A novel robotic reaching task to advance the assessment of approach-avoidance tendencies through kinematic analysis
Bibliographic record
Abstract
The investigation of approach-avoidance tendencies has traditionally relied on computer-based technologies that primarily characterise human behaviour through reaction times. However, these technologies are unable to accurately quantify other kinematic variables such as hand speed and movement direction. To address these limitations, novel robotic devices have been developed, providing more diverse and accurate quantitative assessment of human behaviour. This technical report presents an adaptation of the approach-avoidance task on the Kinarm, a robotic platform designed to track upper limb movements as participants interact with a virtual environment. This variant of the approach-avoidance task assesses the movement of both arms in twelve directions of reach. In addition, resistive loads can be applied to investigate the role of physical effort in approach-avoidance tendencies or to support rehabilitation protocols. Data and analyses from a pilot sample (n = 5) highlights the capabilities of the Kinarm Approach-Avoidance Task (KAAT).
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".