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Record W2001678613 · doi:10.1167/14.10.1061

Familiarity wins over novelty: A persistent attentional bias toward regularities

2014· article· en· W2001678613 on OpenAlexaff
Ruiguo Yu, Jiaying Zhao

Bibliographic record

VenueJournal of Vision · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNoveltyRandom forestComputer scienceArtificial intelligencePattern recognition (psychology)PsychologySocial psychology

Abstract

fetched live from OpenAlex

The visual environment is often stable, but some aspects may change over time. For example, the furniture in the room may be re-arranged, and new furniture may replace old ones. The challenge for the mind is thus to update knowledge about the environment in light of new information. Here we examine whether the attentional bias to regularities can be shifted in the presence of new stimuli. The experiment consists of two halves. In the first half, observers viewed four simultaneous streams of shapes. The stream in one 'structured' location contained triplets, the shapes in one 'random' location were randomized, and a gray square appeared in each of the two 'neutral' locations. Occasional search arrays were presented where the target appeared randomly at one of the four locations. In the second half, everything was the same except that the stream in the structured or the random location may change. Several changes occurred across four conditions: (1) the structured stream became random; (2) the random stream became structured; (3) the random stream now contained new random shapes; and (4) the random stream now contained new structured shapes. In the baseline condition, no change ever occurred. We found that in all conditions, during the first half of the experiment, target discrimination was reliably faster for targets at structured vs. random or neutral locations, suggesting that attention was drawn to the structured location. In the second half, regardless of condition, target discrimination was again reliably faster for targets at structured vs. random or neutral locations. This suggests that attention persisted at the previously structured location, even though the stream was no longer structured, or newly structured stream or new shapes emerged in another location. These findings reveal the robustness and persistence of the attentional bias to regularities even in the presence of novel stimuli. Meeting abstract presented at VSS 2014

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)0.001
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.195
GPT teacher head0.406
Teacher spread0.211 · 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
Published2014
Admission routes1
Has abstractyes

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