16p11.2 duplication shows early male-biased impacts on reward learning, but NMDA receptor antagonism reduces optimal choice selection in both wildtypes and 16p11.2 duplication
Bibliographic record
Abstract
Rationale: 16p11.2 duplication is associated with numerous neuropsychiatric conditions at a genome-wide level, including psychosis. Mice modeling 16p11.2 duplication may provide important insights into cognitive risk factors, in particular in reward-guided decision making. NMDAR function has also been implicated in psychosis phenotypes, but whether these phenotypes differ by genetic risk factor is unknown. Objectives: We aimed to: 1) identify sex and genotype differences in early operant training and two-arm spatial restless bandit task performance; 2) examine the effects of an NMDAR antagonist on task performance and strategy across genotypes. Methods: 16p11.2 duplication and wildtype mice completed a series of training schedules of escalating difficulty followed by bandit tasks. MK-801 and saline were administered in alternating sessions prior to later bandit task performance. Results: Large sex differences in early operant training revealed some male-biased impacts of 16p11.2 duplication, contingent on training schedule difficulty. Once on the two-arm spatial restless bandit task, 16p11.2 duplication was no longer a strong contributor to decision making. However, MK-801 decreased the tendency to stay with a rewarded choice, lowered the probability of selecting the highest rewarded option, and decreased the influence of prior outcomes on choice. Conclusions: The male-biased vulnerability in early operant training suggests that strategies for learning early schemas or in novel environments may be impacted by 16p11.2 duplication in males. In contrast, NMDAR are influential in the ability to flexibly switch between choices, and disrupting this function significantly impairs decision making in all animals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".