Evidence map›Paper›PMID 31191291›Full record

ArticleFrontiers in neurorobotics2019

Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision - The Case of Visual Crowding.

Alban Bornet, Jacques Kaiser, Alexander Kroner, Egidio Falotico, Alessandro Ambrosano, Kepa Cantero, Michael H Herzog, Gregory Francis

Abstract read
In one paragraph

Article in Frontiers in neurorobotics, 2019. 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. Dissecting (un)crowding.Journal of vision · 2021
    Article
  5. 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

8 authors.

Alban BornetLaboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Jacques KaiserFZI Research Center for Information Technology, Karlsruhe, Germany.
Alexander KronerDepartment of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.
Egidio FaloticoThe BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.
Alessandro AmbrosanoThe BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.
Kepa CanteroFortiss GmbH, Munich, Germany.
Michael H HerzogLaboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Gregory FrancisDepartment of Psychological Sciences, Purdue University, West Lafayette, IN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditionally, human vision research has focused on specific paradigms and proposed models to explain very specific properties of visual perception. However, the complexity and scope of modern psychophysical paradigms undermine the success of this approach. For example, perception of an element strongly deteriorates when neighboring elements are presented in addition (visual crowding). As it was shown recently, the magnitude of deterioration depends not only on the directly neighboring elements but on almost all elements and their specific configuration. Hence, to fully explain human visual perception, one needs to take large parts of the visual field into account and combine all the aspects of vision that become relevant at such scale. These efforts require sophisticated and collaborative modeling. The Neurorobotics Platform (NRP) of the Human Brain Project offers a unique opportunity to connect models of all sorts of visual functions, even those developed by different research groups, into a coherently functioning system. Here, we describe how we used the NRP to connect and simulate a segmentation model, a retina model, and a saliency model to explain complex results about visual perception. The combination of models highlights the versatility of the NRP and provides novel explanations for inward-outward anisotropy in visual crowding.

Indexed as

large-scale simulationmodelingneuroroboticsvisionvisual crowding

Identifiers

PMID31191291
PMCPMC6549494

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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.