Evidence map›Paper›PMID 40954320›Full record

ReviewNature reviews. Neuroscience2025

Multisensory coding of self-motion and its contribution to navigation.

Dun Mao, Yong Gu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. A hemispheric decoding principle for vestibular heading perception in the posterior sylvian area.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Review
  7. 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

2 authors.

Dun MaoCenter for Excellence in Brain Science and Intelligence Technology, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Neuroscience, International Center for Primate Brain Research, Chinese Academy of Sciences, Shanghai, China.ORCID http://orcid.org/0000-0001-5523-657X
Yong GuCenter for Excellence in Brain Science and Intelligence Technology, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Neuroscience, International Center for Primate Brain Research, Chinese Academy of Sciences, Shanghai, China. guyong@ion.ac.cn.ORCID http://orcid.org/0000-0003-4437-8956

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mobile organisms integrate multimodal self-motion signals - including motor commands, vestibular inputs, optic flow and proprioceptive feedback - to accurately perceive their heading and speed of traversal. These instantaneous cues are processed, via continuous temporal integration and progressive spatial transformations, to facilitate path-integration-based navigation. Recent cutting-edge neurophysiological recordings in animal models have revealed several ubiquitous cross-modal algorithms that contribute to this processing: vestibular-visual convergence to enhance self-motion perception, predictive coding integration to enable optimal dynamic state estimates, landmark-referenced error correction‌ to mitigate path-integration drift and facilitate cognitive spatial map construction, and egocentric-to-allocentric conversion‌ via integration with proprioceptive cues from the eyes, head, body or limbs. Thus, multisensory coding plays an important role in self-motion perception and self-localization during navigational behaviour.

Indexed as

Motion PerceptionProprioceptionSpace PerceptionSpatial NavigationAnimalsHumans

Identifiers

What OpenQuestion holds

Textmetadata
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.