The double-drift illusion biases the marmoset oculomotor system
Bibliographic record
Abstract
The double-drift illusion has two unique characteristics: The error between the perceived and physical position of the stimulus grows over time, and saccades to the moving target land much closer to the physical than the perceived location. These results suggest that the perceptual and saccade targeting systems integrate visual information over different time scales. Functional imaging studies in humans have revealed several potential cortical areas of interest, including the prefrontal cortex. However, we currently lack an animal model to study the neural mechanisms of location perception that underlie the double-drift illusion. To fill this gap, we trained two marmoset monkeys to fixate and then saccade to the double-drift stimulus. In line with human observers for radial double-drift trajectories with fast internal motion, we find that saccade endpoints show a significant bias that is, nevertheless, smaller than the bias seen in human perceptual reports. This bias is modulated by changes in the external and internal speeds of the stimulus. These results demonstrate that the saccade targeting system of the marmoset monkey is influenced by the double-drift illusion.
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 | 0.002 |
| 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 | 0.001 |
| 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".