Voxelwise Encoding Models (VEMs) are one of the most sensitive methods available for modeling and decoding functional brain activity. In the VEM framework, features are extracted from the stimulus (or task) and used in an encoding model to predict brain activity. If the encoding model is able to predict brain activity in some part of the brain, then one may conclude that some information represented in the features is also represented in the brain.

Welcome to the official Gallant Lab blog! This is a space where we’ll share longer-form articles about our research, insights into cognitive neuroscience, and updates from our lab at UC Berkeley.

What to Expect

In this blog, you’ll find:

• Deep dives into our research: Detailed explanations of our latest findings and methodologies
• Behind-the-scenes looks: How we design experiments and analyze complex fMRI data
• Perspectives on neuroscience: Thoughts on the field of cognitive, systems and computational neuroscience
• Lab life: Stories from our team members and their research journeys

Our Research Focus

The Gallant Lab focuses on understanding how the human brain represents complex information during natural tasks. We use cutting-edge fMRI techniques and computational modeling to map semantic representations, visual processing, and language comprehension across the cerebral cortex.