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Record W2215525683 · doi:10.1080/00224499.2015.1096887

How Ego Depletion Affects Sexual Self-Regulation: Is It More Than Resource Depletion?

2015· article· en· W2215525683 on OpenAlexafffund
Kévin Nolet, Joanne-Lucine Rouleau, Massil Benbouriche, Fannie Carrier Emond, Patrice Renaud

Bibliographic record

VenueThe Journal of Sex Research · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversité du Québec en OutaouaisUniversité de MontréalInstitut Philippe Pinel de Montréal
FundersFonds de Recherche du Québec - SantéFonds de Recherche du Québec-Société et CultureTeva Pharmaceutical Industries
KeywordsEgo depletionResource depletionId, ego and super-egoResource (disambiguation)PsychologySocial psychologyComputer scienceSelf-controlEcologyBiology

Abstract

fetched live from OpenAlex

Rational thinking and decision making are impacted when in a state of sexual arousal. The inability to self-regulate arousal can be linked to numerous problems, like sexual risk taking, infidelity, and sexual coercion. Studies have shown that most men are able to exert voluntary control over their sexual excitation with various levels of success. Both situational and dispositional factors can influence self-regulation achievement. The goal of this research was to investigate how ego depletion, a state of low self-control capacity, interacts with personality traits-propensities for sexual excitation and inhibition-and cognitive absorption, to cause sexual self-regulation failure. The sexual responses of 36 heterosexual males were assessed using penile plethysmography. They were asked to control their sexual arousal in two conditions, with and without ego depletion. Results suggest that ego depletion has opposite effects based on the trait sexual inhibition, as individuals moderately inhibited showed an increase in performance while highly inhibited ones showed a decrease. These results challenge the limited resource model of self-regulation and point to the importance of considering how people adapt to acute and high challenging conditions.

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)0.007
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.244
GPT teacher head0.486
Teacher spread0.242 · 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
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

Citations12
Published2015
Admission routes2
Has abstractyes

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