MétaCan
Menu
Back to cohort
Record W2021968838 · doi:10.1037/a0034574

The interaction between stimulus-driven and goal-driven orienting as revealed by eye movements.

2013· article· en· W2021968838 on OpenAlexaff
Daniël Schreij, Sander A. Los, Jan Theeuwes, James T. Enns, Christian N. L. Olivers

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStimulus (psychology)Eye movementSaccadic maskingPsychologyCognitive psychologySmooth pursuitCommunicationComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

It is generally agreed that attention can be captured in a stimulus-driven or in a goal-driven fashion. In studies that investigated both types of capture, the effects on mean manual response time (reaction time [RT]) are generally additive, suggesting two independent underlying processes. However, potential interactions between the two types of capture may fail to be expressed in manual RT, as it likely reflects multiple processing steps. Here we measured saccadic eye movements along with manual responses. Participants searched a target display for a red letter. To assess contingent capture, this display was preceded by an irrelevant red cue. To assess stimulus-driven capture, the target display could be accompanied by the simultaneous onset of an irrelevant new object. At the level of eye movements, the results showed strong interactions between cue validity and onset presence on the spatiotemporal trajectories of the saccades. However, at the level of manual responses, these effects cancelled out, leading to additive effects on mean RT. We conclude that both types of capture influence a shared spatial orienting mechanism and we provide a descriptive computational model of their dynamics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.395
Teacher spread0.347 · 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.

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

Citations19
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental Psychology Human Perception & PerformanceSame topicVisual perception and processing mechanismsFrench-language works237,207