Evidence map›Paper›PMID 42463676›Full record

ArticleScientific data2026

Larg-scale multimodal fMRI dataset of visual and haptic 3D object category learning.

Sepideh Tabrik, Sama Rahnemayan, Martin Tegenthoff, Mehdi Behroozi

Abstract readDataset
In one paragraph

Article in Scientific data, 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

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

4 authors.

Sepideh Tabrik *Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789, Bochum, Germany.
Sama Rahnemayan *Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Martin TegenthoffDepartment of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789, Bochum, Germany.
Mehdi BehrooziInstitute of Cognitive Neuroscience, Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, 44780, Bochum, Germany. mehdi.behroozi@ruhr-uni-bochum.de.ORCID http://orcid.org/0000-0001-8373-6168

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) SFB 1280, SFB1280 project #316803389
6 · The paper itself

Abstract

How do humans recognize objects in their environment using either vision or touch, even when only one sense is available? Humans routinely integrate visual and haptic sensory systems for object recognition, yet the mechanisms behind cross-modal transfer of category information are not fully understood. In this study, we collected behavioral and fMRI data from 134 participants. Fifty participants (25 per group; 13 females each) performed a similarity rating task with novel 3D objects ("digital embryos") using either visual or haptic modalities. The remaining 84 participants completed a 3D object category learning experiment in unimodal (Visual-Visual: n = 19, 11 females; Haptic-Haptic: n = 22, 10 females) or cross-modal (Visual-Haptic: n = 22, 11 females; Haptic-Visual: n = 21, 10 females) conditions. Alongside task-based fMRI, we acquired resting-state fMRI data at four time points: pre-training, post-training, pre-test, and post-test. High-resolution T1-weighted images were also obtained for each MRI session. These data support investigations into visual and haptic representations of the environment, representational similarity across modalities, cross-modal transfer of category information for object recognition, and neural plasticity in brain networks.

Indexed as

LearningTouch PerceptionVisual PerceptionFemaleHumansMagnetic Resonance ImagingMale

Identifiers

PMID42463676
PMCPMC13376767

What Socratic 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 Socratic graph.