When emotion and time meet from human and rodent perspectives: a central role for the amygdala?
Bibliographic record
Abstract
Initiated by a long stay of Valérie Doyère in the laboratory of Joseph LeDoux, a Franco-American collaborative group was formed around the topic of emotion and time perception in a comparative perspective between humans and non-human animals. Here, we discuss results from our studies on the mechanisms underlying time distortion under 2 conditions, timing of a threatening stimulus and timing of a neutral stimulus in the context of fear, with insights from neurodevelopment. Although the type of temporal distortion depends on the experimental situations, in both humans and rodents a high-arousal emotion automatically triggers acceleration of an "internal clock" system, an effect that may rely on the early maturing amygdala. Our studies, particularly in humans, also point to the role of attention and self-awareness in regulating the effect of fear on timing, relying on the prefrontal cortex, a late maturing structure. Thus, in line with LeDoux, while the amygdala may process all characteristics of events (including time) necessary to quickly trigger appropriate survival behaviors, some type of time distortions may rely on higher-order processing, some specific to humans. The extent of the network underlying threat-related time distortions remains to be explored, with species comparisons being a promising means of investigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".