Evidence map›Paper›PMID 31735904›Full record

ArticleVision (Basel, Switzerland)2018

Investigating Human Visual Sensitivity to Binocular Motion-in-Depth for Anti- and De-Correlated Random-Dot Stimuli.

Martin Giesel, Alex R Wade, Marina Bloj, Julie M Harris

Abstract read
In one paragraph

Article in Vision (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Asymmetries between achromatic and chromatic extraction of 3D motion signals.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
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.

Martin GieselSchool of Psychology and Neuroscience, University of St Andrews, St Andrews KY16 9JP, UK.ORCID 0000-0002-3832-7442
Alex R WadeDepartment of Psychology, University of York, York YO10 5DD, UK.ORCID 0000-0003-4871-2747
Marina BlojSchool of Optometry and Vision Science, University of Bradford, Bradford BD7 1DP, UK.ORCID 0000-0001-9251-0750
Julie M HarrisSchool of Psychology and Neuroscience, University of St Andrews, St Andrews KY16 9JP, UK.ORCID 0000-0002-3497-4503

Funding

Biotechnology and Biological Sciences Research Council BB/M001210/1Biotechnology and Biological Sciences Research Council BB/M001660/1Biotechnology and Biological Sciences Research Council BB/M002543/1
6 · The paper itself

Abstract

Motion-in-depth can be detected by using two different types of binocular cues: change of disparity (CD) and inter-ocular velocity differences (IOVD). To investigate the underlying detection mechanisms, stimuli can be constructed that isolate these cues or contain both (FULL cue). Two different methods to isolate the IOVD cue can be employed: anti-correlated (aIOVD) and de-correlated (dIOVD) motion signals. While both types of stimuli have been used in studies investigating the perception of motion-in-depth, for the first time, we explore whether both stimuli isolate the same mechanism and how they differ in their relative efficacy. Here, we set out to directly compare aIOVD and dIOVD sensitivity by measuring motion coherence thresholds. In accordance with previous results by Czuba et al. (2010), we found that motion coherence thresholds were similar for aIOVD and FULL cue stimuli for most participants. Thresholds for dIOVD stimuli, however, differed consistently from thresholds for the two other cues, suggesting that aIOVD and dIOVD stimuli could be driving different visual mechanisms.

Indexed as

3D motionanti-correlationbinocular cuesCDde-correlationdisparityIOVDmotion-in-depth

Identifiers

PMID31735904
PMCPMC6835454

What OpenQuestion holds

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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.