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Record W2062004313 · doi:10.1167/9.4.20

Contrast-reversal abolishes perceptual learning

2009· article· en· W2062004313 on OpenAlexaff
Zahra Hussain, Allison B. Sekuler, Patrick Bennett

Bibliographic record

VenueJournal of Vision · 2009
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsPerceptual learningContrast (vision)Stimulus (psychology)PerceptionPsychologyAudiologyCommunicationArtificial intelligencePattern recognition (psychology)Cognitive psychologyComputer scienceNeuroscienceMedicine

Abstract

fetched live from OpenAlex

We tested the effects of contrast reversal on perceptual learning in a 10AFC texture identification task. Four groups of subjects performed the task on two consecutive days. One group saw the same textures on both days, whereas three other groups saw novel, rotated (180 deg), or contrast-reversed textures on the second day. Response accuracy improved during the first day in all groups. Accuracy decreased significantly at the start of Day 2 in the groups who saw novel, rotated or contrast-reversed textures, but not in the group who saw the same textures. Moreover, the drop in performance was the same in the groups who saw novel, rotated, and contrast-reversed textures. Control experiments showed that making subjects aware of the stimulus transformations at the start of either the first or second day did not alter the results. Hence, the effects of contrast-reversal and 180 deg rotation on the generalization of learning were the same as the effect of using novel stimuli, and knowledge of the stimulus transformation did not reduce their effects. We consider the implications of this pattern of results for the neural mechanisms recruited during the identification and learning of two-dimensional visual patterns.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.035
GPT teacher head0.340
Teacher spread0.305 · 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 designBench or experimental
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

Citations9
Published2009
Admission routes1
Has abstractyes

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