target projected onto the surface of their left hand, a fake hand, or a tool. After the baseline session, all subjects were tested when holding the fake hand and tool with their left hand. The stimuli were presented on the hairy surface of the fake hand and the top surface of the tool. 16 subjects were then trained to use the fake hand (FH-group) and 16 to use the tool (Tool-group) to move a ball around complex path with their left hand. After training, all subjects were again tested when holding the tool and the fake hand. Finally, three baseline conditions were re-tested. We found that the participants initially responded faster to stimuli projected onto their real hand (307ms) than to stimuli presented on fake hand (318ms) or the tool (331ms), p[[lt]].004. After training, participants in the FH-group now responded faster to the target lights projected on the fake hand than they did before training (p=.004). Similarly, participants in the Tool-group showed the same pattern with the tool (p[[lt]].000). Finally, the FH-group (but not the Tool-group) responded faster to targets on the hairy as opposed to the glabrous surface of the fake hand (p=.005), even when they were not holding the fake hand, whereas the Tool-group (but not the FH-group) responded faster to targets on the top of the tool compared to the bottom (p=.029). These findings suggest that an enhancement effect can be induced in tools and other inanimate objects with training.
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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 | 0.001 |
| 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) | 0.003 | 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".