Evidence map›Paper›PMID 42395382›Full record

ArticlebioRxiv : the preprint server for biology2026

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 readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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, Genève, Switzerland.
Thomas SainsburyEPFL Neuro, School of Life Sciences, EPFL, Genève, Switzerland.
Léo BruneauEPFL Neuro, School of Life Sciences, EPFL, Genève, Switzerland.
Yang LinDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Chenchen CaiDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Mariia PopovaEPFL Neuro, School of Life Sciences, EPFL, Genève, Switzerland.
Kayla PonderDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Lydia NtanavaraDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Rachel FroebeDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Zheng TanDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Paul FaheyDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Katrin FrankeInstitute for Ophthalmic Research, Tübingen University, Tübingen, Germany.
Luis M FrancoDepartment of Biology and Institute of Neuroscience, University of Oregon, Eugene, OR, USA.
Keaton JonesDepartment of Biology and Institute of Neuroscience, University of Oregon, Eugene, OR, USA.
Yizhou ChenNeuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Reece KellerNeuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Xaq PitkowNeuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.ORCID 0000-0001-6376-329X
Cristopher M NiellDepartment of Biology and Institute of Neuroscience, University of Oregon, Eugene, OR, USA.ORCID 0000-0001-6283-3540
Andreas S ToliasDepartment of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.ORCID 0000-0002-4305-6376
Mackenzie Weygandt MathisEPFL Neuro, School of Life Sciences, EPFL, Genève, Switzerland.ORCID 0000-0001-7368-4456

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 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 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 performing 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, highlighting a key role for information-seeking in natural vision.

Indexed as

active sensingdecision makinginfotaxisvirtual realityvisual discrimination

Identifiers

PMID42395382
PMCPMC13320795

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.