Replication Data for: The influence of hand depiction types on behavioural patterns in laterality judgments.
Bibliographic record
Abstract
According to the Motor Simulation Theory, cognitive states such as kinesthetic motor imagery activate the motor system in a similar way to overt motor execution. Action simulation involved in motor imagery can be implicitly triggered when individuals unconsciously simulate an action, as is the case in Hand Laterality Judgement Task (HLJT). Studies employing the HLJT often use various depictions of hands, which may potentially influence behavioural measures such as response times. The present study recruited 70 younger adults who mentally simulated both realistic and line drawing representations of hands using the HLJT. The results indicated that (1) mental transformations were quicker with line drawing depictions than with realistic hands, (2) faster response times were observed for the back of the hand compared to the palm, and (3) when comparing line drawings to real hands, quicker response times were noted for 0° and 90°L orientations. The results suggest that when compared to line drawings, realistic hands have slower response times for both simple (0°) and challenging (90°L) mental transformations. Overall, behavioural measures may vary between realistic hands and line drawings, underscoring the importance of considering this distinction when utilizing the HLJT.
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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 | 0.008 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.055 |
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".