MétaCan
Menu
Back to cohort
Record W3165879905 · doi:10.31234/osf.io/ua2f4

Stopped cold: Motor-response inhibition reduces the capacity of sexually-explicit stimuli to elicit subjective and physiological sexual arousal

2020· preprint· en· W3165879905 on OpenAlexaff
Elizabeth Clancy, Rachel L. Driscoll, Sierra A. Codeluppi, Tuuli M. Kukkonen, M. Fenske

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSexual arousalArousalPsychologyStimulus (psychology)PerceptionDevelopmental psychologyCognitive psychologySocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

The motivational incentive of sexual stimuli can be a salient force in determining the focus of thought and behaviour. Here we show that the simple act of not pressing a key during the perception of sexual content reduces its motivational incentive and subsequent capacity to elicit sexual arousal. Undergraduate participants (N=116) completed a Go/No-go task that required them to inhibit responses to either sexual or non-sexual images. Later they watched sexually explicit videos and reported moment-to-moment changes in self-reported sexual arousal, while thermography was used to record changes in genital physiological arousal. Participants who previously inhibited sexual images experienced lower levels of both self-reported and physiological arousal than those who inhibited non-sexual images. These results extend prior research to suggest that a by-product of motor-response inhibition is a negative alteration of stimulus-value representations for associated items— the kind of value that drives even the most biologically-fundamental forms of motivated behaviour.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0050.000

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 head0.128
GPT teacher head0.335
Teacher spread0.207 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicSexual function and dysfunction studiesFrench-language works237,207