Individual and sex differences in frontloading behavior and approach- avoidance conflict preference predict addiction-like ethanol seeking in rats
Bibliographic record
Abstract
Recent research has identified sex-dependent links between risk taking behaviors, approach-avoidance bias and alcohol intake. However, preclinical studies have typically assessed alcohol drinking using a singular dimension of intake (i.e. drinking level), failing to capture the multidimensional pattern of aberrant alcohol-seeking observed in alcohol use disorder. In this study, we sought to further explore individual and sex differences in the relationship between approach-avoidance bias, frontloading (bingeing and onset skew) and multiple addiction-like indices of ethanol seeking that included motivation for ethanol, persistence despite its absence (extinction), and ethanol-taking in the face of mild footshock. We found that female rats displayed more addiction-like phenotypes than males overall, and that frontloading patterns differed by sex, with females outdrinking males in the early part of access sessions (bingeing), but males strongly concentrating their lever pressing for ethanol in that period (onset skew). Multiple regression analyses revealed that bingeing was a strong positive predictor and onset skew a negative predictor of motivational breakpoint. Cued-conflict preference - a measure of approach-avoidance bias towards a mixed-valence conflict cue - was predictive of both extinction and footshock in males, but not females. Our data highlight key sex differences, and the relevance of both frontloading patterns and conflict preference in predicting future addiction-like phenotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".