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Record W4404040207 · doi:10.7554/elife.97883.3.sa0

eLife Assessment: Coupling of saccade plans to endogenous attention during urgent choices

2024· peer-review· en· W4404040207 on OpenAlexaff
Miriam Spering

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSaccadeCoupling (piping)Computer scienceArtificial intelligenceEngineeringEye movement

Abstract

fetched live from OpenAlex

You are attending a talk at a conference, eyes straight ahead and fixed on the speaker… yet you may in fact also be covertly monitoring your phone, hoping for a long-awaited message to flash on the screen. This ability to focus on something without directly looking at it is called spatial attention. It plays an essential role in everyday tasks, such as spotting keys on a cluttered desk or noticing when a traffic light changes. Overlapping brain circuits control spatial attention and eye movements, creating tight links between the two processes. For example, shifting your gaze towards a specific location automatically leads you to pay at least partial attention to what unfolds at this spot. Whether the reverse is true, however, is less clear. In other words: when we are paying attention to something without looking at it, is our brain set to move our eyes towards this location? To explore this question, Goldstein et al. designed a visual task that allowed them to track human participants’ attention and eye movements moment by moment, and to unpick various factors affecting these processes. The volunteers fixed their gaze on the center of a screen, knowing that they also needed to pay attention to a certain location at the periphery where a cue was set to appear. The color of the cue determined whether the participants would then need to shift their gaze either towards or away from it – for example, they were instructed to look directly at a green cue but away from a magenta one. These analyses showed that participants needed about 30 milliseconds less time to program an eye movement toward the cue – that is, to shift their gaze towards the location that they were already covertly monitoring. Such difference in processing time suggests that eye movements are biased towards the location on which attention is directed, but that this preference can still be overridden quickly. By refining our understanding of the mechanisms underpinning attention, the findings by Goldstein et al. may help us better understand conditions like attention deficit hyperactivity disorder, where the brain struggles to engage and disengage with stimuli effectively.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.210
GPT teacher head0.432
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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