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Record W4392423992 · doi:10.6000/1929-4409.2020.09.198

Eye Movement Features in the Depth and Spatial Perspective Perception of Paintings and Other Static Scenes

2021· article· en· W4392423992 on OpenAlexvenueno aff
Marsel Fazlyyyakhmatov, Adelina Mutagirova, Oleg V. Nedopekin, Vladimir Antipov

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsnot available
FundersKazan Federal University
KeywordsPerspective (graphical)PerceptionMovement (music)Computer visionPaintingArtificial intelligenceAestheticsCognitive psychologyArtPsychologyComputer scienceVisual artsNeuroscience

Abstract

fetched live from OpenAlex

In this paper, using the example of two scenes, they showed that it is possible to perceive the depth and spatial perspective of painting images (and other static scenes) without the condition of binocular disparity (hereinafter referred to as the 3D phenomenon). To study the 3D phenomenon, eye movement is recorded. The students of the Institute of Physics at Kazan Federal University and an experienced researcher of the 3D phenomenon, the author and the developer of the working methodology took part in the surveys on eye movement record. To identify the perception of spatial perspective, 3D raster images are used mounted on the same static stimulus scenes (pictures). The first plot includes obtaining information on eye movement in the conditions of perception of two paintings with the elements of a monocular perspective of images. Histograms show that students perceive the perspective of the pictures displayed on the monitor screen. In two scenes presented, eye focusing occurs outside the plane of stimulus images. When students demonstrate a monitor screen with text and a white sheet of the histogram, they show that the focus planes are located between the screen and the students' eyes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.204

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 studies0.0000.001
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 head0.049
GPT teacher head0.350
Teacher spread0.301 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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