Josh Wilson

1/X Our new method, the Inter-Animal Transform Class (IATC), is a principled way to compare neural network models to the brain. It's the first to ensure both accurate brain activity predictions and specific identification of neural mechanisms.

Preprint: arxiv.org/abs/2510.02523

Attractor dynamics of working memory explain a concurrent evolution of stimulus-specific and decision-consistent biases in visual estimation People exhibit biases when perceiving features of the world, shaped by both external stimuli and prior decisions. By tracking behavioral, neural, and mechanistic markers of stimulus- and decision-rela...

Excited to share that our paper is now out in Neuron @cp-neuron.bsky.social (dlvr.it/TM9zJ8).

Our perception isn't a perfect mirror of the world. It's often biased by our expectations and beliefs. How do these biases unfold over time, and what shapes their trajectory? A summary thread. (1/13)

Nice paper. Curious if you all have done similar analyses on the things brain data - fitting lower dimensional embeddings with simulated triplet judgements based on responses and trying to interpret? Don't think I've seen it in any of the other papers but maybe I missed it

A flexible readout mechanism of human sensory representations - Nature Communications It is known that attention can modify the brain's representations of sensory stimuli to enhance features of importance. Here, the authors show that flexible readout of cortical representations is also...

My understanding (via osmosis from lab mates/advisor) is that information as early as V1 is accessible and that readout is quite flexible - e.g. www.nature.com/articles/s41... (imaging) and openreview.net/forum?id=ec7... (behavior)

Yeah. I see a lot of "if chatGPT coded all of it, how do you know it's right?" - but for me the more important thing is "if chatGPT coded all of it, how did you learn anything???"

starting to get the "NIH grants are a crapshoot" refrain

I really like this review paper by Justin Gardner: www.nature.com/articles/s41...
I keep coming back to it whenever I’m writing about models of perception.

I especially like this quote; it took me a while to fully wrap my head around it, but I think it touches on something quite fundamental.

🧠📈

"I published my paper on biorxiv and now I'm hoping to get it advertised in Nature"

This may seem like a diss, but 'is the exposure you get from Nature worth 10k?' has a different answer than 'is it worth paying 10k to publish a PDF online?' (which was always a strawman)

Did you partition human experts by field? A cognitive neuroscientist who scores 100% on that category, but chance on the rest, would look pretty bad overall. Is it the case that "neuroscience" is simply too broad a field? Sorry if I missed this in the paper somewhere..

A starter pack with people who research visual sensation, perception, cognition, and memory.

Also, a curated feed just for vision science content.

Check out the following starter packs for suggestions on who to follow:
Cognitive neuroscience: bsky.app/starter-pack...
Neural engineering & computational neuroscience: bsky.app/starter-pack...
Affective science: bsky.app/starter-pack...
Women in neuroscience: bsky.app/starter-pack...

fMRI *has* -- and will continue to -- shed much light on "how" questions when paired with predictive computational models of neural activity and behavior