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Record W2129293567 · doi:10.1111/1467-8721.01255

Attention, Researchers! It Is Time to Take a Look at the Real World

2003· article· en· W2129293567 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Directions in Psychological Science · 2003
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsYork UniversityUniversity of British Columbia
Fundersnot available
KeywordsReflexivityPsychologyTask (project management)Relevance (law)Cognitive psychologyNatural (archaeology)Cognitive scienceCognitionSociologyNeuroscience

Abstract

fetched live from OpenAlex

Theories of attention, too often generated from artificial laboratory experiments, may have limited validity when attention in the natural world is considered. For instance, for more than two decades, conceptualizations of “reflexive” and “volitional” shifts of spatial attention have been grounded in methodologies that do not recognize or utilize the basic fact that people routinely use the eyes of other people as rich and complex attentional cues. This fact was confirmed by our novel discovery that eyes will trigger a reflexive shift of attention even when they are presented centrally and are known to be spatially nonpredictive. This exploration of real-world attention also led to our finding that, contrary to popular wisdom, arrows, like eyes, are capable of producing reflexive shifts of attention—a discovery that brings into question much of the existing attention research. We argue that research needs to be grounded in the real world and not in experimental paradigms. It is time for cognitive psychology to reaffirm the difficult task of studying attention in a manner that has relevance to real-life situations.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.011

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 head0.257
GPT teacher head0.482
Teacher spread0.225 · 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