Paul Bogdan

Paul Bogdan

@pbogdan.bsky.social

I have a website: https://pbogdan.com/

153 followers108 following51 posts

🚨 New preprint 🚨 Prior work has mapped how the brain encodes concepts: If you see fire and smoke, your brain will represent the fire (hot, bright) and smoke (gray, airy). But how do you encode features of the fire-smoke relation? We analyzed fMRI with embeddings extracted from LLMs to find out 🧵

View full thread

5/🧵 Relational analysis also identified a role of the dorsal striatum. Despite not being typically seen as an area for semantic coding, striatal regions robustly represent relational content, and the strength of this representation predicts participants’ judgments about item pairs

🚨 New preprint 🚨 Prior work has mapped how the brain encodes concepts: If you see fire and smoke, your brain will represent the fire (hot, bright) and smoke (gray, airy). But how do you encode features of the fire-smoke relation? We analyzed fMRI with embeddings extracted from LLMs to find out 🧵

View full thread

3/🧵 Next, we tested relational information. LLM activity for texts like “A prison and poker table” robustly predicts human ratings on the likelihood of finding a poker table in a prison. Further analyses show how LLMs parsing such texts also capture precise propositional relational features

4/🧵 We used LLM embeddings to model neural representations via fMRI and RSA, which revealed dissociations in information processing. Occipitotemporal structures parse concepts but not relations. Conversely, frontoparietal regions (especially the PFC) almost exclusively encode relational information

🚨 New preprint 🚨 Prior work has mapped how the brain encodes concepts: If you see fire and smoke, your brain will represent the fire (hot, bright) and smoke (gray, airy). But how do you encode features of the fire-smoke relation? We analyzed fMRI with embeddings extracted from LLMs to find out 🧵

2/🧵 To produce concept embeddings, we submit a short text (“A porcupine”) to contemporary LLMs and extract the LLMs’ residual stream. Leveraging data from our normative feature study, we find that LLM embeddings better predict human-reported propositional features than older models (word2vec, BERT)

Thrilled to see a news piece by @science.org on my recent paper. By analyzing p-values across >240k papers, the study suggests that the rate of statistically questionable findings in psychology has declined since the replication crisis began www.science.org/content/arti...

science.org‘A big win’: Dubious statistical results are becoming less common in psychologyFewer papers are reporting findings on the border of statistical significance, a potential marker of dodgy research practices
View full thread

However, fragile p-values are also positively tied to university prestige. Top schools more often publish weak results, which was true both in the past and today. This seems to stem from a focus on topics where replicability progress has been slower (e.g., biological psychology)

Plotting the links between fragile p-values and word usage, we can see how various methodologies and disciplines are linked to strong (green) or weak (red) results. I made a website, where you can look up your own terms and their associations: pbogdan.com/meganal/

I investigated how often papers' significant (p < .05) results are fragile (.01 ≤ p < .05) p-values. An excess of such p-values suggests low odds of replicability. From 2004-2024, the rates of fragile p-values have gone down precipitously across every psychology discipline (!)

Nowadays, papers reporting fragile p-values are less likely published in top journals and receive fewer citations. This was not the case historically, and this shift is a powerful indicator of how the field now better value replicability.

You are up to date.