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Record W2948226866 · doi:10.3389/fpsyg.2019.01262

Eye Contact Is a Two-Way Street: Arousal Is Elicited by the Sending and Receiving of Eye Gaze Information

2019· article· en· W2948226866 on OpenAlexafffund
Michelle Jarick, Renee Bencic

Bibliographic record

VenueFrontiers in Psychology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMacEwan University
FundersMacEwan University
KeywordsGazeArousalPsychologyEye contactSkin conductanceEye trackingEye movementAudiologyCommunicationSocial psychologyNeuroscienceComputer visionComputer scienceMedicine

Abstract

fetched live from OpenAlex

Research shows that arousal is significantly enhanced while participants make eye contact with a live person compared to viewing a picture of direct or averted gaze. Recent research has pointed towards the potential for social interaction as a possible driving force behind the arousal enhancement. That is, eye gaze is not only a signal perceived but also a signal sent out in order to communicate with others. This study aimed to test this by having dyads engage in eye contact and averted gaze naturally, while wearing sunglasses, and while blindfolded; such that the gaze signals were clear, degraded, and blocked, respectively. Autonomic nervous system arousal was measured via skin conductance response and level. The results showed that dyads exhibited the highest degree of arousal (increased skin conductance) while making eye contact (send/receive) compared to send-only or receive-only gaze trials, however this was only the case if eye contact was clear. Once gaze information became degraded (by sunglasses or blindfold), arousal significantly decreased and was no longer modulated by the sending and receiving of gaze. Therefore, the arousal enhancement observed during eye contact is not only caused by receiving gaze signals (the focus of previous research) and should be more accurately attributed to the subtle interplay between sending and receiving gaze signals.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.019
GPT teacher head0.313
Teacher spread0.294 · 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 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

Citations64
Published2019
Admission routes2
Has abstractyes

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