6 papers
Learning Patterns and Abstractions from Perceptual Sequences
Shuchen Wu
Cognition swiftly breaks high-dimensional sensory streams into familiar parts and uncovers their relations. Why do structures emerge, and how do they enable learning, generalizatio…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Concept-Guided Interpretability via Neural Chunking
Shuchen Wu, Stephan Alaniz, Shyamgopal Karthik +3
Neural networks are often described as black boxes, reflecting the significant challenge of understanding their internal workings and interactions. We propose a different perspecti…
Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences
Shuchen Wu, Mirko Thalmann, Peter Dayan +2
Humans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences. In contrast,…
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
Discovering Chunks in Neural Embeddings for Interpretability
Shuchen Wu, Stephan Alaniz, Eric Schulz +1
Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it…