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Record W1992844381 · doi:10.1167/10.7.1398

The temporal profile of visual information sampling and integration

2010· article· en· W1992844381 on OpenAlexaff
Caroline Blais, Martin Arguin, F. Gosselin

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsContrast (vision)Sampling (signal processing)Artificial intelligenceGaussianPerceptComputer sciencePattern recognition (psychology)MathematicsComputer visionPsychologyPhysicsPerception

Abstract

fetched live from OpenAlex

While intuition suggests that visual information sampling through time is continuous, some have argued instead that sampling occurs in temporally discrete moments (VanRullen, & Koch, 2003). Of related interest is the question of how visual information is integrated through time. For example, is the information simply summed? We attempted to clarify the nature of visual information sampling and integration through time using a temporal response classification approach. Five subjects were asked to decide which of two movies, presented successively at the center of the screen, was the brightest. Each movie consisted in a sequence of 30 Gaussian blobs (200 ms) of different contrasts and subtending one degree of visual angle. A patch of spatial bit noise displayed through a Gaussian aperture was presented for 200 ms at the movies' location immediately before and after each movie. The contrast of the Gaussian blobs varied randomly through time. Specifically, on each trial, both movies had the same average and maximum contrast values across their temporal extent, but they differed in the temporal distribution of the contrasts. Thus, the brightness decision could only be influenced by the interaction between the participant's sampling/integration profile and the temporal sequence of contrasts in the stimuli. The sequence of contrasts that “optimally” led to a bright percept was computed for each participant by performing multiple regressions on the contrast temporal sequences and the participant's decisions. Three participants out of five showed a clear oscillation in their information sampling function (ranging between 5 and 15 Hz), and a linear decrease of information intake through time; the other participants reported being incapable of performing the task. Our results support the hypothesis that the visual system samples information in a discrete manner. They also indicate that the weight given to the information sampled decreases as information accumulates.

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)0.001
metaresearch head score (Gemma)0.011
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.369
Teacher spread0.330 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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