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Record W4311802965 · doi:10.1167/jov.22.14.3224

Rapid (<160ms) control over attentional capture by long-term memory attentional control settings

2022· article· en· W4311802965 on OpenAlexaff
Lindsay Plater, Maria Giammarco, Jack Hryciw, Naseem Al-Aidroos

Bibliographic record

VenueJournal of Vision · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsN2pcPsychologyFixation (population genetics)Attentional controlCognitive psychologyCognitionVisual searchVisual spatial attentionVisual attentionInhibition of returnSelective attentionNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Attentional control settings (ACSs) guide attention in our complex visual environments by determining which stimuli capture spatial attention: When searching for something blue, other blue objects will capture attention, but red objects will not. Recent research indicates that humans can maintain a long-term memory (LTM) ACS for up to 30 complex visual objects, whereby only those objects capture attention when searched for (Giammarco et al., 2016, Visual Cognition). This behavioural research suggests that salient stimuli are rapidly compared to LTM ACS representations to assess whether a shift in attention is appropriate. Alternatively, this research might be explained by later effects on behaviour after the capture of attention. The purpose of the current experiment was to use electroencephalography to better understand the timing of attentional control offered by LTM ACSs. Participants memorized and searched for 30 complex visual objects in a modified Posner cueing task. In every target display, one object appeared to the left of fixation and one object appeared to the right. Participants indicated which was previously studied, inducing an ACS for the studied objects. Targets were preceded by cues at each location, one studied (ACS match) and one non-studied (ACS non-match). Similar to previous research, we observed a cueing effect; participants responded more quickly to the target if it appeared at the same location as the studied cue versus the non-studied cue. New for this study, some trials excluded target stimuli, so we could measure the cue-related N2pc as a measure of attentional selection. Consistent with rapid effects of LTM ACSs, we observed an N2pc contralateral to studied cues, with lateralized differences between studied and non-studied cues emerging within 160 ms post cue. These results provide direct evidence that our attentional goals represented in LTM can rapidly regulate which sensory stimuli do, and do not, capture out attention.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.004

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 head0.044
GPT teacher head0.345
Teacher spread0.301 · 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
Published2022
Admission routes1
Has abstractyes

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