Current understanding of fear learning and memory in humans and animal models and the value of a linguistic approach for analyzing fear learning and memory in humans
Bibliographic record
Abstract
Fear is an emotion that serves as a driving factor in how organisms move through the world. In this review, we discuss the current understandings of the subjective experience of fear and the related biological processes involved in fear learning and memory. We first provide an overview of fear learning and memory in humans and animal models, encompassing the neurocircuitry and molecular mechanisms, the influence of genetic and environmental factors, and how fear learning paradigms have contributed to treatments for fear-related disorders, such as posttraumatic stress disorder. Current treatments as well as novel strategies, such as targeting the perisynaptic environment and use of virtual reality, are addressed. We review research on the subjective experience of fear and the role of autobiographical memory in fear-related disorders. We also discuss the gaps in our understanding of fear learning and memory, and the degree of consensus in the field. Lastly, the development of linguistic tools for assessments and treatment of fear learning and memory disorders is discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".