New preprint! Statistical structure skews object memory toward predictable successors. Model simulations show how this bias can arise from the backward expansion of hippocampal representations.
w/co-first @codydong.bsky.social , @marlietandoc.bsky.social & @annaschapiro.bsky.social osf.io/yuxb6_v1
#psychscisky #neuroskyence #compneurosky
Cody Dong
Dhairyya Singh
Memory-augmented LLMs (MA-LLMs) may help solve this problem. They combine the rich, context-sensitive semantic knowledge in LLM weights with an added memory system that can retrieve unique events, similar to human episodic memory. (4/n)
We’ve made progress in understanding how memory systems support real-world event comprehension. Yet we still lack computational models that generate precise predictions about how episodic memory (EM) will be used when processing naturalistic, high-dimensional stimuli. (2/n)
My first, first author paper, comparing the properties of memory-augmented large language models and human episodic memory, out in @cp-trendscognsci.bsky.social!
authors.elsevier.com/a/1lV174sIRv...
Here’s a quick 🧵(1/n)
