Valence-dependent contribution by the basolateral amygdala to active but not inhibitory avoidance and reward-seeking
Bibliographic record
Abstract
The basolateral amygdala (BLA) is integral for promoting instrumental actions to avoid aversive events, and also contributes to certain aspects of reward-seeking. However, this sometimes requires discriminating between stimuli to ascertain whether it is more appropriate to initiate or suppress actions to obtain these goals. The present study examined BLA involvement in different avoidance strategies in male and female rats well-trained on different lever-press avoidance and reward-seeking tasks. Active/inhibitory avoidance required discrimination between tones presented pseudorandomly in a session that signaled shocks could be avoided by making or withholding a press on a lever inserted coincidentally with tone presentation. BLA inactivation (via infusion of GABA agonists) reduced active avoidance while slightly enhancing inhibitory avoidance in the same session. Similarly, on a dual-cued appetitive go/no-go task, BLA inactivation also impaired active, but not inhibitory reward-seeking. These treatments also disrupted performance in rats trained on a simpler, single-cue active avoidance task with no inhibitory component. However, rats trained on a single-cue reward task were impervious to the effects of BLA inactivation. Few sex differences were observed. These data reveal a fundamental contribution by the BLA in promoting actions to avoid punishments or secure rewards when an actor must discriminate between different stimuli to ascertain whether actions should be made or withheld, and may attenuate inhibitory avoidance when active strategies are sometimes required. Yet, under more rudimentary conditions where a single stimulus provokes actions, the valence of the pursued goal biases BLA involvement, as it remains critical for instrumental avoidance, but not reward-seeking.
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 | 0.000 | 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) | 0.001 | 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".