Evidence map›Paper›PMID 37184213›Full record

ArticleeLife2023

Spatiotemporal neural dynamics of object recognition under uncertainty in humans.

Yuan-Hao Wu, Ella Podvalny, Biyu J He

Open access · goldAbstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 25% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

3 authors at 1 institution in 1 country.

Yuan-Hao WuNeuroscience Institute, New York University Grossman School of Medicine, New York, United States.ORCID 0000-0002-5631-5082
Ella PodvalnyNeuroscience Institute, New York University Grossman School of Medicine, New York, United States.
Biyu J HeNeuroscience Institute, New York University Grossman School of Medicine, New York, United States.ORCID 0000-0003-1549-1351
New York University · US

Funding

Neural Mechanisms of Conscious Object Recognition in HumansR01EY032085 · NEI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Biyu Jade He · 2021 to 2026
$2.7M
NEI NIH HHS R01 EY032085
6 · The paper itself

Abstract

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images-how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

Indexed as

Pattern Recognition, VisualVisual PerceptionBrain MappingHumansMagnetic Resonance ImagingPhotic StimulationRecognition, PsychologyUncertaintydecodingfMRIhumanlarge-scale brain networksMEGneuroscienceobject recognitionrepresentational similarity analysis

Identifiers

PMID37184213
PMCPMC10231926
OpenAlexW4376612786

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.