Experience-based neural tradeoffs in perceptual sampling vs. predictive processing in the visual domain

Overview

Sensory inference and predictive processing, in human neural activity, play a critical role in higher-order cognitive processes. However, the neurobiological bases of predictive processing are not well understood. This study used electroencephalography (EE G) to track participants’ response to sign language and reversed sign language videos, to test the hypothesis that the frequency-following response (FFR) of the brain to the visual signal quantifies both behavioral comprehension and frequency of perceptual sampling for continuous visual input. In this study, I used the FFR metric to address two questions. First, I used machine learning to assess the relevance of specific frequencies and regions of interest to brain state classification accuracy. The results highlighted a significance of predictive processing time windows for sign language comprehension and biological motion processing, and the role of long-term experience (learning) in minimizing prediction error. Second, I used neural coherence to optical flow and PCA to assess age-related changes in neural processing. The findings indicate a general slowing of perceptual processing in older adults while increasing the duration of prediction time -window in language comprehension, likely due to accumulated la nguage experience. Together these two findings improve on current models of understanding how the human brain learns, explain how people process visual information, quantify predictive processing based on neural data, and show the impact of language experience on brain function. Making Every Component Count: Using the Shapley Value to Improve Win Ratio Analysis Valerie Fu Carmel High School, Carmel, IN Clinical trials often utilize composite endpoints to provide a comprehensive evaluation of a treatment’s effects by combining multiple related outcomes into a single measure. While traditional methods, such as time -to-first-event analysis, are widely used, they face limitations, including equal weighting of components, disproportionate influence of non -fatal events, and neglect of recurrent outcomes. The win ratio has emerged as an alternative, prioritizing outcomes by clinical importance and offering great er flexibility. However, it provides limited insight into the contributions of individual components to the overall treatment effect. To address this gap, we develop the SEWRA (Shapley-Enhanced Win Ratio Analysis) algorithm, which integrates the Shapley value, a concept from cooperative game theory, into win ratio analysis. SEWRA fairly allocates the total treatment effect across the components of a composite endpoint, reflecting their relative contributions within the context of the overall win ratio. This approach enhances the interpretability of the win ratio and provides a deeper understanding of the treatment’s impact on individual o utcomes. Through simulations and case studies, we demonstrate how SEWRA offers a nuanced framework for analyzing composite endpoints, complementing existing methodologies and addressing their limitations. Our work aims to advance the analytical tools available for clinical trial data, promoting more informed decision - making and improved patient outcomes.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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