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Record W1830656823 · doi:10.1167/15.12.1225

How do we make social decisions? Gaze strategies used to predict and optimize social information during conversation.

2015· article· en· W1830656823 on OpenAlexaff
Nida Latif, M.Badrul Huda el Haque, Monica S. Castelhano, Kevin G. Munhall

Bibliographic record

VenueJournal of Vision · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOpinion Dynamics and Social Influence
Canadian institutionsQueen's University
Fundersnot available
KeywordsGazeConversationPsychologySocial cueTask (project management)Cognitive psychologyTurn-takingNonverbal communicationSocial psychologyCommunication

Abstract

fetched live from OpenAlex

Our ability to make quick social decisions during everyday interactions is vital to effective communication. However, in social situations, we have access to an abundance of information and the cognitive challenge to rapidly select optimal strategies to gather information necessary for the social decision-making process. In this study, we investigated how modifying information availability influences observers’ decisions and the gaze strategies selected to make affiliation judgment (friends vs. strangers) for silent videos of two interacting individuals. We demonstrated that eliminating information (full-body vs. head-only cues) resulted in a reduced ability to distinguish friends from strangers and the use of different eye-movement strategies to perform the same social task. Observers made more fixations towards the talkers’ eyes than other locations in both conditions. However, when information was restricted to a head-only view, participants switched gaze between eyes and mouth more frequently than with full information. Further, availability of full-body cues resulted in observers switching gaze between talkers more frequently to discriminate friends from strangers. When examining how gaze strategy predicts overall accuracy of a social decision given full-body information, we demonstrated that individuals who fixated more on the mouth were likely to be more accurate in affiliation discrimination. We suggest that gaze contributes to social decisions during conversation because we are able to predict the conversational structure of a conversation. For example, we demonstrated that observers are able to predict the point of a turn exchange between talkers by fixating on the talker who is about to speak up to 250 ms prior to them speaking. These results demonstrate that observers select gaze strategies to optimize all available information and that greater optimization predicts increased accuracy of our decisions. We further conclude that human social abilities rely on versatile decision-making and prediction strategies to handle the complexity of our social world. Meeting abstract presented at VSS 2015

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.528

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 communication0.0010.001
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 head0.025
GPT teacher head0.300
Teacher spread0.275 · 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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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