Implementation of a neurophysiological model of saccadic eye movements on an anthropomorphic robotic head
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we investigated the relevance of a robotic implementation in the development and validation of a neurophysiological model of the generation of saccadic eye movements. To this aim, a well-characterized model of the brainstem saccadic circuitry was implemented on a humanoid robot head with 7 degrees of freedom (DOFs), which was designed to mimic the human head in terms of the physical dimensions (i.e. geometry and masses), the kinematics (i.e. number of DOFs and ranges of motion), the dynamics (i.e. velocities and accelerations), and the functionality (i.e. the ocular movements of vergence, smooth pursuit and saccades). Our implementation makes the robot head execute saccadic eye movements upon a visual stimulus appearing in the periphery of the robot visual field, by reproducing the following steps: projection or the camera images onto collicular images, according to the modeled mapping between the retina and the superior colliculus (SC); transformation of the retinotopic coordinates of the stimulus obtained in the camera reference frame into their corresponding projections on the SC; spatio-temporal transformation of these coordinates according to what is known to happen in the brainstem saccade burst generator of primates; and execution of the eye movement by controlling one eye motor of the robot, in velocity. The capabilities of the robot head to execute saccadic movements have been tested with respect to the neurophysiological model implemented, in view of the use of this robotic implementation for validating and tuning the model itself, in further focused experimental trials
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.
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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it