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Record W2900951845 · doi:10.1167/18.12.11

Investigating overt and covert shifts of attention within social naturalistic scenes

2018· article· en· W2900951845 on OpenAlexaff
Aleya Flechsenhar, Olivia Larson, Albert End, Matthias Gamer

Bibliographic record

VenueJournal of Vision · 2018
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsMcGill University
Fundersnot available
KeywordsCovertPsychologyCognitive psychologySaccadeFixation (population genetics)Eye movementSalientEye trackingSet (abstract data type)Computer scienceComputer visionArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Eye-tracking studies on social attention have consistently shown that humans prefer to attend to other human beings. Much less is known about whether a similar preference is also evident in covert attentional processes. To enable a direct comparison, this study examined covert and overt attentional guidance within two different experimental setups using complex naturalistic scenes instead of isolated single features. In the first experiment, a modified version of the dot-probe paradigm served as a measure of covert reflexive attention toward briefly presented scenes containing a social feature in one half of the visual field compared to nonsocial elements in the other while controlling for low-level visual saliency. Participants showed a stable congruency effect with faster reaction times and fewer errors for probes presented on the social side of the scene. In a second experiment, we tracked eye movements for the same set of stimuli while manipulating the presentation time to allow for differentiating reflexive and more sustained aspects of overt attention. Supportive of the first results, analyses revealed a robust preference for social features concerning initial saccade direction as well as fixation allocation. Collectively, these experiments imply preferential processing of social features over visually salient aspects for automatic allocation of covert as well as overt attention.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.322
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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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