ArticleJournal of vision2025
The identification of materials from patterns of fluid flow.
Article in Journal of vision, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- The visual perception of relative mass from object collisions.Journal of vision · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The physical interactions among objects in the natural environment can cause dramatic changes in their shapes or patterns of motion, and those changes can provide reliable information to distinguish different types of events or materials. The present research was designed to investigate the identification of fluid materials. Observers viewed computer animations and static images of a shiny orange translucent fluid flowing from a tube into a glass jar, and they were asked to make confidence ratings about whether the depicted material looked like water/juice, oil/paint, honey/molasses, or caulk/toothpaste. The results reveal that observers can identify different types of fluid materials within broad overlapping categories based on qualitative characteristics of fluid flow that only occur within limited ranges of viscosity.
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Registered trials
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.