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Record W3194823883 · doi:10.1016/j.cub.2021.06.087

Ice hockey spectators use contextual cues to guide predictive eye movements

2021· article· en· W3194823883 on OpenAlexafffund
Alexander Goettker, Hemanth Pidaparthy, Doris I. Braun, James H. Elder, Karl R. Gegenfurtner

Bibliographic record

VenueCurrent Biology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftYork University
KeywordsBiologyIce hockeySensory cueEye movementCognitive psychologyNeurosciencePhysical medicine and rehabilitationPsychology

Abstract

fetched live from OpenAlex

Eye movements are an integral part of human visual perception. They allow us to have a small foveal region with exquisite acuity and at the same time a large visual field. For a long time, eye movements were regarded as machine-like behaviors in response to visual stimulation1Lisberger S.G. Visual guidance of smooth pursuit eye movements.Annu. Rev. Vis. Sci. 2015; 1: 447-468Crossref PubMed Google Scholar, but over the past few decades it has been convincingly shown that expectations, intended actions, rewards and many other cognitive factors can have profound effects on the way we move our eyes2Yarbus A.L. Eye Movements and Vision. Springer, Boston, MA1967Crossref Google Scholar, 3Tatler B.W. Hayhoe M.M. Land M.F. Ballard D.H. Eye guidance in natural vision: Reinterpreting salience.J. Vis. 2011; 11: 5Crossref PubMed Scopus (182) Google Scholar, 4Kowler E. Rubinstein J.F. Santos E.M. Wang J. Predictive smooth pursuit eye movements.Annu. Rev. Vis. Sci. 2019; 5: 223-246Crossref PubMed Scopus (18) Google Scholar. In order to be useful, our oculomotor system must minimize delay with respect to the dynamic events in the visual scene. The ability to do so has been demonstrated in situations where we are in control of these events, for example when we are making a sandwich or tea5Land M.F. Hayhoe M. In what ways do eye movements contribute to everyday activities?.Vis. Res. 2001; 41: 3559-3565Crossref PubMed Scopus (672) Google Scholar, and when we are active participants, for example when hitting a cricket ball6Land M.F. McLeod P. From eye movements to actions: How batsmen hit the ball.Nat. Neurosci. 2000; 3: 1340-1345Crossref PubMed Scopus (523) Google Scholar. But what about scenes with complex dynamics that we do not control or directly take part in, like a hockey game we are watching as a spectator? A semantic influence on gaze fixation location during viewing of tennis videos has been suggested before7Henderson J.M. Gaze control as prediction.Trends Cogn. Sci. 2017; 21: 15-23Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar. Here we use carefully annotated hockey videos to show that the brain is indeed able to exploit the semantic context of the game to anticipate the continuous motion of the puck, leading to eye movements that are fundamentally different than when following exactly the same motion without any context.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.001

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 head0.111
GPT teacher head0.399
Teacher spread0.289 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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