The new data by Fernandez, @mbeyeler.bsky.social, Liu et al will be great here to better map the neural effect of various stimulation patterns

To expand on this: When we built our stimulation->neural predictor (www.biorxiv.org/content/10.1...), we didn't find much experimental data to constrain the model. The best we found was data from @markhisted.org and biophysical modeling by Kumaravelu et al.

Just to support Sam's argument here: there is indeed a lot of evidence across several domains such as vision and language that ML models develop representations similar to the human brain. There are of course many differences but on a certain level of abstraction there is a surprising convergence

Thank you!

More precisely we would categorize it as a brain based disorder, but now I'm curious if you would be on board with that?

You're right and I apologize for the imprecise phrasing. I wanted to connect with the usual "brain in health and disease" phrasing, for which we developed some first tools based on the learning disorder dyslexia. We are hopeful that these tools will be applicable to diseases of brain function

We're super excited about this approach: localizing model analogues of hypothesized neural causes in the brain and testing their downstream behavioral effects is applicable much more broadly in a variety of other contexts!

Digging deeper into the ablated model, we found that its behavioral patterns mirror phonological deficits of dyslexic humans, without a significant deficit in orthographic processing. This connects to experimental work suggesting that phonological and orthographic deficits have distinct origins.

It turns out that the ablation of these units has a very specific effect: it reduced reading performance to dyslexia levels *but* keeps visual reasoning performance intact. This does not happen with random units, so localization is key.

We achieve this via the localization and subsequent ablation of units that are "visual-word-form selective" i.e. are more active for the visual presentation of words over other images. After ablating the units we test the effect on behavior in benchmarks testing reading and other control tasks

We're super excited about this approach more broadly: localizing model analogues of hypothesized neural causes in the brain and testing their downstream behavioral effects is applicable in a variety of other contexts!

Digging deeper into the ablated model, we found that its behavioral patterns mirror phonological deficits of dyslexic humans, without a significant deficit in orthographic processing. This connects to experimental work suggesting that phonological and orthographic deficits have distinct origins.

It turns out that the ablation of these units has a very specific effect: it reduced reading performance to dyslexia levels *but* keeps visual reasoning performance intact. This does not happen with random units, so localization is key.

We achieve this via the localization and subsequent ablation of units that are "visual-word-form selective" i.e. are more active for the visual presentation of words over other images. After ablating the units we test the effect on behavior in benchmarks testing reading and other control tasks