Characterizing eye movement behaviors and kinematics of non-human primates during virtual navigation tasks
Bibliographic record
Abstract
Virtual environments (VE) allow testing complex behaviors in naturalistic settings by combining highly controlled visual stimuli with spatial navigation and other cognitive tasks. They also allow for the recording of eye movements using high-precision eye tracking techniques, which is important in electrophysiological studies examining the response properties of neurons in visual areas of nonhuman primates. However, during virtual navigation, the pattern of retinal stimulation can be highly dynamic which may influence eye movements. Here we examine whether and how eye movement patterns change as a function of dynamic visual stimulation during virtual navigation tasks, relative to standard oculomotor tasks. We trained two rhesus macaques to use a joystick to navigate in a VE to complete two tasks. To contrast VE behavior with classic measurements, the monkeys also performed a simple Cued Saccade task. We used a robust algorithm for rapid classification of saccades, fixations, and smooth pursuits. We then analyzed the kinematics of saccades during all tasks, and specifically during different phases of the VE tasks. We found that fixation to smooth pursuit ratios were smaller in VE tasks (4:5) compared to the Cued Saccade task (7:1), reflecting a more intensive use of smooth pursuit to foveate targets in VE than in a standard visually guided saccade task or during spontaneous fixations. Saccades made to rewarded targets (exploitation) tended to have increased peak velocities compared to saccades made to unrewarded objects (exploration). VE exploitation saccades were 6% slower than saccades to discrete targets in the Cued Saccade task. Virtual environments represent a technological advance in experimental design for nonhuman primates. Here we provide a framework to study the ways that eye movements change between and within static and dynamic displays.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".