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Record W3000575613 · doi:10.1037/xhp0000708

The impact of scaling rather than shaping attention: Changes in the scale of attention using global motion inducers influence both spatial and temporal acuity.

2020· article· en· W3000575613 on OpenAlexaff
Rebecca K. Lawrence, Mark Edwards, Stephanie C. Goodhew

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
FundersAustralian Research CouncilAustralian Government
KeywordsScalingPoolingPerceptionCognitive psychologyZoomPsychologyComputer scienceArtificial intelligenceLens (geology)OpticsPhysicsNeuroscienceMathematics

Abstract

fetched live from OpenAlex

shapes are presented to focus attention in or spread it out broadly. This method is problematic because participants may not spread their attention across the entire region defined by unfilled shapes and instead may attend to only the annulus region of the shape. To address this, we developed a new method to manipulate attention-one which requires the pooling of information across the entire stimulus, not just around the outer border. We then tested the influence of attention scaling on perception using spatial and temporal gap tasks. Across 2 experiments, we found that sustaining a narrow attention scale improved both spatial and temporal acuity. These findings challenge recent research suggesting that attention scaling has differential impacts on spatial and temporal processing and instead support the zoom lens model that was proposed over 30 years ago. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.904
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.133
GPT teacher head0.425
Teacher spread0.292 · 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 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

Citations16
Published2020
Admission routes1
Has abstractyes

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