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Record W2076083770 · doi:10.1037/a0037790

Strategies and pseudoneglect on luminance judgments: An eye-tracking investigation.

2014· article· en· W2076083770 on OpenAlexafffund
Daniel Voyer, Jean Saint‐Aubin, Christine Cook

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyEye movementTask (project management)Context (archaeology)Cognitive psychologyLuminanceAttentional biasTwo-alternative forced choiceEye trackingSocial psychologyComputer visionComputer scienceCognition

Abstract

fetched live from OpenAlex

Four experiments were conducted to examine competing hypotheses relevant to the strategies believed to underlie pseudoneglect. The 4 experiments implemented manipulations relevant to eye-movement monitoring to evaluate the potential role of a comparison versus a global strategy in producing a left bias in a task involving a judgment of luminosity. Experiment 1 required task completion under free viewing while eye movements were monitored. The link between the observed behavioral bias and the strategy inferred from the pattern of eye movements was then examined. In Experiment 2, the eye-movement manipulation promoted reliance on a comparison strategy. Experiment 3 forced participants to use a global strategy. Finally, Experiment 4 also forced participants to use a global strategy, but it minimized the influence of memory in the task. Results from all 4 experiments supported the preponderance of the global strategy as driving the left bias. In Experiment 1, the magnitude of the bias decreased as the number of comparison increased. In Experiment 2, the left bias disappeared. Finally, in Experiments 3 and 4, the bias was the largest in the present series of experiments. These findings are discussed in the context of existing explanations of pseudoneglect.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.146
GPT teacher head0.436
Teacher spread0.290 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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