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Record W3162314863 · doi:10.31234/osf.io/f8jsc

Response inhibition negatively impacts social-emotional evaluations of specific individuals

2020· preprint· en· W3162314863 on OpenAlexaff
Rachel L. Driscoll, Elizabeth Clancy, M. Fenske

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychologyResponse inhibitionBehavioral inhibitionSocial inhibitionStimulus (psychology)TrustworthinessSocial preferencesSocial psychologySocial memoryDevelopmental psychologyCognitive psychologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

Social-emotional evaluations of unfamiliar people are negatively impacted by ignoring or withholding motor-responses from images that depict them; an effect attributed to the propensity of inhibition to affectively devalue associated stimuli. Prior findings suggest that the social-emotional consequences of inhibition may be mediated by category-level representations that impact all members of a corresponding group. Here we assess whether social devaluation by inhibition also operates on item-level representations of specific individuals. Participants memorized individual identities of a group of fellow students before completing a Go/No-go response-inhibition task designed to associate item-level representations of each previously-memorized person with inhibition (No-go trials) or no inhibition (Go trials). Social identities associated with inhibition were consistently rated as less trustworthy in subsequent evaluations than those associated with Go trials that were not inhibited. This suggests that the social-emotional consequences of inhibition can be mediated by item-level stimulus representations in memory.

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)0.002
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
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 head0.394
GPT teacher head0.456
Teacher spread0.063 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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