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Record W2079971667 · doi:10.1167/13.9.811

Visual search for multiple targets remains efficient when supported by recollective long-term memory

2013· article· en· W2079971667 on OpenAlexaff
Emma B. Guild, Jenna Cripps, Naseem Al-Aidroos

Bibliographic record

VenueJournal of Vision · 2013
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsVisual searchTask (project management)MemorizationRecallPsychologyPerceptionCognitive psychologyObject (grammar)Computer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

We are often faced with the task of searching our visual environment for multiple objects, such as when searching for any one of our friends at Club Vision. To accomplish this search we must retrieve information stored in long term memory (LTM). Surprisingly, searching through memory adds little cost to the efficiency of visual search. That is, visual search response times increase logarithmically with the number of targets stored in LTM (Wolfe, 2012, Psychol Sci): It doesn’t take much longer to search for sixteen friends than it does to search for eight. In this previous investigation, memorized targets were both perceptually identical to the visual search targets, and repeated frequently throughout the search task on target present trials. These conditions are conducive to a familiarity-based LTM process, which may have driven the efficiency of search. In the present study, we evaluated whether LTM-supported search remains efficient when based on flexible, abstract memory representations (i.e., recollection, rather than simple judgements of familiarity). Participants memorized 1, 2, 4, 8 or 16 targets, counterbalanced across blocks, and searched for these targets amidst twelve on-screen distractors. Experiment 1 used predominately target-absent trials and blocks terminated after the first successful target-present trial, ensuring that familiarity did not accrue over repeated presentations of the target. In Experiment 2, this task was extended by having participants memorize written object names and visually search for pictures of those objects, thereby eliminating perceptual familiarity. Despite these restrictions on the contribution of familiarity, we continued to see the efficient logarithmic relationship between visual search time and memory set size. These data suggest that interactions between visual search and LTM search are not completed solely based on familiarity; rather, participants can engage a recollection-based LTM process, calling upon flexible memory representations to support efficient visual search, even for previously unobserved information. Meeting abstract presented at VSS 2013

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.420

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 head0.018
GPT teacher head0.317
Teacher spread0.299 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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