NMDA Receptors in the Basolateral Amygdala Complex Are Engaged for Pavlovian Fear Conditioning When an Animal’s Predictions about Danger Are in Error
Bibliographic record
Abstract
It is widely accepted that Pavlovian fear conditioning requires activation of NMDA receptors (NMDARs) in the basolateral amygdala complex (BLA). However, it was recently shown that activation of NMDAR in the BLA is only required for fear conditioning when danger occurs unexpectedly; it is not required for fear conditioning when danger occurs as expected. This study tested the hypothesis that NMDARs in the BLA are engaged for Pavlovian fear conditioning when an animal’s predictions regarding danger are in error. In each experiment, rats (females in Experiment 1 and males in Experiments 2–5) were conditioned to fear one stimulus, S1, when it was paired with foot-shock (S1→shock), and 48 h later, a second stimulus, S2, when it was presented in sequence with the already-conditioned S1 and foot-shock (S2→S1→shock). Conditioning to S2 occurred under a BLA infusion of the NMDAR antagonist, D-AP5 or vehicle. The subsequent tests of freezing to S2 alone and S1 alone revealed that the antagonist had no effect on conditioning to S2 when the shock occurred exactly as predicted by the S1, but disrupted this conditioning when the shock occurred earlier/later than predicted by S1, or at a stronger/weaker intensity. These results imply that errors in the timing or intensity of a predicted foot-shock engage NMDARs in the BLA for Pavlovian fear conditioning. They are discussed in relation to theories which propose a role for prediction error in determining how experiences are organized in memory and how activation of NMDAR in the BLA might contribute to this organization. SIGNIFICANCE STATEMENTThis study is significant in showing that prediction error determines how a new experience is encoded with respect to a past experience and, thereby, whether NMDA receptors (NMDARs) in the basolateral amygdala complex (BLA) encode the new experience. When prediction error is small (e.g., danger occurs as and when expected), the new experience is encoded together with a past experience as part of the same “mental model,” and NMDAR activation in the BLA is not needed for this encoding. By contrast, when prediction error is large (e.g., danger occurs at an unexpected intensity or time), the new experience is encoded separately from the past experience as part of a new mental model, and NMDAR activation in the BLA is needed for this encoding.
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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 | 0.000 |
| 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".