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Record W4411125970 · doi:10.31234/osf.io/x8eaz_v1

Unconscious processing of naturalistic scenes revealed by eye movement dynamics

2025· preprint· en· W4411125970 on OpenAlexfundno aff
Shaked Lublinsky, Shlomit Yuval‐Greenberg, Liad Mudrik

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicAnomaly Detection Techniques and Applications
Canadian institutionsnot available
FundersCanadian Institute for Advanced Research
KeywordsUnconscious mindMovement (music)Dynamics (music)Eye movementComputer scienceComputer visionArtificial intelligencePsychologyArtAestheticsPsychoanalysis

Abstract

fetched live from OpenAlex

The scope of unconscious processing is continuously debated. Here, we focus on scene processing without awareness: During conscious scene processing, the eyes are attracted to meaningful and visually salient areas, yet it is unknown if this occurs for unconscious processing. In two preregistered experiments, fifty-two participants were instructed to freely view scenes presented consciously or unconsciously with Continuous Flash Suppression stimulation. For invisible scenes, participants gazed more, but not longer, on objects and emotional faces, and were attracted to areas that are both semantically and visually salient. However, a comparison of eye movements with a Convolutional Neural Network mimicking the human visual ventral pathway revealed that unconscious gaze patterns correlated only with layers corresponding with early visual processes, while conscious patterns correlated also with higher-level layers. This suggests that even without awareness, the eyes are attracted to meaningful areas in scenes, though such processing is limited compared to conscious viewing.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.699

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 science0.0010.001
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 head0.008
GPT teacher head0.275
Teacher spread0.268 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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