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Record W4404650976 · doi:10.1093/cercor/bhae454

When emotion and time meet from human and rodent perspectives: a central role for the amygdala?

2024· review· en· W4404650976 on OpenAlexfundno aff
Valérie Doyère, Sylvie Droit‐Volet

Bibliographic record

VenueCerebral Cortex · 2024
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueYork UniversityCity University of New YorkNational Science Foundation
KeywordsAmygdalaTime perceptionStimulus (psychology)NeurosciencePsychologyArousalCognitive psychologyPerceptionPrefrontal cortexContext (archaeology)CognitionBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.321
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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