Related Experiment Video
Updated: Aug 6, 2026
14:38
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Larg-scale multimodal fMRI dataset of visual and haptic 3D object category learning
Sepideh Tabrik1,2, Sama Rahnemayan3,4, Martin Tegenthoff1
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789, Bochum, Germany.
Scientific Data
|July 16, 2026
Summary
Humans can recognize objects using vision or touch alone, integrating sensory information for object recognition. This study explores how category information transfers between visual and haptic systems, revealing insights into cross-modal perception.
Area of Science:
- Cognitive Neuroscience
- Sensory Perception
- Neuroimaging
Background:
- Humans integrate visual and haptic information for object recognition.
- Mechanisms of cross-modal category information transfer are not fully understood.
Purpose of the Study:
- Investigate cross-modal transfer of category information for object recognition.
- Examine visual and haptic representations and their similarity across modalities.
- Explore neural plasticity in brain networks supporting sensory integration.
Main Methods:
- Collected behavioral and functional magnetic resonance imaging (fMRI) data from 134 participants.
- Participants performed similarity rating and category learning tasks in unimodal and cross-modal conditions.
- Acquired task-based and resting-state fMRI data, along with high-resolution T1-weighted images.
Main Results:
- Data support investigations into visual and haptic representations.
- Analysis focuses on representational similarity across sensory modalities.
- Findings contribute to understanding cross-modal transfer in object recognition.
Conclusions:
- The study provides data for analyzing cross-modal perception and neural plasticity.
- Results aid in understanding how the brain processes and integrates sensory information.
- This research enhances knowledge of object recognition mechanisms using single or multiple senses.
