The contribution of motion detectors during multiple-object tracking
Bibliographic record
Abstract
Motion perception, an essential skill for human beings, relies on two motion processing systems: a low-level system mediated by early direction-selective neurons known as motion detectors, and a high-level system that attentively tracks the position of objects. The current study aimed to investigate the contribution of motion detectors during multiple-object tracking through two experimental manipulations: reverse-phi and stroboscopic motion. By reversing the contrast polarity at each object displacement, reverse-phi inverses the direction response of motion detectors. By introducing a temporal gap between each displacement (i.e., object disappears briefly before reappearing at a new position), stroboscopic motion can weaken the contribution of motion detectors. Five young participants were asked to track and identify four balls among eight identical ones bouncing around within a virtual three-dimensional cube. The maximum speed threshold at which the 4 target balls were successfully tracked were determined under fourteen conditions: 2 contrast polarities (i.e., reversed or not) X 7 temporal gaps (i.e., 0, 17, 33, 50, 67, 83, or 100 msec). A significant interaction between contrast polarity and temporal gap on maximum speed threshold was found (F(6, 24)=9.194, p<.001). Post hoc analyses revealed that with temporal gaps of 0 and 17 msec, the maximum speed threshold was significantly lower when the contrast polarity of the balls was reversed compared to when it was not (p<.05). With longer temporal gaps, however, no significant difference was observed between the two contrast polarity conditions. The results suggest that motion detectors considerably contribute to multiple-object tracking when the temporal gaps between object displacements are short (<33 msec). But for longer temporal gaps, no considerable contribution of motion detectors was observed suggesting that multiple-object tracking relied mainly on the higher-level, attention-based motion processing system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".