Ketamine disrupts gaze patterns during face viewing in the common marmoset
Bibliographic record
Abstract
Abstract Faces are stimuli of critical importance for primates. The common marmoset ( Callithrix jacchus ) is a promising model for investigations of face processing, as this species possesses oculomotor and face processing networks resembling those of macaques and humans. Face processing is often disrupted in neuropsychiatric conditions such as schizophrenia (SZ) and thus it is important to recapitulate underlying circuitry dysfunction preclinically. The N-Methyl-D-aspartate (NMDA) non-competitive antagonist ketamine has been used extensively to model the cognitive symptoms of SZ. Here, we investigated the effects of a subanesthetic dose of ketamine on oculomotor behaviour in marmosets during face viewing. Four marmosets received systemic ketamine or saline injections while viewing phase-scrambled or intact videos of conspecifics’ faces. To evaluate effects of ketamine on scan paths during face viewing, we identified regions of interest in each face video, and classified locations of saccade onsets and landing positions within these areas. A preference for the snout over eye regions was observed following ketamine administration. In addition, regions in which saccades landed could be significantly predicted by saccade onset region in the saline but not the ketamine condition. No significant drug effects were observed for phase-scrambled videos. Effects on saccade control were limited to a reduction in saccade amplitudes during viewing of scrambled videos. Thus, ketamine induced a significant disruption of scan paths during viewing of conspecific faces but limited effects on saccade motor control. These findings support the use of ketamine in marmosets for investigating changes in neural circuits underlying social cognition in neuropsychiatric disorders.
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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 | 0.000 |
| 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) | 0.001 | 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".