Crossmodal Mvpa
Crossmodal Mvpa - Related Videos
06:43
The Crossmodal Congruency Task as a Means to Obtain an Objective Behavioral Measure in the Rubber Hand Illusion Paradigm
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Cited by 22 •
2013
We demonstrate how an objective measure can be employed in the widely employed rubber hand illusion paradigm. This measure is obtained by modifying the well-established crossmodal congruency task. This task allows the investigation of multisensory processes which are critical for modulations of body representations as in the rubber hand illusion.
13:51
Cross-Modal Multivariate Pattern Analysis
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Cited by 5 •
2011
Classical multivariate pattern analysis predicts sensory stimuli a subject perceives from neural activity in the corresponding cortices (e.g. visual stimuli from activity in visual cortex). Here, we apply pattern analysis cross-modally and show that sound- and touch-implying visual stimuli can be predicted from activity in auditory and somatosensory cortices, respectively.
11:46
Decoding Auditory Imagery with Multivoxel Pattern Analysis
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Imagine the sound of a bell ringing. What is happening in the brain when we conjure up a sound like this in the "mind's ear?" There is growing evidence that the brain uses the same mechanisms for imagination that it uses for perception.1 For example, when imagining visual images, the visual cortex becomes activated, and when imagining sounds, the auditory cortex is engaged. However, to what extent are...
07:12
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Cited by 23 •
2014
Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.
FAQs
Crossmodal Mvpa examines whether information learned from one sensory modality remains useful when activity from another modality is analyzed. Successful transfer suggests that the neural patterns contain information that is consistent across modalities, whereas information restricted to one modality reflects sensory-specific coding. This distinction helps researchers separate shared conceptual processing from representations tied to vision or audition.
Crossmodal prediction reveals whether patterns recorded during one type of sensory experience can account for patterns produced by another. When a classifier or representational model transfers across modalities, the result supports the presence of modality-independent information in neural activity. This provides a data-driven way to study how the brain represents content beyond the sensory format in which it was received.
A pattern classifier tests whether information learned from one modality can predict activity associated with another, while a representational model evaluates whether relationships among activity patterns are aligned across modalities. These approaches address related but distinct questions: one emphasizes transferable information, and the other emphasizes similarity in representational structure. Together, they can characterize shared coding across sensory systems.
The method compares information that generalizes between modalities with information that remains tied to a particular sensory system. If activity patterns associated with visual and auditory inputs support a common prediction or alignment, the shared component may reflect conceptual processing rather than only sensory features. This comparison is useful for examining whether the brain represents meaning independently of how information is perceived.
A typical workflow begins by collecting neuroimaging activity patterns while participants process information in different modalities. Researchers then train a classifier or construct a representational model using data from one modality and test it against activity from another. The resulting prediction or alignment indicates whether the analyzed information transfers across sensory conditions, providing evidence about shared neural representations.
Researchers use Crossmodal Mvpa when they want to investigate whether information is represented consistently across sensory systems. It is relevant to experiments on multisensory integration, semantic representation, perception, attention, and memory. By comparing visual and auditory activity patterns, the method can reveal whether these functions depend on modality-specific signals or also involve information shared across modalities.
In multisensory integration studies, crossmodal MVPA can test whether activity patterns associated with different sensory inputs contain a common signal. It can also examine whether information related to perception, attention, or memory remains recognizable when the sensory modality changes. These analyses help characterize how the brain combines information and whether integrated representations extend beyond individual sensory systems.