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Record W1993368923 · doi:10.1167/5.8.1012

Object-substitution masking: The identity of the mask does matter!

2005· article· en· W1993368923 on OpenAlexaff
Elizabeth S. Olds, Angela Weber

Bibliographic record

VenueJournal of Vision · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsOffset (computer science)Masking (illustration)Stimulus (psychology)Stimulus onset asynchronyComputer scienceDiamondArtificial intelligenceMathematicsPattern recognition (psychology)PsychologyMaterials scienceCognitive psychology

Abstract

fetched live from OpenAlex

The current experiment extends a previous study completed by Olds and Weber (VSS '04), in which a traditional object-substitution masking (OSM) task was combined with a shape manipulation. The design closely resembles that of Enns and Di Lollo's (1997) experiment, in which 4 dots, presented around one of three modified-diamond targets (diamond shapes missing a corner either on the left side or on the right side), trail in the display after target offset; in the present study, the 4-“dot” mask consisted of 4 smaller modified-diamonds (all missing a corner on the left side, or all missing a corner on the right side). The delay between target onset and mask onset (stimulus-onset asynchrony, or SOA) was 80 ms. Our previous study found an effect of target-mask similarity: when the modified-diamond target, and the modified-diamonds of the mask, were the same shape, accuracy was much higher than when the target and mask were different shapes. The present study manipulated the proportion of trials with masks missing corners on the left, versus trials with masks missing corners on the right, to determine whether the effect of mask identity could be attributed to re-entrant processing or simply to a response bias. The present study showed that the effect of target-mask similarity (replicated here) was not attributable to response bias, for these 80-ms SOA trials. Furthermore, in addition to this 80-ms-SOA condition (traditional OSM), a common-onset condition (mask duration 300 ms; trials intermixed with OSM trials) allowed us to compare effects of re-entrant processing with effects of interruption by a transient mask. Performance was lower in the common-onset condition than in the 80-ms-SOA “OSM” condition; in addition, a response bias was evident in the common-onset condition, along with very poor performance when target and mask were different shapes (indicating that observers were responding to the mask rather than the target, on common-onset trials).

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.065
GPT teacher head0.381
Teacher spread0.317 · 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
Published2005
Admission routes1
Has abstractyes

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