MétaCan
Menu
Back to cohort
Record W2089732625 · doi:10.1097/wnr.0b013e3282f48401

Development of static and dynamic perception for luminance-defined and texture-defined information

2008· article· en· W2089732625 on OpenAlexaff
Armando Bertone, Julie Hanck, Kim Cornish, Jocelyn Faubert

Bibliographic record

VenueNeuroreport · 2008
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPerceptionStimulus (psychology)PsychologyLuminanceCognitive psychologyAudiologyVisual perceptionDevelopmental psychologyComputer scienceNeuroscienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The development of static and dynamic perception for stimuli requiring different levels of neural analysis was assessed by measuring orientation-identification and direction-identification thresholds for both lower-level [or first-order (FO)] and higher-level [or second-order (SO)] stimuli as a function of age. Results demonstrate that both lower-level and higher-level perception continue to develop during school-age years in both dynamic and static domains. When compared with adult levels, dynamic performance for 5-6-year-olds is significantly decreased for SO, but not for the FO perception; however, type of stimulus (FO vs. SO) did not affect the development of static perception. We therefore suggest that levels of stimulus complexity should be considered an important variable when assessing and making inferences regarding the typical and atypical development of static and dynamic perception.

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)0.004
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
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.048
GPT teacher head0.301
Teacher spread0.252 · 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

Citations27
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueNeuroreportSame topicVisual perception and processing mechanismsFrench-language works237,207