Higher dopamine D1 receptor expression in prefrontal parvalbumin neurons underlies higher distractibility in marmosets versus macaques
Bibliographic record
Abstract
Marmosets and macaques are common nonhuman primate models of cognition, yet marmosets appear more distractible and perform worse in cognitive tasks. The dorsolateral prefrontal cortex (dlPFC) is pivotal for sustained attention, and research in macaques suggests that dopaminergic modulation and inhibitory parvalbumin (PV) neurons could influence distractor resistance. Here we compare the two species using a visual fixation task with distractors, perform molecular and anatomical analyses in dlPFC, and link functional microcircuitry with cognitive performance using computational modeling. We show that marmosets are more distractible than macaques, and that marmoset dlPFC PV neurons contain higher levels of dopamine D1 receptor (D1R) transcripts and protein, similar to levels in mice. Our modeling indicates that higher D1R expression in marmoset dlPFC PV neurons may increase distractibility by making dlPFC microcircuits more vulnerable to disruptions of their task-related persistent activity, especially when dopamine is released in dlPFC in response to unexpected salient stimuli. Integrative anatomical, molecular, behavioral, and modeling evidence suggests that higher dopamine D1 receptor expression in prefrontal cortical parvalbumin neurons in marmosets likely contributes to their higher distractibility relative to macaques.
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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".