Visual-motor transformations at the Neuronal Level in the Gaze System
Bibliographic record
Abstract
The fundamental question in perceptual-motor integration is how, and at what level, do sensory signals become motor signals? Does this occur between brain areas, within brain areas, or even within individual neurons? Various training or cognitive paradigms have been combined with neurophysiology and/or neuroimaging to address this question, but the visuomotor transformations for ordinary gaze saccades remain elusive. To address these questions, we developed a method for fitting visual and motor response fields against various spatial models without any special training, based on trial-to-trial variations in behavior (DeSouza et al. 2011). More recently we used this to track visual-motor transformations through time. We find that superior colliculus and frontal eye field visual responses encode target direction, whereas their motor responses encode final gaze position relative to initial eye orientation (Sajad et al. 2015; Sadeh et al. 2016). This occurs both between neuron populations, but can also be observed within individual visuomotor cells. When a memory delay is imposed, a gradual transition of intermediate codes is observed (perhaps due to an imperfect memory loop), with a further 'leap' toward gaze motor coding in the final memory-motor transformation (Sajad et al. 2016). However, we found a similar spatiotemporal transition even within the brief burst of neural activity that accompanies a reactive, visually-evoked saccade. What these data suggest is that visuomotor transformations are a network phenomenon that is simultaneously observable at the level of individual neurons, and distributed across different neuronal populations and structures. Meeting abstract presented at VSS 2017
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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 | 0.000 |
| 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".