arXiv:2606.06857v1 Announce Type: new Abstract: A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs) have emerged as a powerful tool to address this challenge, but studies relating biological and artificial representations are often criticized as relating one black box to another. The present work introduces Augmented Sparse Encoding Models, an encoding framework that replaces dense LM hidden states with hierarchically-organized sparse autoencoder (SAE) features, while explicitly including surpr
The proliferation of advanced AI language models intersects with ongoing efforts in cognitive neuroscience to understand human brain function, creating an opportune moment for interdisciplinary research.
This research provides a more interpretable framework for understanding how AI models relate to biological cognition, moving beyond 'black box' criticisms and potentially accelerating both AI development and neuroscience.
The ability to interpret AI models and brain responses using sparse, hierarchical features offers a new method for mapping language processing in the human brain, potentially refining our understanding of consciousness and intelligence.
- · Cognitive Neuroscience Researchers
- · AI Interpretablity Researchers
- · Developers of foundational AI models
- · Researchers relying solely on 'black-box' comparisons
Improved understanding of human language processing at a neural level due to clearer AI-brain mappings.
Development of more biologically inspired AI architectures and learning algorithms.
Enhanced ability to diagnose and treat language-related neurological disorders, or even interface with the brain for communication.
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