Yaxin L

Why an overreliance on AI-driven modelling is bad for science Without clear protocols to catch errors, artificial intelligence’s growing role in science could do more harm than good.

Science is not collection of findings. Progress happens through theories.As we move from findings to theories things r less amenable to automation. Proliferation of scientific findings based on AI hasn't accelerated—& might even have inhibited—higher levels of progress www.nature.com/articles/d41...

09.04.2025 15:45 — 👍 122    🔁 49    💬 3    📌 3

Figure 1. A schematic depiction of a model-mechanism mapping between a human learning system (left side) and a cognitive model (right side). Candidate model mechanism mappings are pictured as mapping between representations but also can be in terms of input data, architecture, or learning objective.

Figure 2. Data efficiency in human learning. (left) Order of magnitude of LLM vs. human training data, plotted by human age. Ranges are approximated from Frank (2023a). (right) A schematic depiction of evaluation scaling curves for human learners vs. models plotted by training data quantity.

Paper abstract

AI models are fascinating, impressive, and sometimes problematic. But what can they tell us about the human mind?

In a new review paper, @noahdgoodman.bsky.social and I discuss how modern AI can be used for cognitive modeling: osf.io/preprints/ps...

06.03.2025 17:39 — 👍 63    🔁 25    💬 2    📌 0

With his permission, I'm sharing Dean Treanor's response to Ed Martin's letter:

06.03.2025 20:17 — 👍 19762    🔁 5502    💬 858    📌 1167

Great opportunity! Vlad is truly a great mentor, and the research he does is cutting-edge.

18.02.2025 04:18 — 👍 1    🔁 0    💬 0    📌 0

Does the culture you grow up in shape the way you see the world? In a new Psych Review paper, @chazfirestone.bsky.social & I tackle this centuries-old question using the Müller-Lyer illusion as a case study. Come think through one of history's mysteries with us🧵(1/13):

25.01.2025 22:05 — 👍 1093    🔁 423    💬 33    📌 79

KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models This paper investigates visual analogical reasoning in large multimodal models (LMMs) compared to human adults and children. A "visual analogy" is an abstract rule inferred from one image and applied ...

Here's another example from our lab, just accepted at ICLR, young kids can do these easily but Multi Modal Models fail.
arxiv.org/abs/2407.177...

23.01.2025 18:31 — 👍 37    🔁 8    💬 1    📌 0