Immunometabolic state modulation of sequential decision making in patch-foraging mice
Bibliographic record
Abstract
Summary Animals have evolved sophisticated behavioural and metabolic adaptations to respond to threats to homeostasis, including resource scarcity and infectious pathogens. Energy deficits associated with lack of food availability and sickness-associated anorexia elicit distinctive hypometabolic states, however how such states are integrated with higher-order cognition is largely unknown. Patch-foraging paradigms have proven useful for deciphering evolutionarily conserved and ethologically grounded insights into cost-benefit decision-making as animals continually deliberate between exploiting and exploring their environment. We developed and extensively validated a touchscreen-based patch-foraging task for mice in which food reward dynamically varied across trials, in a dataset comprising over 111,000 sequential decisions from 35 adult male mice. Contrary to predictions that emphasize the impact of inflammation to blunt effortful reward-driven behaviour, our results demonstrate that systemic lipopolysaccharides-induced inflammation promotes hyper-exploitation by attenuating exploratory choice behaviour in animals interacting with complex food environments. Such behaviour can be seen as a bias towards immediate outcomes, with impulsivity as a feature affecting the weighting of temporal factors. Given the ubiquity of systemic inflammation in numerous infectious, metabolic and psychiatric disorders featuring dysfunctional value- and cost-sensitive behaviour, these results provide insight into how immunometabolic states are linked to altered decision-making.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".