[3Win5] Poster session 3

[3Win5-03] Bridging Text Embeddings and Brain Activity: Insights from Lipschitz-Enhanced Ridge Regression

〇YING LUO¹, Ichiro Kobayashi¹ (1.Ochanomizu University)

1Win4-106で発表

Keywords:Boostrap Ridge Regression, Lipschitz Algorithm, Brain Encoding

Brain Encoding commonly relies on regression analysis to predict neural responses from external stimuli. However, preserving the complex relationships within the data remains a critical challenge. To address this, a method is proposed that applies the Lipschitz constraint to enhance ridge regression, significantly improving the accuracy of cortical response predictions from text embeddings.
Comparison across seven state-of-the-art deep learning models reveals the superior performance of the proposed approach. The Lipschitz constraint effectively preserves the structural integrity of the data and improves prediction correlation. Additionally, information-theoretic analysis is employed to further investigate cortical response patterns.
Results demonstrate that Lipschitz-enhanced ridge regression outperforms conventional methods in both prediction correlation and data structural preservation.
Specifically, the Pearson correlation coefficients improved substantially, with increases ranging from 111% to over 175% across multiple models.
Moreover, previously underperforming metrics now exhibit more intuitive and pronounced enhancements.