Homomorphic spaces generated by advanced LMs yield excellent recommendation performance. Semantic similarities in language representations may imply user preference similarities. The complicated user ...

When people listen to a story, their brains do not process language all at once. Instead, meaning unfolds over time, with different regions contributing at different moments as words accumulate into ...

Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond ...

In their classic 1998 textbook on cognitive neuroscience, Michael Gazzaniga, Richard Ivry, and George Mangun made a sobering observation: there was no clear mapping between how we process language and ...

This valuable study provides a large-scale EEG investigation into how visual deep neural networks (DNNs) and large language models (LLMs) differentially explain the temporal dynamics of visuo-semantic ...

In Sarah Yuska’s sixth-grade science class at Monocacy Middle School in Frederick, Maryland, students are just finishing up learning about body systems—respiratory, circulatory, skeletal, and so on.

This useful study uses creative scalp EEG decoding methods to attempt to demonstrate that two forms of learned associations in a Stroop task are dissociable, despite sharing similar temporal dynamics.

Large language models (LLMs) show intriguing human-like behaviors despite being trained solely via language prediction. Are these models developing human-like concepts central to human understanding?

😸 Welcome to LRCD, this is a comprehensive repository specializing in Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis published in KDD 2025. Here, we propose LRCD, a new ...

Language models have demonstrated remarkable capabilities in processing diverse data types, including multilingual text, code, mathematical expressions, images, and audio. However, a fundamental ...