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Record W2161710547 · doi:10.1177/0956797613491968

In the Eye of the Beholder

2013· article· en· W2161710547 on OpenAlexaff
Frances S. Chen, Julia A. Minson, Maren Schöne, Markus Heinrichs

Bibliographic record

VenuePsychological Science · 2013
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of British Columbia
FundersAssociation for Psychological Science
KeywordsPersuasionPsychologyGazeEye contactPersuasive communicationEye trackingVariety (cybernetics)Social psychologyAttitude changeCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Popular belief holds that eye contact increases the success of persuasive communication, and prior research suggests that speakers who direct their gaze more toward their listeners are perceived as more persuasive. In contrast, we demonstrate that more eye contact between the listener and speaker during persuasive communication predicts less attitude change in the direction advocated. In Study 1, participants freely watched videos of speakers expressing various views on controversial sociopolitical issues. Greater direct gaze at the speaker's eyes was associated with less attitude change in the direction advocated by the speaker. In Study 2, we instructed participants to look at either the eyes or the mouths of speakers presenting arguments counter to participants' own attitudes. Intentionally maintaining direct eye contact led to less persuasion than did gazing at the mouth. These findings suggest that efforts at increasing eye contact may be counterproductive across a variety of persuasion contexts.

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)0.003
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.166
GPT teacher head0.453
Teacher spread0.287 · 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

Citations47
Published2013
Admission routes1
Has abstractyes

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