140 citations · 359 across the 24 of their papers we have counts for
7 papers · 1 filter
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics
Camila Kolling, Omer Moussa, Emin Çelik +1
Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varies in natural settings. To en…
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations
Camila Kolling, Mariya Toneva
Large language models (LLMs) predict neural responses during naturalistic language comprehension, but the linguistic information underlying this brain alignment remains difficult t…
RABBiT: Rapidly adaptive BOLD foundation model via brain-tuning for accurate zero-shot and few-shot prediction of speech-elicited responses in the brain
Omer Moussa, Mariya Toneva
Language understanding in the brain is context-dependent, varying across experimental stimuli and individuals, which makes it difficult to build computational models that generaliz…
Neural Signatures of Programming Expertise: Classifying Programmer Skill Levels Using EEG Data
Maurice Rekrut, Mahima Mahabaleshwar Acharya, Taisiia Ulianova +5
Accurately assessing a programmer's skill level is critical for hiring, team composition, and performance evaluation in the software industry. Conventional methods, such as coding…
Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models
Mathis Pink, Vy Ai Vo, Qinyuan Wu +7
Human episodic memory supports the retrieval of experiences that unfold over extended timescales, yet the computational mechanisms underlying this ability remain debated due to the…
Tracking Equivalent Mechanistic Interpretations Across Neural Networks
Alan Sun, Mariya Toneva
Mechanistic interpretability (MI) is an emerging framework for interpreting neural networks. Given a task and model, MI aims to discover a succinct algorithmic process, an interpre…
When Language Models Lose Their Mind: The Consequences of Brain Misalignment
Gabriele Merlin, Mariya Toneva
While brain-aligned large language models (LLMs) have garnered attention for their potential as cognitive models and for potential for enhanced safety and trustworthiness in AI, th…