M
Michael S. Beauchamp
Researcher at Baylor College of Medicine
Publications - 125
Citations - 10185
Michael S. Beauchamp is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Multisensory integration & Speech perception. The author has an hindex of 47, co-authored 120 publications receiving 9164 citations. Previous affiliations of Michael S. Beauchamp include University of Texas Health Science Center at San Antonio & National Institutes of Health.
Papers
More filters
Journal ArticleDOI
Integration of auditory and visual information about objects in superior temporal sulcus.
TL;DR: It is suggested that pSTS/MTG is specialized for integrating different types of information both within modalities (e.g., visual form, visual motion) and acrossmodalities (auditory and visual).
Journal ArticleDOI
Unraveling multisensory integration: patchy organization within human STS multisensory cortex.
TL;DR: These studies suggest a functional architecture in which information from different modalities is brought into close proximity via a patchy distribution of inputs, followed by integration in the intervening cortex.
Journal ArticleDOI
Parallel visual motion processing streams for manipulable objects and human movements.
TL;DR: Specificity for different types of complex motion (in combination with visual form) may be an organizing principle in lateral temporal cortex.
Journal ArticleDOI
fMRI Responses to Video and Point-Light Displays of Moving Humans and Manipulable Objects
TL;DR: The lateral temporal cortex showed strong responses to both moving videos and moving point-light displays, supporting the hypothesis that the lateral temporal prefrontal cortex is the cortical locus for processing complex visual motion.
Journal ArticleDOI
A New Method for Improving Functional-to-Structural MRI Alignment using Local Pearson Correlation
TL;DR: An improved modality-specific cost functional which uses a weighted local Pearson coefficient (LPC) to align T2- and T1-weighted images and emphasizes the importance of precise visual inspection of alignment quality and presents an automated method for generating composite images that help capture errors of misalignment.