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Record W3216057240 · doi:10.1037/xlm0001086

Keep an eye on your belongings: Gaze dynamics toward familiar and unfamiliar objects.

2021· article· en· W3216057240 on OpenAlexfundno aff
Oryah C. Lancry-Dayan, Tal Nahari, Gershon Ben‐Shakhar, Yoni Pertzov

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2021
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science Foundation
KeywordsGazeEye trackingPrioritizationObject (grammar)PsychologyTask (project management)Eye movementCognitive psychologyPreferenceHuman–computer interactionObserver (physics)Computer scienceCommunicationArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Through a series of studies, we investigate how people direct gaze toward familiar and unfamiliar objects. When an observer tries to encode objects, gaze is first directed preferentially to the familiar object followed by a later prioritization of the unfamiliar ones. We demonstrate that the initial preference reflects prioritization of personally significant information and could be volitionally controlled. The latter prioritization of the unfamiliar objects is determined by the immediate goals due to the task and is less controllable. These findings imply that the mechanism that guides gaze is flexible, affected by both long-term significance and short-term goals and could be only partially controlled. This study has also imperative practical implications for detecting concealed information using eye tracking. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.370
Teacher spread0.333 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of Experimental Psychology Learning Memory and CognitionSame topicDeception detection and forensic psychologyFrench-language works237,207