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Record W2345919830 · doi:10.3389/fnagi.2016.00033

Modulation of Prepulse Inhibition and Startle Reflex by Emotions: A Comparison between Young and Older Adults

2016· article· en· W2345919830 on OpenAlexafffund
Jolyanne Le Duc, Philippe Fournier, Sylvie Hébert

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsInternational Laboratory for Brain, Music and Sound ResearchUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du TravailCanada Foundation for Innovation
KeywordsPrepulse inhibitionMoro reflexPsychologyAudiologyYoung adultGatingDevelopmental psychologySenescenceStartle responseReflexNeuroscienceMedicineSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

This study examined whether or not the acoustic startle response and sensorimotor gating may be modulated by emotions differentially between young and older adults. Two groups of participants (mean age Young: 24 years old; Elderly: 63.6 years old) were presented with three types of auditory stimuli (Startle alone, High or Low frequency Prepulse) while viewing pleasant, neutral, or unpleasant images. Electromyographic activity of the eyeblink response was measured. Results show that older adults displayed diminished eyeblink responses whereas younger adults displayed enhanced eyeblink responses when viewing negative images. Sensorimotor gating also differed between young and older adults, with enhanced sensorimotor gating abilities while viewing positive pictures in older adults and diminished abilities while viewing negative pictures among younger adults. These results argue in favor of a differential emotional influence on the sensorimotor abilities of young and older adults, with a positivity bias among the latter.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.064
GPT teacher head0.341
Teacher spread0.278 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations20
Published2016
Admission routes2
Has abstractyes

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