Biological motion elicits between-person Inhibition of Return in temporal and spatial movement parameters
Bibliographic record
Abstract
The present study was designed to gain a deeper understanding of the processing of biological motion stimuli. To this end, we investigated if the inhibition of return (IoR) effect emerges in initiation times and trajectories of pointing movements to targets in left and right space where the preceding cues were pointing movement of a human model or a dot with the same biological motion. Targets were randomly presented in the same or opposite side from the direction of the motion cue. It was hypothesised that the visuomotor system should resonate with the biological motion of a dot, but that the human model should exaggerate the effect. Thus, the human model should trigger stronger attention shifts compared with the dot model and lead to more robust IoR effects in both spatial (movement) and temporal parameters of the observer's pointing responses. Initiation times and the spatial parameters (angle of the hand trajectory) of the pointing movements were analysed. Results indicate that facilitation and IoR effects triggered by human and dot stimuli did not differ. Based on these findings, it seems that the crucial feature of motion cues that generate shifts in attention is biological motion, rather than human appearance per se.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
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".