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A review of the neural basis underlying the acoustic startle response with a focus on recent developments in mammals

2023· review· en· W4323928485 on OpenAlexafffund
Alice Zheng, Susanne Schmid

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2023
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchWestern University
KeywordsStartle responseMoro reflexNeuroscienceStimulus (psychology)Sensory gatingStartle reactionPsychologySensory systemContext (archaeology)Cognitive psychologyBiologyGatingReflex

Abstract

fetched live from OpenAlex

The startle response consists of whole-body muscle contractions, eye-blink, accelerated heart rate, and freezing in response to a strong, sudden stimulus. It is evolutionarily preserved and can be observed in any animal that can perceive sensory signals, indicating the important protective function of startle. Startle response measurements and its alterations have become a valuable tool for exploring sensorimotor processes and sensory gating, especially in the context of pathologies of psychiatric disorders. The last reviews on the neural substrates underlying acoustic startle were published around 20 years ago. Advancements in methods and techniques have since allowed new insights into acoustic startle mechanisms. This review is focused on the neural circuitry that drives the primary acoustic startle response in mammals. However, there have also been very successful efforts to identify the acoustic startle pathway in other vertebrates and invertebrates in the past decades, so at the end we briefly summarize these studies and comment on the similarities and differences between species.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.416
GPT teacher head0.467
Teacher spread0.051 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2023
Admission routes2
Has abstractyes

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