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Record W2905863100 · doi:10.1177/0956797619842198

Where Does Time Go When You Blink?

2019· article· en· W2905863100 on OpenAlexfundno aff
Shany Grossman, Chen Gueta, Slav Pesin, Rafael Malach, Ayelet N. Landau

Bibliographic record

VenuePsychological Science · 2019
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationCanadian Institute for Advanced ResearchVan Leer Jerusalem InstituteJames S. McDonnell Foundation
KeywordsPsychologyStimulus (psychology)Time perceptionAudiologyPerceptionAttentional blinkStimulus modalityNeural correlates of consciousnessSensory systemCognitive psychologyNeuroscienceCognition

Abstract

fetched live from OpenAlex

Retinal input is frequently lost because of eye blinks, yet humans rarely notice these gaps in visual input. Although previous studies focused on the perceptual and neural correlates of diminished awareness to blinks, the impact of these correlates on the perceived time of concurrent events is unknown. Here, we investigated whether the subjective sense of time is altered by spontaneous blinks. We found that participants ( N = 22) significantly underestimated the duration of a visual stimulus when a spontaneous blink occurred during stimulus presentation and that this underestimation was correlated with the blink duration of individual participants. Importantly, the effect was not present when durations of an auditory stimulus were judged ( N = 23). The results point to a link between spontaneous blinks, previously demonstrated to induce activity suppression in the visual cortex, and a compression of subjective time. They suggest that ongoing encoding within modality-specific sensory cortices, independent of conscious awareness, informs the subjective sense of time.

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)0.005
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.069
GPT teacher head0.381
Teacher spread0.312 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

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