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Record W2253271832 · doi:10.1177/2041669515625793

The Nature and Timing of Tele-Pseudoscopic Experiences

2016· article· en· W2253271832 on OpenAlexaff
Stephen Palmisano, Harold Hill, Robert S. Allison

Bibliographic record

Venuei-Perception · 2016
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersAustralian Research Council
KeywordsStereoscopic acuityVisual fieldPsychologyObserver (physics)Object (grammar)Cognitive psychologyComputer visionArtificial intelligenceComputer scienceStereopsisPhysics

Abstract

fetched live from OpenAlex

Interchanging the left and right eye views of a scene (pseudoscopic viewing) has been reported to produce vivid stereoscopic effects under certain conditions. In two separate field studies, we examined the experiences of 124 observers (76 in Study 1 and 48 in Study 2) while pseudoscopically viewing a distant natural outdoor scene. We found large individual differences in both the nature and the timing of their pseudoscopic experiences. While some observers failed to notice anything unusual about the pseudoscopic scene, most experienced multiple pseudoscopic phenomena, including apparent scene depth reversals, apparent object shape reversals, apparent size and flatness changes, apparent reversals of border ownership, and even complex illusory foreground surfaces. When multiple effects were experienced, patterns of co-occurrence suggested possible causal relationships between apparent scene depth reversals and several other pseudoscopic phenomena. The latency for experiencing pseudoscopic phenomena was found to correlate significantly with observer visual acuity, but not stereoacuity, in both studies.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.332
Teacher spread0.291 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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