Evidence map›Paper›PMID 38806251›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2024

MEG Evidence That Modality-Independent Conceptual Representations Contain Semantic and Visual Features.

Julien Dirani, Liina Pylkkänen

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Enhanced Neural Decoding With Optically Pumped Magnetometer MEG Using Multivariate Pattern Analysis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  5. 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.

Julien DiraniDepartments of Psychology, New York University, New York, New York 10003 julien.dirani@nyu.edu.ORCID 0000-0002-5632-9863
Liina PylkkänenDepartments of Psychology, New York University, New York, New York 10003.ORCID 0000-0002-6332-9378

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The semantic knowledge stored in our brains can be accessed from different stimulus modalities. For example, a picture of a cat and the word "cat" both engage similar conceptual representations. While existing research has found evidence for modality-independent representations, their content remains unknown. Modality-independent representations could be semantic, or they might also contain perceptual features. We developed a novel approach combining word/picture cross-condition decoding with neural network classifiers that learned latent modality-independent representations from MEG data (25 human participants, 15 females, 10 males). We then compared these representations to models representing semantic, sensory, and orthographic features. Results show that modality-independent representations correlate both with semantic and visual representations. There was no evidence that these results were due to picture-specific visual features or orthographic features automatically activated by the stimuli presented in the experiment. These findings support the notion that modality-independent concepts contain both perceptual and semantic representations.

Indexed as

MagnetoencephalographyPhotic StimulationSemanticsAdultBrainBrain MappingConcept FormationFemaleHumansMalePattern Recognition, VisualVisual PerceptionYoung AdultconceptslexicalMEGmodalitysemanticvisual

Identifiers

PMID38806251
PMCPMC11223456

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.