Evidence map›Paper›PMID 37728915›Full record

ArticleJournal of vision2023

Cues for predictive eye movements in naturalistic scenes.

Alexander Goettker, Nils Borgerding, Linus Leeske, Karl R Gegenfurtner

Abstract read
In one paragraph

Article in Journal of vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alexander GoettkerJustus Liebig Universität Giessen, Giessen, Germany.
Nils BorgerdingJustus Liebig Universität Giessen, Giessen, Germany.
Linus LeeskeJustus Liebig Universität Giessen, Giessen, Germany.
Karl R GegenfurtnerJustus Liebig Universität Giessen, Giessen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We previously compared following of the same trajectories with eye movements, but either as an isolated targets or embedded in a naturalistic scene-in this case, the movement of a puck in an ice hockey game. We observed that the oculomotor system was able to leverage the contextual cues available in the naturalistic scene to produce predictive eye movements. In this study, we wanted to assess which factors are critical for achieving this predictive advantage by manipulating four factors: the expertise of the viewers, the amount of available peripheral information, and positional and kinematic cues. The more peripheral information became available (by manipulating the area of the video that was visible), the better the predictions of all observers. However, expert ice hockey fans were consistently better at predicting than novices and used peripheral information more effectively for predictive saccades. Artificial cues about player positions did not lead to a predictive advantage, whereas impairing the causal structure of kinematic cues by playing the video in reverse led to a severe impairment. When videos were flipped vertically to introduce more difficult kinematic cues, predictive behavior was comparable to watching the original videos. Together, these results demonstrate that, when contextual information is available in naturalistic scenes, the oculomotor system is successfully integrating them and is not relying only on low-level information about the target trajectory. Critical factors for successful prediction seem to be the amount of available information, experience with the stimuli, and the availability of intact kinematic cues for player movements.

Indexed as

CuesEye MovementsHumansMovementSaccades

Identifiers

PMID37728915
PMCPMC10516764

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.