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A Reentrant View of Visual Masking, Object Substitution, and Response Priming

2006· book-chapter· en· W199482671 on OpenAlexaff
James T. Enns, Alejandro Lleras, Vince di Lollo

Bibliographic record

VenueThe MIT Press eBooks · 2006
Typebook-chapter
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMasking (illustration)ReentrancySubstitution (logic)Priming (agriculture)Visual maskingObject (grammar)Computer sciencePsychologyCommunicationArtificial intelligenceNeuroscienceVisual perceptionPerceptionBiologyArtProgramming language

Abstract

fetched live from OpenAlex

Abstract When a mask follows a briefly presented target there are several consequences. Theone that has historically received the most attention is a reduction in the visibility ofthe target. This is the conventional definition of masking. Yet, another equallyimportant consequence is that errors in target identification are biased toward theidentity of the mask rather than being randomly distributed among the targetalternatives. This is evidence of object substitution. Finally, when the target is asignal to make a speeded action, this action can be influenced by a prime stimulusthat is not even visible to the participant. This is known as masked responsepriming. In this chapter we review evidence concerning all three of theseconsequences of viewing rapid visual sequences. We argue that these consequencesare difficult to understand, either individually or together, as the consequence ofstrictly feed-forward processing in the visual brain. In contrast, when these resultsare considered from the perspective of reentrant visual circuitry, they are easier tounderstand and to relate to one another. Moreover, predictions derived from areentrant view of the brain lead to unexpected and novel results that are confirmedwhen tested against psychophysical data.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.527
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.075
GPT teacher head0.308
Teacher spread0.233 · 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
GenreOther

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

Citations6
Published2006
Admission routes1
Has abstractyes

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