Contextual Fear Conditioning in Humans: Cortical-Hippocampal and Amygdala Contributions
Bibliographic record
Abstract
Functional imaging studies of cued fear conditioning in humans have mostly confirmed findings in animals, but it is unclear whether the brain mechanisms that underlie contextual fear conditioning in animals are also preserved in humans. We investigated this issue using functional magnetic resonance imaging and virtual reality contexts. Subjects underwent differential context conditioning in which they were repeatedly exposed to two contexts (CXT+ and CXT-) in semirandom order, with contexts counterbalanced across participants. An unsignaled footshock was consistently paired with the CXT+, and no shock was ever delivered in the CXT-. Evidence for context conditioning was established using skin conductance and anxiety ratings. Consistent with animal models centrally implicating the hippocampus and amygdala in a network supporting context conditioning, CXT+ compared with CXT- significantly activated right anterior hippocampus and bilateral amygdala. In addition, context conditioning was associated with activation in posterior orbitofrontal cortex, medial dorsal thalamus, anterior insula, subgenual anterior cingulate, and parahippocampal, inferior frontal, and parietal cortices. Structural equation modeling was used to assess interactions among the core brain regions mediating context conditioning. The derived model indicated that medial amygdala was the source of key efferent and afferent connections including input from orbitofrontal cortex. These results provide evidence that similar brain mechanisms may underlie contextual fear conditioning across species.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".