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Record W1986576408 · doi:10.1016/j.actpsy.2010.07.008

The voice of self-control: Blocking the inner voice increases impulsive responding

2010· article· en· W1986576408 on OpenAlexafffund
Alexa M. Tullett, Michael Inzlicht

Bibliographic record

VenueActa Psychologica · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsBlocking (statistics)PsychologyAudiologyMedicineComputer scienceComputer network

Abstract

fetched live from OpenAlex

Philosophers and psychologists have long-debated the notion that the voice in our heads might help us to control our actions. Evidence from a number of lines of research suggests that verbal resources help us to focus attention, providing reason to believe that the inner voice might aid self-control via this capacity. In this study we explored the link between verbal resources and self-control by occupying the inner voice and then assessing behavioral indices of self-control. Participants completed regular and switching versions of the Go/No-Go task while doing verbal or spatial secondary tasks. Compared with the spatial task, doing the verbal task resulted in more impulsive responding, as indicated by a greater tendency to make a 'Go' response, a pattern that was accentuated in the switching version of the Go/No-Go. Our results suggest that the inner voice helps us to exert self-control by enhancing our ability to restrain our impulses.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.355
Teacher spread0.298 · 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 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

Citations73
Published2010
Admission routes2
Has abstractyes

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