Fundamental Contribution by the Basolateral Amygdala to Different Forms of Decision Making
Bibliographic record
Abstract
Impairments in decision making about risks and rewards have been observed in patients with amygdala damage. Similarly, lesions of the basolateral amygdala (BLA) in rodents disrupts cost/benefit decision making, reducing preference for larger rewards obtainable after a delay or considerable physical effort. We assessed the effects of inactivation of the BLA on risk- and effort-based decision making, using discounting tasks conducted in an operant chamber. Separate groups of rats were trained on either a risk- or effort-discounting task, consisting of four blocks of 10 free-choice trials. Selection of one lever always delivered a smaller reward (one or two pellets), whereas responding on the other delivered a larger, four pellet reward. For risk discounting, the probability of receiving the larger reward decreased across trial blocks (100-12.5%), whereas on the effort task, the larger reward was delivered after a ratio of presses that increased across blocks (2-20). Infusions of GABA agonists baclofen/muscimol into the BLA disrupted risk discounting, inducing a risk-averse pattern of choice, and increased response latencies and trial omissions, most prominently during trial blocks that provided the greatest uncertainty about the most beneficial course of action. Similar inactivations also increased effort discounting, reducing the preference for larger yet more costly rewards, even when the relative delays to reward delivery were equalized across response options. These findings point to a fundamental role for the BLA in different forms of cost/benefit decision making, facilitating an organism's ability to overcome a variety of costs (work, uncertainty, delays) to promote actions that may yield larger rewards.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".