Michael Lepori

@michael-lepori.bsky.social

PhD student at Brown interested in deep learning + cog sci, but more interested in playing guitar.

It is not easy to characterize the features represented by human language cortex. This work is a step toward doing so. Using small, interpretable feature sets, we explain language-network responses and show a shared feature basis across brain regions, with graded variation across individuals.

Michael Lepori@michael-lepori.bsky.social · 5mo ago

🚨New preprint!🚨 We know that LM representations can be used to predict brain responses to language. But what *features* of these representations underlie this alignment? We use SAEs to find out!

I think Fodor & Pylyshyn's 1988 paper is possibly the most mischaracterized paper in the history of cognitive science. It's often cited as arguing that neural networks cannot achieve systematicity, compositionality, and productivity. But that's not what they actually argue...

🚨New preprint! In-context learning underlies LLMs’ real-world utility, but what are its limits? Can LLMs learn completely novel representations in-context and flexibly deploy them to solve tasks? In other words, can LLMs construct an in-context world model? Let’s see! 👀

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I had a great time helping out on this project with @jennhu.bsky.social and Michael Franke! If you're interested in the intersection of interpretability and cogsci, check it out!

Jennifer Hu@jennhu.bsky.social · last yr.

Excited to share a new preprint w/ @michael-lepori.bsky.social & Michael Franke! A dominant approach in AI/cogsci uses *outputs* from AI models (eg logprobs) to predict human behavior. But how does model *processing* (across layers in a forward pass) relate to human real-time processing? 👇 (1/12)

Screenshot of Figure 1, which has two panels labeled (a) and (b). The caption states the following. Figure 1: Overview of our study. (a) Experiment 1: We explore whether forward passes show mechanistic signatures of competitor interference, first preferring a salient competing intuitive answer before preferring the correct answer. (b) Experiment 2: We systematically investigate the ability of dynamic measures derived from forward passes to predict indicators of processing load in humans.