Disentangling Superpositions: Interpretable Brain Encoding Model with Sparse Concept Atoms
Alicia Zeng, Jack Gallant
Encoding models that use word embeddings or artificial neural network (ANN) representations to predict brain activity achieve high predictive performance but offer limited interpretability. A key challenge is that dense feature representations encode many distinct concepts along overlapping directions due to superposition, where multiple semantic features are compressed into correlated directions in the embedding space. This structure creates ambiguity in interpreting model weights, as different combinations of semantic directions can produce the same predicted brain response. To address this challenge, we introduce the Sparse Concept Encoding Model, which transforms dense embeddings into a higher-dimensional, sparse space composed of learned, interpretable concept atoms. Each stimulus is re-encoded as a sparse, non-negative combination of these atoms, enabling voxel responses to be modeled in an axis-aligned semantic basis where each dimension corresponds to a distinct concept. Applied to naturalistic fMRI data, the Sparse Concept Encoding Model matches the prediction accuracy of conventional dense models while offering substantially greater interpretability. It enables novel neuroscientific analyses that are infeasible with dense features, such as identifying overlapping cortical representations of time, space, and number. This work offers a more interpretable framework for linking language representations in ANNs to conceptual representations in the brain.