Jack Gallant

Jack Gallant

@gallantlab.org

Cognitive, Systems and Computational Neuroscientist, Professor at UC Berkeley, and lab head. Check out our lab web site http://gallantlab.org For the latest news, publications, brain viewers, code and tutorials, and data.

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Dr. Catherine Chen's study of semantic relations has been accepted in Nature Human Behaviour! fMRI encoding models across 1000+ concept pairs show that relations and concepts share cortical regions, with most voxels selective for a single relation. Read the preprint. gallantlab.org

Dr. Tianjiao Zhang's landmark study of naturalistic navigation has been accepted in Nature Neuroscience! Encoding models fit 28,134 features to fMRI from a virtual taxi-driving task, revealing 11 distinct cortical regions that transform perception into action. Read the preprint. gallantlab.org

New paper from Dr. Tianjiao Zhang accepted in eNeuro: comparing semantic representations during passive movie watching to those measured during naturalistic navigation. Attention in different task contexts alters semantic representations to optimize task performance. gallantlab.org

Our postdoc Dr. Tianjiao Zhang has released "Its Complicated", a great software library that provides infrastructure to implement naturalistic, complex, interactive experiments in game engines. The toolbox supports peripherals and coding game logic for later analysis. gallantlab.org/Its-Complica...

We've posted a new fMRI study examining attention across tasks. We compare semantic representations during movie watching to those during naturalistic navigation. We show that attention in different contexts alters semantic representations to optimize performance. www.biorxiv.org/content/10.6...

Our bilingualism paper is now published in PNAS. We used fMRI to compare semantic brain representations in English-Chinese bilinguals. Semantic representations are largely shared across languages, but finer-grained differences modulate how meaning is represented. www.pnas.org/doi/10.1073/...

We've posted a new fMRI study of semantic relations (has-part, is-a, made-of, etc.), a key aspect of language. We find that relations are represented in the same brain regions as are other semantic concepts, though voxels tend to be selective for only one relation or another. doi.org/10.64898/202...

Voxelwise Encoding Models do some things differently from other GLM-related methods because the roots of VEM are in neurophysiology, not psychology. This 2006 paper describes system identification for neurophysiology. Change "neurons" to "voxels" and it all still applies. tinyurl.com/wu-etal-2006

tinyurl.com(PDF) Complete functional characterization of sensory neurons by system identificationPDF | System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of... | Find, read and cite all the research you need on...

Stephen got one thing wrong in the Transmitter article. He forgot that the tree was motivated by a hand drawn tree of my academic lineage that was posted in our lab. That got him interested in the larger lineage of all neuroscientists. And he was right, that lineage information is valuable!

A recent paper made big waves arguing that fMRI data are hopelessly confounded and uninterpretable. But I'm always suspicious of strong claims that go against a large body of well-supported science. This important post by @alexanderhuth.bsky.social suggests that the paper is fundamentally flawed.

Alexander Huth@alexanderhuth.bsky.social

This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!

‪Voxelwise Encoding Models (VEMs) are a great framework for modeling fMRI data, but it can be difficult to implement. We've made VEM accessible by providing software, tutorials and reviews that guide its use an implementation. Get it here: gallantlab.org/blog/2025-12... #neuroscience, #neuroimaging

Put this new preprint by T. Zhang on your reading list! People drove a car to navigate in VR. Voxelwise encoding models were fit using 38 feature spaces (28,134 features!). We show the cortical navigation network comprises 11 regions arranged in functional gradients. www.biorxiv.org/content/10.6...

This cool new study is, as far as I know, the largest computational modeling effort ever undertaken in fMRI. The methods developed for this landmark project push the boundaries on the breadth and depth of information that can be recovered from a single fMRI study. Great work from Dr. Tianjiao Zhang!

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