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Attention and Perception

2015· other· en· W1498188424 on OpenAlexaff
Ronald A. Rensink

Bibliographic record

VenueEmerging Trends in the Social and Behavioral Sciences · 2015
Typeother
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInattentional blindnessPerceptionBlindnessChange blindnessExperiential learningRelation (database)PsychologyCognitive psychologyFilter (signal processing)Computer scienceCognitive scienceOptometryMathematics education

Abstract

fetched live from OpenAlex

Abstract This essay discusses several key issues concerning the study of attention and its relation to visual perception, with an emphasis on behavioral and experiential aspects. It begins with an overview of several classical works carried out in the latter half of the twentieth century, such as the development of early filter and spotlight models of attention. This is followed by a survey of subsequent research that extended or modified these results in significant ways. It includes work on various forms of induced blindness and on the capabilities of nonattentional processes. It also covers proposals about how a “just‐in‐time” allocation of attention can create the impression that we see our surroundings in coherent detail everywhere, as well as how the failure of such allocation can result in various perceptual deficits. The final section examines issues that have not received much consideration to date, but that may be important for new lines of research in the near future. These include the prospects for a better characterization of attention, the possibility of more systematic computational explanations, factors that may significantly modulate attentional operation, and the possibility of several kinds of visual attention and visual experience.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

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

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 head0.210
GPT teacher head0.452
Teacher spread0.241 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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