Investigating the influence of ‘losses disguised as wins’ on decision making and motivation in rats
Bibliographic record
Abstract
Multiline slot machines encourage continued play through 'losses disguised as wins' (LDWs), outcomes in which the money returned is less than that wagered. Individuals with gambling problems may be susceptible to this game feature. The cognitive and neurobiological mechanisms through which LDWs act are unknown. In a novel rat operant task, animals chose between a 'certain' lever, which always delivered two sugar pellets, or an 'uncertain' lever, resulting in four sugar pellets on 50% of trials. LDWs were then introduced as a return of three sugar pellets on 30-40% of uncertain rewarded trials. For half the rats, winning outcomes were paired with audiovisual feedback (cues). In a second study, the basolateral amygdala (BLA) was inactivated during initial presentation of LDWs. While LDWs shifted most rats' choice toward the certain lever, a subgroup of LDW vulnerable rats continued to choose the uncertain option, when the reward rate diminished. This profile of LDW vulnerability was reproduced after inactivating the BLA. Persistent choice of uncertain outcomes despite lower reward rates may reflect impaired functioning within the BLA. Future work using this model may provide insight into the neurobiological mechanisms contributing to the motivational properties of LDWs and their contribution to problematic gambling.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".