Evidence map›Paper›PMID 42379160›Full record

ArticleCurrent biology : CB2026

Visual uncertainty and task demands shape active sensing strategies in mice.

Célia Benquet, Thomas Sainsbury, Léo Bruneau, Yang Lin, Chenchen Cai, Mariia Popova, Kayla Ponder, Lydia Ntanavara, Rachel Froebe, Zheng Tan and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current biology : CB, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Célia BenquetEPFL Neuro, School of Life Sciences, EPFL, 1202 Genève, Switzerland.
Thomas SainsburyEPFL Neuro, School of Life Sciences, EPFL, 1202 Genève, Switzerland.
Léo BruneauEPFL Neuro, School of Life Sciences, EPFL, 1202 Genève, Switzerland.
Yang LinDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Chenchen CaiDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Institute for Ophthalmic Research, Tübingen University, 72076 Tübingen, Germany.
Mariia PopovaEPFL Neuro, School of Life Sciences, EPFL, 1202 Genève, Switzerland.
Kayla PonderDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Lydia NtanavaraDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Rachel FroebeDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Zheng TanDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Paul FaheyDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Katrin FrankeInstitute for Ophthalmic Research, Tübingen University, 72076 Tübingen, Germany.
Luis M FrancoDepartment of Biology and Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
Keaton JonesDepartment of Biology and Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
Yizhou ChenNeuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Reece KellerNeuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Xaq PitkowNeuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Department of Computer Science, Rice University, Houston, TX 77005, USA; NSF AI Institute for Artificial and Natural Intelligence (ARNI), Columbia Engineering School, New York, NY 10027, USA.
Cristopher M NiellDepartment of Biology and Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
Andreas S ToliasDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford University, Palo Alto, CA 94303, USA; Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Mackenzie Weygandt MathisEPFL Neuro, School of Life Sciences, EPFL, 1202 Genève, Switzerland. Electronic address: mackenzie.mathis@epfl.ch.

Funding

Computational foundations of active visual sensingUF1NS126566 · NINDS · STANFORD UNIVERSITY · PI MATHIS, MACKENZIE WEYGANDT, NIELL, CRISTOPHER M · 2022 to 2022
$4.8M
NINDS NIH HHS UF1 NS126566
6 · The paper itself

Abstract

In natural environments, animals actively sample visual information to guide their behavior. Sensory feedback is dynamic and often requires active movements, whether saccading across the lines of this page or walking through a park. From high-acuity vision in hawks to low-acuity vision in mice, many animals actively navigate to seek information, which can be called infotaxis. Although mice have relatively low-acuity vision, they still rely on sight for critical behaviors including navigation and prey capture. Yet how sensitive they are to visual information and whether they perform infotaxis has not been established. Here, we develop a virtual-reality object-discrimination task to investigate visual decision-making under naturalistic conditions. We show that mice perform infotaxis by actively seeking out informative views to guide their choices. Stimulus manipulations confirm that this strategy is modulated by the amount of available visual information. These results reveal that mice use principled active strategies to resolve visual uncertainty, thus highlighting a key role for information-seeking in natural vision.

Indexed as

Decision MakingVisual PerceptionAnimalsFemaleMaleMiceUncertaintyVirtual Realityactive sensingdecision-makinginfotaxisvirtual realityvisual discrimination

Identifiers

PMID42379160
PMCPMC13388018

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.