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Record W2019508573 · doi:10.1080/13576500701479970

Fixation and attention control in lateralised target detection and free recall with words

2007· article· en· W2019508573 on OpenAlexaff
Daniel Voyer, David B. Boles

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2007
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsFree recallPsychologyRecallLateralityFixation (population genetics)Cognitive psychologyModality (human–computer interaction)AudiologyTask (project management)PerceptionDevelopmental psychologyArtificial intelligenceNeuroscienceComputer scienceBiology

Abstract

fetched live from OpenAlex

The purpose of the present study was to test the hypothesis that lateralised target detection in the visual modality would produce results similar in magnitude, reliability, and validity to those obtained in the auditory modality with an analogue task. Thus, it was expected that it would produce laterality effects that are larger, more reliable, and more valid than those obtained in a free recall task. The claim that target detection provides its own attention control also led to the hypothesis that the magnitude of laterality effects should be affected by fixation control in free recall but not target detection. A total of 349 right-handed participants completed a word recognition task with either free recall or target detection with or without fixation control. Only the finding that free recall was generally more reliable than target detection went contrary to the hypotheses. This finding is interpreted as reflecting a consistent attentional bias that stems from task requirements. In general, the results suggest that target detection without fixation control has much potential as a measure of perceptual asymmetries in the visual modality.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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