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Record W4311560923 · doi:10.1167/jov.22.14.4106

Is There One “Beam” of Attention for Searching in Space and Time?

2022· article· en· W4311560923 on OpenAlexaff
Raymond M. Klein, Brett Feltmate, Yoko Ishigami, N.M.F. Murray

Bibliographic record

VenueJournal of Vision · 2022
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSpace (punctuation)SpacetimeComputer scienceSpace timeTheoretical computer sciencePhysics

Abstract

fetched live from OpenAlex

In their pure forms, searching in space entails the allocation of attention to items distributed in space and presented at the same time whereas searching in time entails the allocation of attention to items distributed in time and presented at the same location. In two quite independent projects we have explored whether the metaphorical “beams” operating the domains of space and time might be independent or the same. In one project we used a differential approach. Early research using spatial (Snyder, 1972) and temporal search tasks (McLean, Broadbent & Broadbent, 1983) reported a substantial degree of sloppiness (binding errors). We had participants perform both of these tasks to see if the frequency of these binding errors in the domains of space and time might be correlated. We replicated both early findings of binding errors in space and in time, but their frequency of occurrence in the two domains was not significantly correlated. In the other project, we used an experimental approach. Here we explored whether the principles described by Duncan & Humphreys (1989; hereafter D&H) for searching in space would apply similarly to searching in time. Not surprisingly, performance in spatial search conformed to the predictions of D&H's principles. Importantly, temporal search performance followed the same pattern, suggesting that D&H’s principles are indeed generalizable to temporal search. We will speculate on why these two approaches seem to yield different answers to the question posed in our title.

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 score0.692
Threshold uncertainty score0.096

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 communication0.0000.000
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 head0.027
GPT teacher head0.316
Teacher spread0.290 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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